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1、KPMG I bankingA blueprint for creating value through AI-driven transformationKPMG.Make the Difference.Contents03Foreword22Second phase:Embed04At a glance27Third phase:Evolve08Research findings36Preparing for an AI future05Introduction31Key considerations15First phase:Enable11Building the intelligent

2、 bank39KPMG:Guiding your AI transformation with experience and trust 2025 Copyright owned by one or more of the KPMG International entities.KPMG International entities provide no services to clients.All rights reserved.Intelligent banking:A blueprint for creating value through AI-driven transformati

3、on|2This report is the result of extensive research into the value being created by artificial intelligence(AI)within the banking sector.It is designed to provide actionable insights for leaders at every stage of their AI journey,from those deploying their first pilots to banks seeking to scale ente

4、rprise-wide AI initiatives.While some banks are highly advanced in their use of AI,many others face significant barriers that impede progress.Moving beyond tests and pilots is no longer an option it is an imperative.This report serves as a guide to help banks navigate this critical transition and un

5、lock AIs transformative potential.Banks are beginning to grapple with the reality that seizing the significant opportunities AI presents will require far more than just an investment in technology.It demands a holistic rethinking of strategy,culture,operation practices and an ethical framework for d

6、eployment.Yet,many find themselves stalled by inertia.Long-term value is difficult to define,and many organizations struggle with setting clear objectives,identifying key performance indicators(KPIs),and proving return on investment(ROI).These challenges are compounded by the costs of technology upg

7、rades,implementation risks and hesitations among senior executives who are eager to transform but cautious about leading the charge.To prepare for an intelligent enterprise,banks should embrace AI as a driver of sustainable growth.By integrating AI across functions from marketing and customer servic

8、e to fraud prevention and risk management banks can create innovative,customer-centric solutions that not only enhance profitability but also deepen customer loyalty.This report provides the tools and insights needed to break through barriers,scale AI adoption and position banks to succeed in an inc

9、reasingly competitive and intelligent future.ForewordAI is not just a technology investment for banks its a catalyst for redefining strategy,operations and culture.To unlock its potential,banks must overcome inertia,embrace transformation and integrate AI as a core enabler of customer-centric,sustai

10、nable growth.Francisco Ura Global Head of Banking and Capital Markets KPMG InternationalIntelligent banking:A blueprint for creating value through AI-driven transformation|3 2025 Copyright owned by one or more of the KPMG International entities.KPMG International entities provide no services to clie

11、nts.All rights reserved.Research findingsBuilding the intelligent bankFirst phaseSecond phaseThird phaseKey considerationsPreparing for an AI futureGuiding your AI transformationIntroductionAt a glanceForewordAt a glanceare seeking to reduce coststo enhance the customer experienceAI goals are clear1

12、3%have experienced a high revenue contribution from AI26%have experienced revenue growthOnlyAnd onlyThe initial benefitsBut the pressure is on to prove ROIface significant pressure from shareholders to show immediate ROI on AI investmentAI use is widespread in banking say AI is fundamentally reshapi

13、ng their businessAI spending will likely increase significantlyplan to increase the percentage of global budget spent on AIsay up to 20 percentOf whichand62%say 20 percent plus 38%Banking executives have high expectations80%68%have achieved cost savings66%believe that banks that embrace AI will deve

14、lop a competitive edge over those who do not expect a moderate to very high ROI from AI investments62%70%70%At a glanceAt a glance42%51%Intelligent banking:A blueprint for creating value through AI-driven transformation|4 2025 Copyright owned by one or more of the KPMG International entities.KPMG In

15、ternational entities provide no services to clients.All rights reserved.Research findingsBuilding the intelligent bankFirst phaseSecond phaseThird phaseKey considerationsPreparing for an AI futureGuiding your AI transformationForewordIntroductionAt a glanceIntroductionBanks are increasingly experime

16、nting with generative AI in isolated use cases,such as chatbots,content creation and personalized marketing.However,many are struggling to extract meaningful value from these efforts.Our study finds that while many financial institutions see AI as critical to their future,and beginning to realize ef

17、ficiencies,only a small fraction report achieving revenue growth from their AI investments.Banks face unique challengesMany struggle to establish a risk environment robust enough to support broader AI deployment,particularly in highly regulated areas like credit decisioning or compliance monitoring.

18、Concerns about data governance,operational integrity and regulatory scrutiny create barriers to progress.At the same time,the rapid pace of technological advancements combined with the competitive dynamics of proprietary versus open-source AI platforms adds complexity,leaving banks uncertain about h

19、ow to scale their efforts.Banks embrace the new world of AI agentsAI agents are set to transform banking by enabling hyper-personalized,efficient,and seamless customer experiences while driving operational efficiency.These intelligent agents can act as 24/7 virtual advisors,providing tailored financ

20、ial guidance,automating routine transactions,and proactively managing customer needs based on real-time data and predictive insights.In operations,AI agents can streamline back-office processes,such as fraud detection,compliance monitoring,and risk assessment,by analyzing vast amounts of data with u

21、nmatched speed and precision.A blueprint for valueTo overcome these challenges,seize the opportunity and prepare for the next generation of AI technologies,banks should adopt a deliberate,structured approach to AI adoption.In this report we introduce the three phases of AI value a framework designed

22、 to help banks prioritize efforts,align investments,and realize the full potential of AI.say employees within their organization are quickly getting to grips with using the AI tools/technology theyve invested in85%Intelligent banking:A blueprint for creating value through AI-driven transformation|5

23、2025 Copyright owned by one or more of the KPMG International entities.KPMG International entities provide no services to clients.All rights reserved.Research findingsBuilding the intelligent bankFirst phaseSecond phaseThird phaseKey considerationsPreparing for an AI futureGuiding your AI transforma

24、tionForewordAt a glanceIntroductionFigure 1:Data concerns and lack of skills emerge as top challengesPercentage who say their organization has faced the following challenges when integrating AISecurity and data privacy concernsLack of Al skills or expertise amongst workforceDifficulty in measuring r

25、eturn on investment(ROl)Ethical risksData silosLack of communication and alignment between departmentsBudget restrictions or lack of investmentLack of leadership support and understandingEmployee resistance to change and reluctance to use Al toolsLegal or regulatory constraintsPoor data qualityLack

26、of leadership communication and alignment Time and resource constraintsInconsistent data formats38%33%30%28%27%26%24%23%23%23%22%22%22%21%What challenges has your organization faced when integrating AI?(Maximum 5)n=183Source:Intelligent banking:A blueprint for creating value through AI-driven transf

27、ormation,KPMG International,2025 2025 Copyright owned by one or more of the KPMG International entities.KPMG International entities provide no services to clients.All rights reserved.Intelligent banking:A blueprint for creating value through AI-driven transformation|6ForewordIntroductionAt a glanceR

28、esearch findingsBuilding the intelligent bankFirst phaseSecond phaseThird phaseKey considerationsPreparing for an AI futureGuiding your AI transformationIntroductionFour key considerations that will likely accelerate AI adoption and the creation of long-term value:Design an AI strategy that aligns w

29、ith core competencies and unlocks valueBanks should establish a bold vision for AI that aligns with their core strengths.This vision should guide a transformation roadmap that redefines how AI drives growth and innovation while holding teams accountable for results.Aligning AI deployments with strat

30、egic goals such as improving fraud detection,streamlining underwriting and enhancing customer personalization can help maximize ROI.Build trust into the transformation roadmapAI in banking introduces unique risks that can undermine trust,meaning proactive risk management critical from the outset.Ban

31、ks should address data privacy and security challenges,helping ensure compliance with financial regulations while protecting sensitive customer information.Combating algorithmic bias and adopting explainable AI systems that regulators,customers,and internal stakeholders can trust is critical.Create

32、sustainable technology and data infrastructure for AI adoptionData is a critical strategic asset and the foundation for all AI initiatives.Banks should build a robust data governance framework,focusing on quality,integration and security,while also creating a foundation for long-term scalability.Thi

33、s includes investing in enterprise-grade AI infrastructure that can support high volumes of transactions,complex risk models and real-time decision-making.Build a culture that uses AI to uplift human potentialA multifaceted talent strategy that balances retention with upskilling is a key priority.Ac

34、ademic institutions,fintech startups and innovation hubs can inject fresh perspectives and enhance workforce capabilities.Immersive AI training programs help to drive innovation in customer experiences and operational models,diversify hiring pipelines and enable transformative outcomes.2025 Copyrigh

35、t owned by one or more of the KPMG International entities.KPMG International entities provide no services to clients.All rights reserved.Intelligent banking:A blueprint for creating value through AI-driven transformation|7ForewordIntroductionAt a glanceResearch findingsBuilding the intelligent bankF

36、irst phaseSecond phaseThird phaseKey considerationsPreparing for an AI futureGuiding your AI transformationIntroductionResearch findingsCurrent stateBanks are actively exploring and refining strategies to deploy AI.Amidst foundational and talent readiness challenges,the banking sector is cautiously

37、yet innovatively adopting AI,employing diverse implementation strategies and integrating complementary technologies to transform key business functions.AI is no longer just a futuristic concept in banking its already driving innovation in critical areas such as fraud detection,personalization and ri

38、sk management.Leading global institutions are at the forefront,using AI to transform key processes and deliver enhanced customer experiences.These banks have identified hundreds of AI use cases,from streamlining operations to offering hyper-personalized products and services.However,for other banks

39、the research highlights a number of inhibiting factors:The foundational infrastructure is still in developmentBanks face a challenge in building the critical infrastructure necessary to support scalable AI solutions:Only 25 percent have enterprise-wide cloud or hybrid-cloud platforms strategically s

40、upporting data-driven services,leaving many banks struggling to lay the groundwork for effective AI adoption.Leaders and employees are just coming to grips with AIs potentialSixty-one percent of banks provide ethics and guardrails training for AI,helping employees navigate the responsible applicatio

41、n of the technology.However,in-depth AI training remains limited,with only 30 percent offering advanced content that fosters a comprehensive understanding of AI capabilities.Our organization is still in an experimental stage with respect to AI.Weve not yet established any clear objectives or KPIs ar

42、ound how its used.Chief Executive Officer Japan 2025 Copyright owned by one or more of the KPMG International entities.KPMG International entities provide no services to clients.All rights reserved.Intelligent banking:A blueprint for creating value through AI-driven transformation|8ForewordIntroduct

43、ionAt a glanceResearch findingsBuilding the intelligent bankFirst phaseSecond phaseThird phaseKey considerationsPreparing for an AI futureGuiding your AI transformationResearch findingsNo dominant AI implementation modelBanks are experimenting with a mix of approaches to AI implementation.Sixty-six

44、percent of banks are leveraging cloud-based AI platforms,while 46 percent use open-source tools and 83 percent rely on on-premises solutions.Additionally,86 percent are developing custom AI solutions in-house,although approaches to implementation vary depending on the level of digital maturity.Leade

45、rs are concerned about controlControl over AI remains a key concern for banking leaders,with 58 percent expressing nervousness about the influence AI technology providers may have on their business operations.Furthermore,71 percent agree it is prudent to wait for greater clarity in the evolving AI t

46、echnology landscape before committing to significant investments.AI is being coupled with automation technologiesBanks are increasingly integrating AI with complementary technologies to maximize impact.For example,82 percent are pairing AI with robotic process automation(RPA)to streamline workflows,

47、while 84 percent are exploring autonomous agentic AI solutions.AI is transforming business functions in pocketsAI is making its greatest impact in information technology(IT)and marketing functions,with 61 percent of banks reporting high or transformative effects on IT operations and 55 percent seein

48、g significant advancements in marketing.agree it is prudent to wait for greater clarity in the evolving AI technology landscape before committing to significant investments.71%2025 Copyright owned by one or more of the KPMG International entities.KPMG International entities provide no services to cl

49、ients.All rights reserved.Intelligent banking:A blueprint for creating value through AI-driven transformation|9Building the intelligent bankFirst phaseSecond phaseThird phaseKey considerationsPreparing for an AI futureGuiding your AI transformationBuilding the intelligent bankResearch findingsForewo

50、rdIntroductionAt a glanceBarriers to progressMaking substantive progress is complicated by:Significant concerns over AI risks and ethicsAI adoption in banking is accompanied by widespread concern about risks and ethical implications.Seventy-one percent of leaders agree that establishing robust frame

51、works for regulatory compliance is essential to ensure responsible AI implementation.Balancing AI adoption with sustainability objectivesHowever,balancing sustainability goals with AI initiatives poses a challenge,as 70 percent struggle to reconcile AIs energy usage with their environmental objectiv

52、es and 66 percent view meeting sustainability goals as a higher strategic priority than implementing AI.Data is a significant barrierThe quality of data remains a major obstacle for banks seeking to scale AI adoption,with 72 percent expressing concerns about data quality.Without consistent,reliable

53、and accessible data,banks face challenges in building accurate and effective AI models,stalling progress toward meaningful AI-driven transformation.The biggest challenge right now is regulatory requirements and specific regulators for authorities from different locations from Spain,Germany,UK.They a

54、ll have their different requirements and expectations.Chief Compliance Officer GermanyBankers are taking a wait-and-see approachThe rapid evolution of AI technologies is creating uncertainty and hesitation among banking leaders.Seventy-one percent believe it is best to wait for the AI tech landscape

55、 to stabilize before making significant investments,while 57 percent feel overwhelmed by the sheer volume of information and hype surrounding AI.AI expertise is limitedA lack of specialized AI expertise is also holding banks back,with only 19 percent guided by highly specialized AI teams driving str

56、ategy across the organization.In addition,just 18 percent use AI as a core driver for product and service development across multiple areas.AccuracyAccuracy is a significant inhibitor to scaling AI in banking,as the highly regulated financial sector demands strict compliance with risk and regulatory

57、 requirements,leaving little room for error.Without mechanisms to ensure AI outputs are consistently accurate,repeatable and explainable,banks face the challenge of balancing innovation with the need for human oversight,which can slow down adoption and limit scalability.Moving forwardIts clear that

58、bankers face a complex web of challenges as they navigate AI adoption.Despite significant efforts to implement AI,these fragmented strategies and limited readiness illustrate the need for a more structured approach.In this report we introduce the three phases of AI value creation:An organizational f

59、ramework designed to help banks progress from isolated foundational capabilities to enterprise-wide innovation.By building trust,aligning strategies,enabling technology and empowering their workforce,banks can unlock AIs transformative potential while mitigating risks.This framework not only offers

60、an approach for successful adoption but also helps ensure that AI becomes a sustainable,strategic enabler for long-term growth and value.2025 Copyright owned by one or more of the KPMG International entities.KPMG International entities provide no services to clients.All rights reserved.Intelligent b

61、anking:A blueprint for creating value through AI-driven transformation|10ForewordIntroductionAt a glanceResearch findingsBuilding the intelligent bankFirst phaseSecond phaseThird phaseKey considerationsPreparing for an AI futureGuiding your AI transformationResearch findingsBuilding the intelligent

62、bankA well-run corporation is like a Swiss watch with lots of gears.If you wanted to make it digital,you cannot just take out one gear and put a transistor in.You have to have a holistic plan for how all the pieces fit together.Erik Brynjolfsson Professor and Senior Fellow at the Stanford Institute

63、for Human-Centered AI(HAI),Director of the Stanford Digital Economy LabEnterpriseThis layer orchestrates transformational change of the whole enterprise,starting with how AI can adjust strategy,business models and key objectives for the enterprise.It defines enterprise-wide operating model shifts,wo

64、rkforce evolution and risks and controls.This layer prioritizes AI transformation initiatives into a roadmap and runs a transformation office to help manage funding,track benefits and adjust priorities dynamically to help maximize the value delivered.FunctionsThis layer drives AI-enabled transformat

65、ion across business functions,prioritizing customer-facing value streams and end-to-end enabling processes and workflows,which enhance the flow of value.AI applications,agents and robotics are embedded in the workflows.Functional operating model changes are delivered to realize potential benefits.Fo

66、undationsThis layer establishes the AI-first technology stack,including infrastructure,cloud and choices on chips.High quality enterprise data needs to be curated and diverse model are likely to be deployed to handle domain specific AI and support the adoption of AI agents.An increased focus on cybe

67、rsecurity for AI is needed as well as a plan for other emerging technology,such as quantum.Successfully implementing AI in an organization involves a strategic approach to building capability across foundational,functional and enterprise layers.Establishing a transformation management office is also

68、 crucial for aligning AI strategy,value orchestration and project delivery across all layers.The body coordinates initiatives,establishes standards and best practices,and facilitates cross-functional collaboration to drive accountability and enterprise-wide value.2025 Copyright owned by one or more

69、of the KPMG International entities.KPMG International entities provide no services to clients.All rights reserved.Intelligent banking:A blueprint for creating value through AI-driven transformation|11ForewordIntroductionAt a glanceResearch findingsFirst phaseSecond phaseThird phaseKey considerations

70、Preparing for an AI futureGuiding your AI transformationBuilding the intelligent bankBlueprint for intelligent bankingThis blueprint outlines some of the key,high-level capabilities for an AI-powered,customer-centric bank.The intelligent bank leverages advanced technologies,personalized experiences,

71、data-driven insights and automated operations to enhance efficiency,innovation and resilience.Focused on embedding intelligence across value streams,capability centers and processes,it ensures seamless customer interactions,robust risk management,intelligent product manufacturing and future-ready ad

72、aptability to thrive in the intelligent economy.FunctionsValueEnterpriseSeamless Customer EngagementPredictive OperationsIntelligent Product ManufacturingInsight-Driven Strategy&Value CreationContinuous Business&Operating Model EvolutionValue Stream&Experience CentricityComposable Enterp

73、rise ArchitectureEnterprise Resilience,Sustainability&TrustWorkforce Shaping,Change&LearningFoundationsHybrid Cloud InfrastructureIntelligent Data ManagementResponsible AI Model DevelopmentApplications of Embedded IntelligenceCybersecurity&SafetyDigital Twins&Banking Ecosystem Integr

74、ationOperational Value StreamsEnabling Capabilities&ProcessesCapability CenterCapability CenterCapability CenterCapability Center 2025 Copyright owned by one or more of the KPMG International entities.KPMG International entities provide no services to clients.All rights reserved.Intelligent bank

75、ing:A blueprint for creating value through AI-driven transformation|12ForewordIntroductionAt a glanceResearch findingsFirst phaseSecond phaseThird phaseKey considerationsPreparing for an AI futureGuiding your AI transformationBuilding the intelligent bankEnableThe Enable phase focuses on enabling pe

76、ople and building AI foundations.Organizations appoint a responsible executive,create an AI strategy,identify high-value use cases,boost AI literacy,align with regulations and establish ethical guardrails.AI pilots are launched across functions,while cloud platforms and pre-trained models are levera

77、ged with minimal customization.EmbedThe Embed phase integrates AI into workflows,products,services,value streams,robotics and wearables,delivering greater value.A senior leader drives enterprise-wide workforce redesign,re-skilling and change,embedding AI into operating models with a focus on ethics,

78、trust and security.AI agents and diverse models are deployed,supported by cloud and legacy tech modernization,while enterprise-wide data enhances operations.EvolveThe Evolve phase evolves business models and ecosystems,using AI and frontier technologies like quantum computing and blockchain to solve

79、 large sector-wide challenges.AI can orchestrate seamless value across enterprises and partners.Emphasizing ethics and trust with real-time security,this phase uplifts human potential with broad and deep workforce training,fostering a creative,innovative and value-driven future.A company may have a

80、portfolio of initiatives aimed at any level(of the operating model)within each phase.The ratio of effort and investment across the phases will vary as the organization matures.Initially,most resources will focus on phase one,with a small effort to explore enterprise-wide transformation.Over time,as

81、foundational efficiencies are realized more effort is invested in phase two,while,with an eye on the future,long-term investments in phase three start to lay the groundwork for transformative innovation.This dynamic balancing act ensures banks can achieve immediate results while setting themselves u

82、p in the right way for future success.The journey to become an intelligent bankEffective AI enabled transformation goes beyond technology implementation.By examining leading practice,we have identified that banks can increase capability and value across three phases of AI transformation.This provide

83、s a structured yet flexible framework for navigating the complexities of AI adoption.It balances the need for short-term efficiency gains with the imperative to prepare for future growth and innovation.It helps banks prioritize their efforts,allocate resources effectively,build capability and align

84、their AI initiatives with both short-term goals and long-term strategic objectives.EnableValueMaturityEmbedEnterpriseFunctionsFoundationsEvolveIntelligent banking:A blueprint for creating value through AI-driven transformation|13 2025 Copyright owned by one or more of the KPMG International entities

85、.KPMG International entities provide no services to clients.All rights reserved.Research findingsFirst phaseSecond phaseThird phaseKey considerationsPreparing for an AI futureGuiding your AI transformationForewordIntroductionAt a glanceBuilding the intelligent bankPhases of the AI journeyFocusing on

86、 maturity across the three phases Enable,Embed and Evolve is critical for sustained value creation.It requires increasing the maturity of the capabilities that are vital to the foundations,functions and enterprise layers simultaneously.At the enterprise layer,increased AI maturity involves orchestra

87、ting AI across functions to enable enterprise-wide innovation and strategic alignment.Without a balanced focus on all three layers,organizations risk missing opportunities for transformation.At the functions layer,AI should be embedded into key value streams,optimizing specific processes and creatin

88、g improved outcomes,such as more compelling products and services and more engaging,end-to-end employee and customer experiences.At the foundations layer organizations should build up the new AI-first technology stack,through a process of technology modernization.Infrastructure,data,models and appli

89、cations can all become optimized for delivery of AI.Define highest value use cases Model value opportunities Deploy in operating model Initiate early AI guardrails Invest in AI literacy Jumpstart an initial program Align strategy and OKRs with AI Define value and investments Redesign operating model

90、 Strengthen trust in AI Reshape the workforce Orchestrate enterprise change Define an ecosystem strategy Model value of the ecosystem Redesign business model Always-on AI trust platforms Extend with partner workforce Orchestrate ecosystem changeEnable Enable peopleEmbed Embed AI in workEvolve Evolve

91、 the enterprise Implement functional use cases Test and learn and refine Augment people with AI skills Treat AI as co-pilot/assistant Focus on learning rapidly Build and deploy in sprints Embed AI in value streams Embed AI in process workflows Embed AI agents as they mature Use AI to transform produ

92、cts&experiences Focus on end-to-end value flow Undertake agile change AI powers ecosystems AI fuels inter-organization workflows Deploy agents across ecosystems Evolve new experience possibilities Focus on end-to-end value outcomes Continuous,agile change Select AI strategic alliances Implement

93、AI applications Configure and tailor Introduce simple models first Access AI through the cloud Build an AI development factory Select and train domain models Curate enterprise-wide data Invest in AI infrastructure Invest in increased cybersecurity Deploy AI across ecosystem Compete using domain mode

94、ls Compete using ecosystem data Cloud with AI optimized chips Consider AI with quantumEnterpriseFunctionsFoundations 2025 Copyright owned by one or more of the KPMG International entities.KPMG International entities provide no services to clients.All rights reserved.Intelligent banking:A blueprint f

95、or creating value through AI-driven transformation|14ForewordIntroductionAt a glanceResearch findingsFirst phaseSecond phaseThird phaseKey considerationsPreparing for an AI futureGuiding your AI transformationBuilding the intelligent bankThe first phase:Enable Enable people with AIThe Enable phase i

96、s about enabling people and establishing the foundations for AI adoption.At the enterprise level,this includes appointing a responsible executive,developing an AI strategy,identifying high-value use cases,increasing AI literacy,aligning with regulations and introducing ethical guardrails.At the func

97、tion level,businesses pilot AI solutions across various areas,building skills,fostering innovation and learning from these initial implementations.At the foundation level,organizations leverage cloud platforms and pre-trained AI models from strategic providers with limited customization.This phase f

98、ocuses on creating awareness,experimentation and alignment to ensure the organization is prepared for broader AI integration.Figure 2:Leadership goals for AI adoption focus heavily on operational gains rather than strategic valuePercentage who say their organization wants to achieve the followingthr

99、ough using AIIncrease operational efficiency(e.g.automating repetitive tasks and processes)Enhance customer experienceIncrease revenueMitigate business risks(e.g.fraud detection,risk management,compliance)Reduce costsGain significant competitive advantage in the marketImprove decision-makingImprove

100、data management(e.g.quality,protection)Drive innovation and new product developmentImprove stakeholder management(e.g.supply chain,clients,etc.)Support sustainability and environmental goalsOptimize financial planning and forecastingWhich of the following goals does your organization want to achieve

101、 through using AI?(Maximum 5)n=18345%42%41%40%37%37%37%34%31%29%27%26%Source:Intelligent banking:A blueprint for creating value through AI-driven transformation,KPMG International,2025 2025 Copyright owned by one or more of the KPMG International entities.KPMG International entities provide no servi

102、ces to clients.All rights reserved.Intelligent banking:A blueprint for creating value through AI-driven transformation|15ForewordIntroductionAt a glanceResearch findingsBuilding the intelligent bankSecond phaseThird phaseKey considerationsPreparing for an AI futureGuiding your AI transformationFirst

103、 phaseFigure 3a:Gen AI opportunity by function:BankingSource:Quantifying the GenAI opportunity,KPMG in the US,February 2025To guide clients AI strategy and investments,KPMG in the US recently concluded an 18-month research project Quantifying the GenAI opportunity.The research evaluated the realisti

104、c value at stake from fully deploying and adopting Gen AI.After looking in depth at 7,000 72 million people and pressure-testing results with 500 clients,the results conservatively equate to 418 percent EBITDA improvement in labor productivity alone.The following chart reveals the potential value at

105、 stake within the banking panies globally were assessed.Over 17 millioncompanies employingRiskOps&Supply chainHRFinanceMarketingITSalesServices&data analyticsOther front officeCyberValues in US$millions32.419.53.010.962.25.24.15.25.19.3Back officeMiddle officeFront office 2025 Copyright owne

106、d by one or more of the KPMG International entities.KPMG International entities provide no services to clients.All rights reserved.Intelligent banking:A blueprint for creating value through AI-driven transformation|16Second phaseThird phaseKey considerationsPreparing for an AI futureGuiding your AI

107、transformationFirst phaseForewordResearch findingsBuilding the intelligent bankIntroductionAt a glanceFigure 3b:Gen AI opportunity,task complexity breakdown:BankingLow complexityMedium complexityHigh complexityComplexity RiskOps&Supply chainHRFinanceMarketingITSalesServices&data analyticsOth

108、er front officeCyberBased on tasks that are relatively simple and can be effectively augmented using readily available Gen AI tools such as Copilot,ChatGPT and other out-of-the-box technologies.Based on tasks that have potential for Gen AI augmentation but may necessitate the development of more int

109、egrated and customized solutions.Based on tasks that have potential for Gen AI augmentation but will likely require the creation of integrated and sophisticated solutions,as well as comprehensive governance and change management to enable adoption.Back officeMiddle officeFront office58%69%57%54%62%5

110、8%63%70%69%64%30%5%28%4%24%7%32%4%27%14%27%11%41%5%33%9%26%5%34%7%Source:Quantifying the GenAI opportunity,KPMG in the US,February 2025Top 10 areas of opportunity:Banking01Customer relationship management02Operations execution03Performance optimization04Data analysis05Product performance analytics06

111、Operations and supply chain resource allocation07Customer sentiment analysis08Content generation09Data compression10Quality assurance and testingSource:Quantifying the GenAI opportunity,KPMG in the US,February 2025 2025 Copyright owned by one or more of the KPMG International entities.KPMG Internati

112、onal entities provide no services to clients.All rights reserved.Intelligent banking:A blueprint for creating value through AI-driven transformation|17ForewordIntroductionAt a glanceResearch findingsBuilding the intelligent bankFirst phaseSecond phaseThird phaseKey considerationsPreparing for an AI

113、futureGuiding your AI transformationFirst phaseConsequently,most banks focus on reducing costs and increasing efficiency.AI automates repetitive,manual processes,which can help streamline operations and minimize human error.For example:Fraud detection and prevention:Several banks are using AI to ana

114、lyze transactional data in real time,identifying patterns indicative of fraud.This automation not only reduces the workload for fraud analysts but also enhances accuracy and speed,minimizing financial losses.For instance,in 2023,a major credit card company thwarted 80 million fraudulent transactions

115、 worth$40 billion globally,attributing this success to substantial investments in AI technology.1Document processing:A major US bank uses AI to review and analyze thousands of legal documents in seconds.This task previously required hundreds of hours of manual labor,significantly reducing costs and

116、human error.Loan underwriting:AI-driven automation is resulting in faster and more accurate loan underwriting decisions.At one global bank,AI-based underwriting has reduced loan processing cycle times from 30 to 16 days,effectively doubling underwriting efficiency.By analyzing vast amounts of custom

117、er data,including credit history,income patterns and risk indicators,AI models identify and assess creditworthiness with speed and precision,enabling faster decision-making while minimizing errors.21 Reuters,“Visa prevented$40 bln worth of fraudulent transactions in 2023 official”,23 July 20242 Cofo

118、rge,“AI and Automation:Enhancing efficiency in Mortgage Underwriting amid market challenges”,Accessed November 2024AI is changing the customer experience in bankingAI-powered chatbots and virtual assistants are revolutionizing customer service by providing instant responses to common queries,transac

119、tion searches and account management tasks.First generation chatbots are being upgraded to conversational,contextually aware chatbots that emulate human interactions.These tools reduce wait times and allow customers to access support 24/7,offering convenience and reliability.Similarly,AI-driven pers

120、onalization engines analyze transaction history,spending patterns and demographics to deliver tailored product recommendations,such as suggesting relevant credit card offers or savings plans.As mentioned,AI also improves customer experience by enhancing transaction security and reducing fraud.HSBC,f

121、or example,uses AI to detect fraud across millions of accounts,providing customers with a sense of security and trust.These innovations make everyday banking more seamless,secure and customer-centric,laying the groundwork for deeper engagement and loyalty in later stages.Banks are leveraging AI to r

122、evolutionize know your customer(KYC)processes,improving efficiency,accuracy and compliance.AI-powered tools automate identity verification,analyze vast datasets to detect anomalies,When we started leveraging AI,especially for some of our solutions,usually for the customer enrolment process,we acquir

123、ed many customers through the chatbot system,which resulted in more revenue.Chief Information Security Officer USand flag potential risks in real time,significantly reducing the time and cost associated with traditional KYC checks.Machine learning algorithms enhance fraud detection by identifying pa

124、tterns of suspicious activity that may go unnoticed by manual processes.Additionally,natural language processing(NLP)enables banks to extract and verify critical information from unstructured documents,such as passports and utility bills,with precision.By integrating AI into KYC,banks can strengthen

125、 regulatory compliance and also enhance the customer experience through faster,frictionless onboarding.2025 Copyright owned by one or more of the KPMG International entities.KPMG International entities provide no services to clients.All rights reserved.Intelligent banking:A blueprint for creating va

126、lue through AI-driven transformation|18ForewordIntroductionAt a glanceResearch findingsBuilding the intelligent bankFirst phaseSecond phaseThird phaseKey considerationsPreparing for an AI futureGuiding your AI transformationFirst phaseFigure 4:AI maturity deepens focus on operational gains,with stra

127、tegic goals lagging behindAI and the employee experienceFor employees,AI in the first phase automates repetitive processes such as data entry,document verification and compliance checks,reducing errors and freeing up employees to focus on customers.This not only increases efficiency but also reduces

128、 the mental fatigue associated with repetitive,manual work.AI tools also act as cognitive assistants,supporting employees with real-time insights and help with decision-making.Relationship managers and call center agents are using AI-driven dashboards that provide personalized customer information,s

129、uch as financial goals or recent inquiries,allowing for more meaningful and informed interactions.Additionally,predictive analytics helps employees anticipate customer needs,enabling proactive outreach and tailored solutions.By minimizing administrative burdens and empowering employees,AI can help b

130、anks deliver better customer experiences.AI will change the way we hire people,from the number of staff that are required to do certain functions,the types of staff and the number of people in certain roles.I dont think therell be less staff,but I just think theyll be doing different things.Division

131、al Director AustraliaPercentage who say their organization wants to achieve the following in the first phase through using AI(top 5)Which of the following goals does your organization want to achieve through using AI?(Maximum 5)Early AI maturity(n=34)Mitigate business risksReduce costsGain significa

132、nt competitiveadvantage in the marketIncrease revenueEnhance customer experience47%47%44%41%38%Source:Intelligent banking:A blueprint for creating value through AI-driven transformation,KPMG International,2025 2025 Copyright owned by one or more of the KPMG International entities.KPMG International

133、entities provide no services to clients.All rights reserved.Intelligent banking:A blueprint for creating value through AI-driven transformation|19ForewordIntroductionAt a glanceResearch findingsBuilding the intelligent bankFirst phaseSecond phaseThird phaseKey considerationsPreparing for an AI futur

134、eGuiding your AI transformationFirst phaseA major German bankCase studyThe Chief Compliance Officer(CCO)at a major German bank shared insights into its journey towards AI adoption.The bank applies AI in key areas like anti-money laundering,fraud detection and compliance monitoring,aiming to streamli

135、ne processes such as transaction monitoring and KYC.Current AI usage Experimentation without a cohesive AI strategy The banks current AI usage is fragmented.Although individual teams are independently experimenting with AI tools such as Copilot in Power BI and Gen AI,the bank does not have a cohesiv

136、e organizational AI strategy.Despite this decentralized implementation,AI has proven instrumental in enhancing efficiency,particularly by programmers who are using it to streamline coding,or for data processing by the data analytics teams.This has saved the teams significant time on repetitive tasks

137、.Initially hesitant to adopt AI,the bank has since transitioned into a cautious integration phase,acknowledging the technologys transformative potential while remaining mindful of associated risks.Challenges Navigating regulatory,technical and human hurdles in AI adoption The bank faces several chal

138、lenges in its AI adoption,including meeting stringent regulatory requirements for explaining how AI decisions are made,which are critical for compliance.In addition,the CCO notes that legacy systems and data silos create technical barriers that hinder seamless integration,while a lack of staff exper

139、tise hinders the effective evaluation and utilization of AI outputs.Outlook Leaders expect to be more agile and open-minded as they embrace AIs potentialThe banks journey mirrors industry trends and the struggle of balancing AIs potential with regulatory and workplace challenges.Although the respond

140、ent mentions that a unified AI strategy is expected by mid-2025,he highlights the need for leaders to become more agile and experimental to drive AI innovation and guide AI usage responsibly.2025 Copyright owned by one or more of the KPMG International entities.KPMG International entities provide no

141、 services to clients.All rights reserved.Intelligent banking:A blueprint for creating value through AI-driven transformation|20Research findingsBuilding the intelligent bankFirst phaseSecond phaseThird phaseKey considerationsPreparing for an AI futureGuiding your AI transformationForewordIntroductio

142、nAt a glanceFirst phaseMaking a step change in regulatory compliance with Gen AICase studyClient challengeKPMGs client,a leading global banking group serving over 21 million customers offers a wide range of banking,insurance and asset management services to individuals,businesses and institutional c

143、lients.The risk team,however,has struggled to find an efficient way to navigate and comply with 17 separate regulations that involve over 2,000 regulatory requirements across over 1,600 pages.The team faced difficulties in comparing the most recent version of the regulations with previous versions o

144、f regulations and drawing synthetic opinions efficiently.The manual process required a significant amount of time and effort has also led to concerns related to potential compliance risks.Our approachProfessionals from a KPMG member firm analyzed the available AI solutions on the market and built a

145、customized Gen AI chatbot.This technology solution operates on a“chat-with-document”logic,which means that changes to policy documents can be visualized,reducing the time it takes for employees to review and compare documents and extract specific information.It integrates relevant banking regulation

146、s and produced responses through pre-defined prompts and precise questioning.A Proof of Concept(POC)was developed initially to demonstrate the models capability to learn from a closed set of regulatory documents that were overseen by human experts to mitigate risks.Value deliveredThe Gen AI chatbot

147、solution demonstrated the potential to shift from a primarily manual review process to an AI-assisted approach that reduces time,effort and risk.Thirty-six regulatory compliance evaluation cases within the POC were tested and validated the models high confidence level in generating the expected resu

148、lts.It represents the first step in an evolutionary path for the technology,including extending it to all relevant regulations,comparative analyses and integration into the banks cost optimization system.2025 Copyright owned by one or more of the KPMG International entities.KPMG International entiti

149、es provide no services to clients.All rights reserved.Intelligent banking:A blueprint for creating value through AI-driven transformation|21Research findingsBuilding the intelligent bankFirst phaseSecond phaseThird phaseKey considerationsPreparing for an AI futureGuiding your AI transformationForewo

150、rdIntroductionAt a glanceFirst phaseThe second phase:Embed Embedding AI in the flow of workThe Embed phase integrates AI into end-to-end workflows,products,services and value streams,transforming how work is performed across the enterprise and delivering greater value.Here,AI enables large teams to

151、handle complex tasks and enhances efficiency.A senior leader,supported by a capable transformation office,oversees enterprise-wide change,setting strategic goals and embedding AI into operating models,robotics and wearable devices.This phase emphasizes ethics,inclusion,safety,security and trust.AI a

152、gents,along with diverse models(large and complex,small and low cost,open,closed and domain-specific),are embedded into workflows,supported by data from various sources.Infrastructure combines cloud resources with on-premises GPUs,with a strong focus on security.Figure 5:The banking sector is yet to

153、 fully leverage AIs potential for new services and enhancements Using AI in existing product and service developmentUsing AI in new product and service developmentAI-driven innovationStrategic integrationStrategic integrationPartial integrationPartial integrationInitial experimentationInitial experi

154、mentationTo what extent(if at all)is your organization using AI in existing products or services?To what extent(if at all)is your organization using AI to develop new products or services?n=18318%34%27%18%AI-driven innovation17%37%24%17%Source:Intelligent banking:A blueprint for creating value throu

155、gh AI-driven transformation,KPMG International,2025 2025 Copyright owned by one or more of the KPMG International entities.KPMG International entities provide no services to clients.All rights reserved.Intelligent banking:A blueprint for creating value through AI-driven transformation|22ForewordIntr

156、oductionAt a glanceResearch findingsBuilding the intelligent bankFirst phaseSecond phaseThird phaseKey considerationsPreparing for an AI futureGuiding your AI transformationSecond phaseThe focus is on breaking down silos,redesigning the ways banks unlock more complex value opportunities in the secon

157、d phase.AI becomes embedded across the banks core functions,enabling dynamic decision-making,real-time insights,and predictive personalization.Shared data platforms foster collaboration and a culture of agility and innovation.Customer satisfaction scores,cross-sell effectiveness,and speed-to-market

158、join efficiency and cost reduction as key metrics of success.Banks also need to prepare for agentic AI,which is rapidly evolving in capability.Initially,macros(simple,rule-based agents)operate on data tasks like credit scoring and fraud detection.As banks advance,software(platform-based)agents will

159、be configured handle more complex,context-aware tasks,acting as semi-autonomous assistants.Banks should build shared data platforms for use by deterministic AI or true agents built on large action models that can independently assess risks,design products and optimize entire value streams like loan

160、processing.This can drive a paradigm shift in how a banks workforce is shaped,where employees go from traditional roles to becoming managers of AI agents,treating these systems as complex cognitive co-workers.In this model,employees are no longer merely task executors but orchestrators of AI-driven

161、workflows,overseeing and optimizing the performance of advanced AI agents capable of handling complex,context-aware operations.We use AI to help us accelerate development,speed up time to market,production cycles and project life cycles I think it will help us to drive revenue.Chief Executive Office

162、r JapanLoan origination:This value stream encompasses the entire process of a customer applying for a loan,inquiry,risk assessment,approval,disbursement,servicing and eventually,closure.Onboarding and account opening:This journey begins when a customer first engages with the bank,opening an account,

163、going through compliance checks and receiving initial financial services.Fraud prevention:This stream spans the entire banking system,integrating real-time fraud detection,risk mitigation and customer protection activities into one seamless process.Payment processing:Covering a wide range of activit

164、ies,this stream involves domestic and cross-border transactions,payment approvals,reconciliation and real-time tracking of funds.Customer retention and growth:This focuses on the continuous engagement with a customer over their financial lifecycle,including personalized advice,loyalty programs and c

165、ustomer satisfaction management.Key value streams in banking 2025 Copyright owned by one or more of the KPMG International entities.KPMG International entities provide no services to clients.All rights reserved.Intelligent banking:A blueprint for creating value through AI-driven transformation|23For

166、ewordIntroductionAt a glanceResearch findingsBuilding the intelligent bankFirst phaseSecond phaseThird phaseKey considerationsPreparing for an AI futureGuiding your AI transformationSecond phaseBarriers to realizing valueBanks should address critical foundational activities early on,as failing to do

167、 so can stall progress.Key barriers include an incomplete vision for the future operating model,outdated foundational and technology infrastructure and inadequate governance frameworks to mitigate risks and uphold ethical standards.Equally vital is securing the buy-in of leaders and employees by dem

168、onstrating AIs transformative potential.Specific areas for focus include:Lack of trust As AI becomes integral to processes,banks face increasing pressure to maintain transparency and trust,both internally and externally.Scaling AI without robust frameworks for accountability and explainability can l

169、ead to mistrust among employees,customers and regulators.Managing strategy implementationScaling and embedding AI across the organization requires a profound transformation of structures and leadership.Most banks are historically organized around products such as credit cards,mortgages,or savings ac

170、counts,with each function operating in silos.Moving to value streams disrupts these legacy structures,requiring processes that cut across departments and focus on end-to-end customer journeys.Change management becomes a critical task,as banks must not only roll out new technologies but also foster a

171、cceptance of new workflows and cultural norms.Poorly managed transitions risk low adoption of AI tools,employee dissatisfaction and stalled transformation efforts.Figure 6:AI maturity deepens focus on operational gains,with strategic goals lagging behindPercentage who say their organization wants to

172、 achieve the following,in the second phase through using AI(top 5)Which of the following goals does your organization want to achieve through using AI?(Maximum 5)Growth AI maturity(n=139)Increase operational efficiencyEnhance customer experienceIncrease revenueMitigate business risksImprove decision

173、-making48%42%41%40%38%Source:Intelligent banking:A blueprint for creating value through AI-driven transformation,KPMG International,2025 2025 Copyright owned by one or more of the KPMG International entities.KPMG International entities provide no services to clients.All rights reserved.Intelligent b

174、anking:A blueprint for creating value through AI-driven transformation|24ForewordIntroductionAt a glanceResearch findingsBuilding the intelligent bankFirst phaseSecond phaseThird phaseKey considerationsPreparing for an AI futureGuiding your AI transformationSecond phaseWorkforce concernsMany banks l

175、ack the in-house expertise to meet AIs demands.This talent gap slows the pace of transformation,as banks struggle to upskill employees or hire specialized professionals but upskilling and talent acquisition are only part of the battle:This shift often encounters resistance from teams and leaders ali

176、gned to specific products,slowing the adoption of value stream approaches.For instance,aligning risk,operations and customer service into a single value stream like“home ownership”can create tensions as functions adjust to new ways of working.The transformation also has significant implications for

177、employees and organizational structures.Many roles will evolve as automation reduces the need for routine tasks,requiring employees to shift to higher-value activities,such as oversight and strategic decision-making.This creates uncertainty among staff,who may fear losing relevance:A 2024 KPMG globa

178、l customer experience excellence study found that younger employees in particular are worried that entry level jobs will no longer exist because of AI.3 Early wins are needed to generate momentum.Leadership also plays a pivotal role in this transition.Scaling AI demands leaders who can break down si

179、los,I think the biggest challenge is peoples adoption and having a common understanding of the tech.I think thats the biggest one,readiness.Then,the change management piece is always a journey.Head of AI Canada3 KPMG International,“Beyond the noise:Orchestrating AI-driven customer excellence”,Octobe

180、r 2024align cross-functional teams,and foster a culture of experimentation and collaboration.However,traditional leadership styles in banking,often hierarchical and risk-averse,are not always equipped for these demands.Leaders must champion value streams and empower teams to innovate,even when this

181、disrupts established practices.Without such transformational leadership,efforts to scale AI may falter.The technology evolutionSignificant investment is required to roll out new technologies that can support AI at scale.Banks must implement advanced machine learning platforms,real-time analytics,and

182、 scalable infrastructure like cloud computing.These investments may conflict with existing IT roadmaps and budgets,creating delays and prioritization challenges.Legacy issues also play a big role.Banks are increasingly turning to AI enabled software re-engineering tools(such as Codeium)to tackle the

183、 challenges of legacy code and technical debt.These tools analyze,refactor,and optimize outdated codebases,making them more efficient,secure,and maintainable.They automate labor-intensive tasks like identifying redundant code,optimizing performance bottlenecks,and modernizing code to align with curr

184、ent programming standards.2025 Copyright owned by one or more of the KPMG International entities.KPMG International entities provide no services to clients.All rights reserved.Intelligent banking:A blueprint for creating value through AI-driven transformation|25ForewordIntroductionAt a glanceResearc

185、h findingsBuilding the intelligent bankFirst phaseSecond phaseThird phaseKey considerationsPreparing for an AI futureGuiding your AI transformationSecond phaseAn American financial services companySpecializing in cash handling and manufacturing smart safes and point-of-sale equipment,Loomis,a US fin

186、ancial services company,employs 23,000 people across 20 countries and generates US$3 billion annually.Chief Information Officer(CIO)Kendall Knight discussed how the organization uses AI to improve operations and create new business opportunities.Current AI usage:Using AI for operational excellence a

187、nd customer engagementThe organization employs Gen AI and machine learning to solve operational and customer engagement challenges.Gen AI enhances response capabilities,personalizes customer interactions and provides 24/7 service.Machine learning applications include a software-as-a-service(SaaS)off

188、ering that forecasts cash requirement for banks and credit unions,generating US$20 million annually.Internally,AI analyzes video and data patterns to detect internal theft,replacing manual processes and significantly improving efficiency.Challenges:Overcoming expertise,compliance and innovation chal

189、lenges in AI adoptionIdentifying meaningful use cases,ensuring compliance with strict banking regulations,data governance and privacy and overcoming legacy system constraints are challenges.Loomis initially lacked in-house expertise,so relied on external consultants and freelance data scientists to

190、implement pilot programs.Business leaders are still developing their understanding of AIs capabilities and limitations.Organizations AI outlook:Scaling AI across departments to drive competitive advantageLooking ahead,Knight envisions broader AI adoption across departments,enabling efficiencies in a

191、reas like finance,human resources and operations.The company expects significant changes in leadership roles,emphasizing the need for cross-functional AI knowledge.Learn how KPMG firms help clients gain value through AIKPMG in the UKs AI policy platform helped a leading global bank untangle the comp

192、lex world of managing risk.KPMG in the US helped one of the worlds largest financial institution reduce its loan processing time from days to hours.Case study 2025 Copyright owned by one or more of the KPMG International entities.KPMG International entities provide no services to clients.All rights

193、reserved.Intelligent banking:A blueprint for creating value through AI-driven transformation|26Research findingsBuilding the intelligent bankFirst phaseSecond phaseThird phaseKey considerationsPreparing for an AI futureGuiding your AI transformationForewordIntroductionAt a glanceSecond phaseThe thir

194、d phase:Evolve Evolving your banks ecosystemThe Evolve phase transforms enterprises to adapt to market disruptions,forming new business models and ecosystems to solve larger,industry-wide problems.Companies establish and orchestrate ecosystems with customers,suppliers and governments,orchestrated by

195、 AI to deliver seamless value.The third phase gives the biggest payoff.As AI enables costs to come down,some markets will grow,some decline,and new ones emerge.Invest in areas of price elasticity things we can do more of with AI as costs decline.Your competitors may focus more on what is disappearin

196、g and risk being replaced.Erik Brynjolfsson Professor and Senior Fellow at the Stanford Institute for Human-Centered AI(HAI),Director of the Stanford Digital Economy LabAI integrates with frontier technologies like quantum computing,blockchain,and advanced visualization,driving breakthroughs innovat

197、ion in products and services and involving close collaboration with customers,key alliances and partners.Ethics,safety,and trust are paramount,with real-time monitoring and security updates ensuring platform integrity.This phase emphasizes uplifting human potential,improving experiences,and providin

198、g robust training and support to help the workforce transition into a creative,imaginative future of value creation and collaboration.In the third phase,organizations use predictive insights to continuously optimize for better,more sustainable outcomes.AI agents,no longer inhibited by silos and orga

199、nized along value streams,can enable embedded intelligence in core processes,improving customer experiences and product value.2025 Copyright owned by one or more of the KPMG International entities.KPMG International entities provide no services to clients.All rights reserved.Intelligent banking:A bl

200、ueprint for creating value through AI-driven transformation|27ForewordIntroductionAt a glanceResearch findingsBuilding the intelligent bankFirst phaseSecond phaseThird phaseKey considerationsPreparing for an AI futureGuiding your AI transformationThird phaseAI-driven autonomous bankingAI takes full

201、control of managing customer finances.AI-driven systems automatically manage account balances,optimize investment portfolios,and even adjust loan terms in real-time based on market conditions and customer financial behavior.For example,a retail bank in the UK is experimenting with fully autonomous b

202、anking services that automatically transfer money between accounts based on real-time cash flow analysis.AI-powered digital marketplacesLeading banks are increasingly transforming into platform businesses,using AI to create digital ecosystems where customers can access a range of services from third

203、-party providers.AI helps banks match customers with the most relevant products and services,whether its a mortgage from a partner bank or an insurance product from a fintech company.Ping An in China has already developed a comprehensive financial ecosystem,using AI to integrate insurance,banking an

204、d health services into a seamless customer experience.4 Ping An reported over 202 billion Chinese Yuan Renminbi(RMB)in AI driven product sales as a result of this approach.5Blockchain and AI synergyIn the third phase,banks are exploring the intersection of AI and blockchain to enhance security,trans

205、parency and operational efficiency.By combining AIs predictive power with the decentralized nature of blockchain,banks can offer more secure and efficient services,such as smart contracts that automatically execute agreements based on predefined conditions.J.P.Morgans Quorum blockchain platform inte

206、grates AI to create secure,transparent transactions in its trading and financial services.6AI-driven predictive financeIn this phase,banks use AI to move from reactive to predictive finance,where AI not only helps customers manage their money but anticipates their needs and financial goals.Banks can

207、 offer predictive insights,such as when a customer might need a loan,helping them plan their financial future with precision.Third phase use casesMarket leadership and innovation4 FutureCIO,“Ping An Bank forges ahead with smart banking 3.0”,3 July 20235 Ping An Annual Results 20236 CoinDCX,“How JP M

208、organ is Transforming Banking with Blockchain&JPM Coin”,15 September 2023 2025 Copyright owned by one or more of the KPMG International entities.KPMG International entities provide no services to clients.All rights reserved.Intelligent banking:A blueprint for creating value through AI-driven tra

209、nsformation|28Research findingsBuilding the intelligent bankFirst phaseSecond phaseThird phaseKey considerationsPreparing for an AI futureGuiding your AI transformationForewordIntroductionAt a glanceThird phaseThe new services that financial institutions can provide with AI are going to create new c

210、ustomer facing products and new decision-making tools.Head of AI CanadaLeading banks are already exhibiting third phase characteristics.Creating and orchestrating new ecosystems.Banks can enhance their core offerings by integrating with partners in wealth management,health solutions and sustainabili

211、ty.Fintechs,tech giants and non-financial partners can help banks create a holistic ecosystem tailored to customer needs.For example,customers may access real-time financial insights alongside tailored investment recommendations,lifestyle perks,or business analytics all from a unified,AI-driven plat

212、form.Ping An and BBVA have developed open banking platforms that allow third-party developers to create applications that integrate with its services.Banking will likely also become deeply predictive and proactive.Instead of customers seeking financial services,the bank anticipates their needs and d

213、elivers services seamlessly at the right time,through the right channel.For example,AI-powered assistants may analyze spending patterns to suggest cost-saving measures or automatically optimize loan terms based on market conditions.Financial services integrate invisibly into customers daily lives,em

214、bedded within other activities like shopping,traveling,or running a business.AI can enable this by providing instant,contextual financial decisions in real time,without requiring active customer input.2025 Copyright owned by one or more of the KPMG International entities.KPMG International entities

215、provide no services to clients.All rights reserved.Intelligent banking:A blueprint for creating value through AI-driven transformation|29ForewordIntroductionAt a glanceResearch findingsBuilding the intelligent bankFirst phaseSecond phaseThird phaseKey considerationsPreparing for an AI futureGuiding

216、your AI transformationThird phaseEuropean bank boosts customer insight analysis for product development with AICase studyClient challengeKPMGs client is a European bank specializing in serving small and medium enterprises.It has several brands,subsidiaries and associated banks.The client sought to g

217、ain a better understanding of customers perceptions and experiences to improve their products and services.Their primary challenge was the complexity of extracting actionable insights from the large volume of qualitative data collected through customer satisfaction surveys.The objective was to gain

218、detailed insight into customer sentiment toward the banks offerings;pinpointing satisfaction levels,areas of discontent,and the general sentiment toward the bank.Our approachThe KPMG team leveraged its OpenAI platform to conduct a detailed sentiment analysis of the customer satisfaction survey respo

219、nses to extract actionable insights.With the help of Python,MS Excel and OpenAI,the KPMG team created and analyzed a large dataset of responses to generate an overview of customer sentiment.A close examination of the emotional content of the feedback allowed the range of customer emotions from satis

220、fied to disappointed to be charted in detail.The bank recognized this as a strategic method for developing a deeper understanding of customer perception to allow targeted improvements that enhanced customer satisfaction and loyalty.Value delivered The analysis provided a refined understanding of ove

221、rall customer sentiment,allowing the bank to identify areas of strength and opportunities for improvement.The AI implementation helped reduce response time for customer feedback from months to weeks through automated AI-driven analysis.It also provided a deeper level of analysis that had not previou

222、sly been available.The use of Gen AI reduced the survey data analysis time by 75 percent,and reduced manual errors by 5 percent.At a strategic level,the approach contributed to improved product and service offerings.As these improvements were based on customer feedback,they led to higher customer sa

223、tisfaction levels and loyalty.2025 Copyright owned by one or more of the KPMG International entities.KPMG International entities provide no services to clients.All rights reserved.Intelligent banking:A blueprint for creating value through AI-driven transformation|30Research findingsBuilding the inte

224、lligent bankFirst phaseSecond phaseThird phaseKey considerationsPreparing for an AI futureGuiding your AI transformationForewordIntroductionAt a glanceThird phaseKey considerationsFour strategic actionsThe research reveals that those organizations that are realizing the most value from their AI inve

225、stments have focused on four strategic actions:Leaders should craft a vision that aligns core competencies product innovation,customer success,data management and ecosystem partnerships with AI capabilities,while ensuring accountability for execution and outcomes.Leadership accountability is critica

226、l to help ensure that the vision translates into measurable impact.Leaders help actively engage with ecosystem players,fostering collaboration to strengthen AI strategies.By uniting teams across engineering,product and data science,and focusing on market leadership and customer experience innovation

227、,leaders can drive alignment and ensure measurable impact from AI initiatives.Design an AI strategy that aligns with core competencies and unlocks value1Source:Title,KPMG International,2025 Define a unified AI vision and strategyLeaders should articulate a clear,organization-wide vision for AI that

228、aligns with the banks core competencies such as product innovation,customer success and ecosystem partnerships.This vision should be specific,actionable,and tied to measurable outcomes,helping them to ensure that all teams understand how AI initiatives contribute to broader business goals.Establish

229、cross-functional collaborationBefore attempting to breaking down silos between engineering,product and data science teams,banks should baseline their infrastructure and operating model maturity to assess readiness for larger operating model transformation.Banks should create cross-functional teams f

230、ocused on understanding where the bank can productize services,using AI to create new business model opportunities and fuel growth.Implement measurable objectives and key results(OKRs)Banks should adopt robust performance measurement frameworks,such as OKRs,to track the effectiveness of the implemen

231、tation of the strategy across the organization.These metrics should be tied to strategic business outcomes,such as revenue growth,customer satisfaction,or market share.Regularly evaluating progress against these metrics helps ensure that AI initiatives remain aligned with the organizations strategic

232、 priorities and provides a basis for iterative improvement.Key actions 2025 Copyright owned by one or more of the KPMG International entities.KPMG International entities provide no services to clients.All rights reserved.Intelligent banking:A blueprint for creating value through AI-driven transforma

233、tion|31ForewordIntroductionAt a glanceResearch findingsBuilding the intelligent bankFirst phaseSecond phaseThird phaseKey considerationsPreparing for an AI futureGuiding your AI transformationKey considerationsFigure 7:Only half of organizations are strategically aligned with a clear vision on how t

234、hey will implement AILevel of strategic alignmentClarity of visionFully embedded into business strategyStrategic alignmentPartial alignmentLimited alignmentNo alignmentTo what extent(if at all)have leaders within your organization established strategic alignment on the adoption and implementation of

235、 AI?/To what extent(if at all)does your organizations leadership have a clear vision of the way AI can be used to its benefit to help the organization transform within the next 5 years?n=18315%34%29%15%5%Transformational AI visionClear visionDeveloping visionLimited visionNo vision21%31%22%21%4%I th

236、ink everyone in functional leadership roles will have to become more knowledgeable on what AI means,what its capabilities and limitations are,and how we can use it.Chief Information Officer USSource:Intelligent banking:A blueprint for creating value through AI-driven transformation,KPMG Internationa

237、l,2025 2025 Copyright owned by one or more of the KPMG International entities.KPMG International entities provide no services to clients.All rights reserved.Intelligent banking:A blueprint for creating value through AI-driven transformation|32ForewordIntroductionAt a glanceResearch findingsBuilding

238、the intelligent bankFirst phaseSecond phaseThird phaseKey considerationsPreparing for an AI futureGuiding your AI transformationKey considerationsKey actionsI think theyre going to have to understand far better what kind of risks lie therein.Divisional Director AustraliaAs banks advance across the t

239、hree phases of AI adoption,the potential for risk and reputational damage grows exponentially.Governance,ethics and accurate data are critical to maintaining stakeholder trust and unlocking AIs transformative potential.Build trust into the transformation roadmap Establish robust AI governance framew

240、orksBanks should implement comprehensive AI governance structures that set clear standards for accountability,transparency and compliance.This includes defining roles and responsibilities across teams,establishing protocols for monitoring AI performance and creating mechanisms to address risks such

241、as algorithmic accountability and ethical considerations proactively.Leaders and employees need to trust the AI tools they are using if they are to advocate their use to others.Embed ethical and bias-detection mechanismsEnsuring fairness and mitigating bias in AI systems is essential.Banks should de

242、velop and deploy tools that continuously audit AI models for unintended biases,especially in sensitive applications like credit decisioning or fraud detection.This requires diverse and representative training datasets,regular model testing and clear guidelines for acceptable model outcomes.Collabora

243、ting with independent auditors or ethics boards can provide additional oversight and credibility.Prioritize privacy by designAs banks handle vast amounts of customer data,integrating privacy considerations into every stage of AI development is crucial.Adopting a privacy by design approach means impl

244、ementing encryption,anonymization,and secure data-sharing practices as standard.This helps ensure compliance with data protection laws and builds trust with customers and stakeholders.Invest in security and resilienceAI systems are increasingly attractive targets for cyberattacks,including model poi

245、soning and adversarial attacks.Banks should focus on eliminating the weakest links in the infrastructure and invest in advanced security measures to protect AI systems and underlying data.This includes implementing real-time monitoring for anomalies,regularly updating defenses and training teams to

246、respond to emerging threats.Building resilience into AI systems helps ensure that they can continue to function reliably even in the face of disruptions or breaches.2 2025 Copyright owned by one or more of the KPMG International entities.KPMG International entities provide no services to clients.All

247、 rights reserved.Intelligent banking:A blueprint for creating value through AI-driven transformation|33ForewordIntroductionAt a glanceResearch findingsBuilding the intelligent bankFirst phaseSecond phaseThird phaseKey considerationsPreparing for an AI futureGuiding your AI transformationKey consider

248、ationsThe ability to retain people or to attract people who understand AI is important because you also need a technical skill set.Chief Information Security Officer USCreate sustainable technology and data infrastructure for AI adoption3Banks should balance the need for experimentation with the pur

249、suit of scalable returns.As the AI landscape evolves,banks should make no-regret investments looking for early wins that build momentum and can provide a solid foundation for future innovation regardless of how the technology matures.Invest in scalable and flexible infrastructureBanks should build a

250、 robust,cloud-native infrastructure capable of supporting the dynamic needs of AI technologies.This includes adopting platforms that enable machine learning operations for efficient model deployment and lifecycle management.By focusing on scalable solutions,banks can help ensure they have the flexib

251、ility to expand AI initiatives as needs grow,avoiding the pitfalls of rigid,short-term fixes.Establish comprehensive data management practicesHigh-quality,unified data is the backbone of effective AI.Banks should prioritize investments in advanced data platforms that consolidate siloed datasets into

252、 a single source of truth,enabling seamless data access and governance.Implementing tools for data quality,lineage and security ensures AI models are built on reliable foundations and can adapt as regulatory requirements or business needs evolve.Banks should clean,organize and validate data to keep

253、it free from inconsistencies,redundancies,and biases that could undermine AI performance.Observability tools can monitor data health continuously,addressing potential issues before they impact AI outcomes.Focus on modular and interoperable solutionsTo help future-proof their technology stack,banks s

254、hould invest in modular AI systems that can integrate with existing tools and accommodate emerging technologies.Open APIs,interoperable software and vendor-agnostic solutions allow banks to experiment with new innovations without locking themselves into specific ecosystems.This approach helps ensure

255、 flexibility in responding to technological advancements.Create a balanced investment portfolioBanks should adopt a dual strategy of no-regret foundational investments such as infrastructure,data management and governance frameworks paired with controlled experimentation in cutting-edge AI technolog

256、ies like generative AI or autonomous systems.This balance helps ensure the organization can drive immediate value while remaining agile enough to adopt transformative innovations as they mature.Key actions 2025 Copyright owned by one or more of the KPMG International entities.KPMG International enti

257、ties provide no services to clients.All rights reserved.Intelligent banking:A blueprint for creating value through AI-driven transformation|34ForewordIntroductionAt a glanceResearch findingsBuilding the intelligent bankFirst phaseSecond phaseThird phaseKey considerationsPreparing for an AI futureGui

258、ding your AI transformationKey considerationsI think theyre going to have to take an approach of being very,very strict around making sure that any investment rolling out some kind of AI function has specific KPIs,has return expectations and has a genuine process improvement,as opposed to just rolli

259、ng out something for the sake of it.Divisional Director AustraliaBuild a culture that uses AI to uplift human potential 4Human expertise remains indispensable alongside AI-driven automation.Attracting top talent in AI and machine learning is one of the most pressing challenges for the banking sector

260、,given the increasing global demand for these specialists.To meet this challenge,companies should not only recruit the best but also focus on upskilling and reskilling their existing workforce.Investing in robust learning and development programs helps ensure employees stay ahead of the curve as tec

261、hnology evolves.Such efforts are critical to building internal expertise and fostering a workforce capable of adapting to new AI capabilities and opportunities.Foster transformational leadershipLeadership should evolve to champion AI adoption by fostering a culture of trust,transparency and collabor

262、ation.Leaders should actively communicate the strategic vision for AI,emphasizing its role as an enabler rather than a disruptor.Transformational leadership practices,such as empowering teams to experiment with AI,recognizing successes and addressing concerns openly,helps build confidence and alignm

263、ent across the organization.Leaders also need to model adaptability,demonstrating a willingness to embrace change and invest in their own AI knowledge to guide the organization effectively.Build an AI-literate workforceBanks should create tailored learning programs that provide employees with a foun

264、dational understanding of AI,its applications,and its implications for their roles.Upskilling should go beyond technical teams;employees across all functions,including operations,customer service and risk management,need to understand how AI impacts their work and enhances their decision-making capa

265、bilities.For technical roles,banks should provide specialized training in AI development,machine learning and data science to close the talent gap.Address cultural resistance through change managementOvercoming cultural resistance requires a structured change management approach that includes clear

266、communication,employee engagement and support systems.Banks should proactively address fears around job displacement by emphasizing how AI can augment roles rather than replace them.Engaging employees in co-creating AI solutions fosters buy-in and helps ensure AI tools are designed with their practi

267、cal needs in mind.Establishing forums for feedback,collaboration and success stories can help dispel misconceptions and create enthusiasm for AI-driven transformation.Redefine roles and career pathwaysAI will likely fundamentally change the nature of work in banking,requiring a redefinition of roles

268、 and career pathways.Banks should identify areas where AI can automate routine tasks and shift employees toward higher-value activities,such as customer engagement,strategic analysis and innovation.Clearly mapping out these new career opportunities and pathways helps employees see AI as a means of g

269、rowth rather than a threat.Additionally,banks should establish new roles,such as AI ethics officers or value-stream leaders,to align human expertise with AI capabilities to help ensure responsible implementation.Key actions 2025 Copyright owned by one or more of the KPMG International entities.KPMG

270、International entities provide no services to clients.All rights reserved.Intelligent banking:A blueprint for creating value through AI-driven transformation|35ForewordIntroductionAt a glanceResearch findingsBuilding the intelligent bankFirst phaseSecond phaseThird phaseKey considerationsPreparing f

271、or an AI futureGuiding your AI transformationKey considerationsPreparing for an AI futureIt has become a clich to warn that banks must move quickly on AI or risk being left behind,but clichs often contain a grain of truth.Our research highlights a deeper tension:Banks,inherently risk-averse institut

272、ions,currently perceive the risks of AI as outweighing its benefits.This hesitancy creates a significant barrier to progress.Banking executives face a dual imperative:they must simultaneously articulate the transformative potential of AI and address its associated risks in a way that inspires confid

273、ence across the organization.Figure 8:A variety of benefits are selected in the top 5,showing that organizations are yet to distinguish where AI can be best usedPercentage who say their organization has achieved the following benefits through using AIIncreased operational efficiency and cost reducti

274、onImproved stakeholder managementFewer business risks and greater regulatory complianceImproved data analytics and insightsGaining competitive advantage in the marketEnhanced customer experienceEnhanced supply chain managementIncreased revenue/growthOptimized financial planning and forecastingBetter

275、 supported sustainability and environmental goalsFaster,data driven decision makingStrengthened cybersecurity and data privacyWhat benefits has your organization had from using AI in the business?(Maximum 5)n=18336%31%31%30%28%27%26%26%26%26%25%24%23%23%Development of new products and servicesIncrea

276、sed employee engagement and ability to attract talentSource:Intelligent banking:A blueprint for creating value through AI-driven transformation,KPMG International,2025 2025 Copyright owned by one or more of the KPMG International entities.KPMG International entities provide no services to clients.Al

277、l rights reserved.Intelligent banking:A blueprint for creating value through AI-driven transformation|36ForewordIntroductionAt a glanceResearch findingsBuilding the intelligent bankFirst phaseSecond phaseThird phaseKey considerationsPreparing for an AI futureGuiding your AI transformationPreparing f

278、or an AI futureAI is not a passing trend;it is an accelerating force that continues to evolve and disrupt.The first step is to build a compelling case for AIs long-term benefits,showing how it can fundamentally transform the business.This requires thinking beyond incremental efficiencies and envisio

279、ning a future where AI drives personalized customer experiences,optimizes risk management and enables entirely new business models.Leaders should engage in rigorous strategic analysis,including wargaming scenarios that explore how AI might reshape the competitive landscape.What will competitors do?H

280、ow might non-banking entrants and tech companies encroach on traditional markets?What are the risks of inaction,such as losing customers to more agile rivals or being disintermediated by fintechs?Answering these questions should galvanize action,making the cost of doing nothing starkly clear.At the

281、same time,banks should confront the risks of AI head-on.Moving forward with AI as an enterprise-wide asset requires robust frameworks for identifying,mitigating and monitoring risks.These include ethical concerns like bias,compliance challenges,security vulnerabilities and the operational risks inhe

282、rent in relying on complex models.Leaders need to create a culture of transparency and build processes that surface risks early,with mechanisms to detect and address unknown risks as they emerge.Proactive risk management isnt about eliminating uncertainty its about managing it effectively enough to

283、move forward with confidence.AI is not a passing trend;it is an accelerating force that continues to evolve and disrupt.To navigate this reality,banks should take no-regrets actions foundational investments in scalable infrastructure,data governance and AI-friendly operating models that position the

284、m to capitalize on future breakthroughs.These actions can help ensure that when the technology advances,the bank is ready to integrate,adapt and thrive.Inaction is not an option;banks should balance the perceived risks with the transformative potential of AI to remain competitive and relevant in a r

285、apidly changing financial landscape.ForewordIntroductionAt a glanceResearch findingsBuilding the intelligent bankFirst phaseSecond phaseThird phaseKey considerationsGuiding your AI transformation 2025 Copyright owned by one or more of the KPMG International entities.KPMG International entities provi

286、de no services to clients.All rights reserved.Building the intelligent bankFirst phaseSecond phaseThird phaseKey considerationsGuiding your AI transformationPreparing for an AI futureIntelligent banking:A blueprint for creating value through AI-driven transformation|37To gain a broad understanding o

287、f how leaders are navigating the opportunities and challenges of implementing AI,KPMG International conducted a robust research program involving multiple methodologies.This included in-depth interviews with eight AI experts spanning technology,government regulation and industry,as well as discussio

288、ns with sector-specific KPMG specialists.Qualitative research was conducted to uncover nuanced,industry-specific challenges and opportunities,including insights from several industry experts,including Erik Brynjolfsson of Stanford University,a renowned authority on AI and digital transformation.The

289、research was further strengthened by a quantitative survey of 1,390 decision-makers across key global markets,including 183 respondents from the banking sector.These leaders shared their experiences and perspectives on overcoming barriers to AI adoption,from dismantling legacy systems to addressing

290、organizational inertia.In parallel,an 18-month research project evaluated the realistic value at stake for fully deploying and adopting generative AI.Together,these inputs offer a clear roadmap for organizations to unlock AIs potential and drive meaningful,enterprise-wide change.MethodologyThe resea

291、rch was further strengthened by a quantitative survey of1,390decision-makers across key global markets,including 183 respondents from the banking sector.MethodologyMethodologyIntelligent banking:A blueprint for creating value through AI-driven transformation|38 2025 Copyright owned by one or more of

292、 the KPMG International entities.KPMG International entities provide no services to clients.All rights reserved.Research findingsBuilding the intelligent bankFirst phaseSecond phaseThird phaseKey considerationsPreparing for an AI futureGuiding your AI transformationForewordIntroductionAt a glanceWit

293、h over 150 years of experience in data,industry insights,technology and regulatory expertise,KPMG is uniquely positioned to help you uncover AI opportunities,work through critical business challenges and unlock new revenue streams.From strategy to implementation,we guide you in taking small,impactfu

294、l steps to tackle even the most complex problems all underpinned by trust.Weve invested in an AI-enabled platform for organizational change.It brings together the best of our thinking,frameworks,strategies and tools.So,you can change smarter and move faster eliminating inefficiencies and building tr

295、ust and confidence,at every step.experienceexperienceKPMG:Guiding your AI transformation with experience and trustForewordIntroductionAt a glanceIntelligent banking:A blueprint for creating value through AI-driven transformation|39 2025 Copyright owned by one or more of the KPMG International entiti

296、es.KPMG International entities provide no services to clients.All rights reserved.Research findingsBuilding the intelligent bankFirst phaseSecond phaseThird phaseKey considerationsPreparing for an AI futureGuiding your AI transformationDevelop a transformational AI strategyDefine your AI goals,ident

297、ify opportunities and risks and create a tailored strategy and execution plan.Build a business case with clear metrics to secure investments and ensure measurable success by scaling AI for enterprise-wide impact and building lasting capabilities.Ensure AI trust and complianceScaling AI introduces co

298、mplexities and risks.KPMG Trusted AI teams can help ensure your AI solutions are ethical,secure and compliant.Our Trusted AI Framework,built on ten ethical pillars,empowers organizations to boldly deploy AI responsibly,transparently and with confidence.2025 Copyright owned by one or more of the KPMG

299、 International entities.KPMG International entities provide no services to clients.All rights reserved.Intelligent banking:A blueprint for creating value through AI-driven transformation|40ForewordIntroductionAt a glanceResearch findingsBuilding the intelligent bankFirst phaseSecond phaseThird phase

300、Key considerationsPreparing for an AI futureGuiding your AI transformationWherever you are on your AI journey,KPMG can help:Empower your workforce with AIKPMG AI Enabled Workforce solutions deliver personalized adoption and upskilling experiences,helping your team embrace generative AI and infuse it

301、 into everyday work.Build a sustainable AI technology infrastructureLeverage KPMG professionals experience to integrate AI frameworks,platforms and accelerators,helping you ensure your technology infrastructure is ready to scale AI initiatives.We help clients harness the power and potential of AI.Fr

302、om strategy to implementation.Small steps to solving seemingly impenetrable problems.Underpinned by trust.You can discover endless opportunities with AI.You can with KPMG.Guiding your AI transformationAbout the authors and contributorsAdrian ClampGlobal Head of Connected EnterpriseKPMG International

303、Leanne AllenPartner,FS ConsultingTechnology and Data,DataScience&AI Capability LeadKPMG in the UKBenedikt HckHead of Artificial Intelligence KPMG in GermanyAdrian Clamp is the Global Head of Connected Enterprise KPMGs customer centric,agile approach to digital transformation,tailored by sector.H

304、e has over 30 years experience in leading complex technology change.He specializes in leading large scale digital transformation programs,deploying new advanced technologies,including AI to unlock value within large complex organizations.Adrian is a member of KPMGs global Consulting leadership team

305、and Global AI Council.He is dedicated to helping to deliver technology enabled innovation and new ventures which improve the lives of millions of customers,consumers,citizens and patients.Leanne Allen is a Partner in KPMGs Financial Services Tech Consulting Practice and leads KPMGs Data capability.S

306、he is an experienced data architect with broad experience across data management,data and systems architecture,data visualization,reporting and analytics and data migration.She is a thought leader in Tech-Data including driving the Data lens for KPMGs 30 Voices campaign and co-authoring a paper with

307、 UK Finance on the Ethical use of customer data in a digital economy.Leanne is passionate about driving a diverse culture in technology and in particular,supports working mothers in tech founding the Superwoman network at KPMG with over 100 women in the network.Benedikt Hck is Head of Artificial Int

308、elligence for KPMG in Germany.In this role,he is responsible for the go-to-market,the range of services and the internal use of(Gen)AI.He provides clients with holistic support during the transformation:from strategy,the implementation of suitable use cases and the enablement of the organization to

309、secure use through trusted AI.He is also a partner in the field of management consulting,where he focuses on the data/AI-driven optimization of processes,particularly in the area of customer centricity.We could not have created this report without the support,knowledge and insights of AI experts and

310、 colleagues around the world who contributed their time to this report.Thank you to Leanne Allen,Gerrit Bojen,Rebecca Brokmeier,Erik Brynjolfsson,Sam Burns,Swaminathan Chandrasekaran,Adrian Clamp,Pr Edin,Paul Greenan,Benedikt Hck,Kendall Knight,Dan Marsh,Scott Marshall,Joseph Parente,Jeff Potter,Dav

311、id Rowlands,Anthony Street and Ren Vader.2025 Copyright owned by one or more of the KPMG International entities.KPMG International entities provide no services to clients.All rights reserved.Intelligent banking:A blueprint for creating value through AI-driven transformation|41ForewordIntroductionAt

312、a glanceResearch findingsBuilding the intelligent bankFirst phaseSecond phaseThird phaseKey considerationsPreparing for an AI futureGuiding your AI transformationContactsFrancisco UraGlobal Head of Banking and Capital MarketsKPMG InternationalE:furiakpmg.esEMA Financial Services AI Leads:Leanne Alle

313、nPartner,FS ConsultingTechnology and Data,DataScience&AI Capability LeadKPMG in the UKE:leanne.allenkpmg.co.ukBenedikt HckPartner,KPMG Head of Gen AIKPMG in GermanyE:ASPAC Financial Services AI Lead:Brad DaffyPartner,Powered&AIKPMG AustraliaE:.auAngel MokPartner,Management ConsultingKPMG Chi

314、naE:Americas Financial Services AI Leads:Binoy PalakkalPrincipalKPMG in the USE: Mark Shanks Principal,Advisory,LighthouseKPMG in the USE:Global Digital Banking Hub:Dan MarshGlobal Digital Banking Hub LeadKPMG in the UKE:dan.marshkpmg.co.uk 2025 Copyright owned by one or more of the KPMG Internation

315、al entities.KPMG International entities provide no services to clients.All rights reserved.Intelligent banking:A blueprint for creating value through AI-driven transformation|42ForewordIntroductionAt a glanceResearch findingsBuilding the intelligent bankFirst phaseSecond phaseThird phaseKey consider

316、ationsPreparing for an AI futureGuiding your AI information contained herein is of a general nature and is not intended to address the circumstances of any particular individual or entity.Although we endeavor to provide accurate and timely information,there can be no guarantee that such information

317、is accurate as of the date it is received or that it will continue to be accurate in the future.No one should act on such information without appropriate professional advice after a thorough examination of the particular situation.2025 Copyright owned by one or more of the KPMG International entitie

318、s.KPMG International entities provide no services to clients.All rights reserved.KPMG refers to the global organization or to one or more of the member firms of KPMG International Limited(“KPMG International”),each of which is a separate legal entity.KPMG International Limited is a private English c

319、ompany limited by guarantee and does not provide services to clients.For more details about our structure please visit KPMG name and logo are trademarks used under license by the independent member firms of the KPMG global organization.Throughout this document,“we”,“KPMG”,“us”and“our”refers to the K

320、PMG global organization,to KPMG International Limited(“KPMG International”),and/or to one or more of the member firms of KPMG International,each of which is a separate legal entity.Designed by Evalueserve.Publication name:Intelligent banking:A blueprint for creating value through AI-driven transformationPublication number:139767-G|Publication date:February 2025