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1、1FINANCIAL SERVICES DATA+AI PREDICTIONS 2024FINANCIAL SERVICESDATA+AI PREDICTIONS 20242While global markets show signs of improvement,the longer-term economic forecast indicates potential storms ahead for the financial sector.To remain competitive in this climate,financial services organizations nee

2、d to become more agile and focused on cost optimization to remain competitive.They must also contend with the disruptive arrival of generative AI(gen AI).If financial services organizations implement this pivotal new technology improperly,they risk wasting millions of dollars and creating reputation

3、al or regulatory harm.If they are strategic about gen AI,however,they can accelerate growth and stay ahead of the competition.To learn more about the impact generative AI and other developments will have in the coming year,we sat down with our in-house experts to hear their predictions.In our report

4、 Data+AI Predictions 2024,we cover artificial intelligence(AI),cybersecurity and open-source technologies that will transform the broader landscape in the years to come.Here,well focus on whats next for the financial services sector in particular.3FINANCIAL SERVICES DATA+AI PREDICTIONS 2024123456THE

5、SE ARE SIX OF THE MOST IMPORTANT INDUSTRY TRENDS WERE TRACKING FOR 2024:Gen AI will be a make-or-break technologySpecialized AIs integrated with data sharing will be a competitive differentiatorMining unstructured data will be key to unlocking novel analyticsStrong data security for gen AI will be a

6、 defining factor for growthLeaders will stay nimble to meet quickly shifting regulatory requirementsA strong data strategy will distinguish industry leaders from followersFinancial institutions that unravel the complexities of gen AI and can create proprietary gen AI models will see major competitiv

7、e advantage and value creation as they transform customer experience,streamline operations and reduce waste.But for others,implementing gen AI will be complex,costly and insufficient in providing competitive differentiation.Accessing the vast volumes of data and compute capacity required to train an

8、d fine-tune large language models(LLMs)can be costly.Some analysts and technologists estimate the cost to be in the millions of dollars.Organizations will need to consider whether they want to create their own LLM trained on specific data,fine-tune a ready-made commercial or OSS LLM for a specific u

9、se case or implement a commercial LLM for the entire organization.The decision will likely hinge on several factors,including fine-grained access control to critical data and the availability levels of budget,time and specialized skills.According to our internal technology experts,these will be the

10、most important data requirements for gen AI and LLMs:Strong security and governance:To protect personally identifiable information,enforce regulatory and business-critical fine grained access controls,ensure ethical data use and mitigate potential risks,strict security and governance capabilities ar

11、e essential for model training and use.Scale of data:Access to large volumes of data is required to train or fine-tune LLMs.Data quality:AI systems are only as good as the data theyre built on,and poor data quality can lead to data bias.Compute power:AI models require substantial computational resou

12、rces for actions such as processing large datasets,making complex computations and processing data in real time.The foundation for success is a data platform that allows flexible,cost-effective ways to access gen AI whether organizations want to use off-the-shelf commercial and OSS LLMs for simple w

13、orkloads or fine-tune their own LLMs for more complex applications.4FINANCIAL SERVICES DATA+AI PREDICTIONS 2024GEN AI WILL BE A MAKE-OR-BREAK TECHNOLOGY“In 2023,nothing captured the imagination of the financial services industry more than generative AI,”says Rinesh Patel,Snowflakes Global Head of Fi

14、nancial Services.“While it will unlock enormous productivity and open up countless opportunities,organizations will also have to mitigate the risks of ungoverned usage to deliver successful outcomes.Unified data access,strong governance and robust security combined with technical expertise and a cle

15、ar understanding of business objectives will be essential.”Gen AI will become the modus operandi of customer interactionPublic sentiment for the financial services industry took a hit in the aftermath of the 2008 financial crisis.Since then,organizations have invested heavily in improving their rela

16、tionships with clients.But customer satisfaction for companies in this space still lags behind brands in other sectors such as technology,retail and consumer goods.To improve customer experience,financial services institutions will invest heavily in gen AI to develop customer interfaces that speed r

17、esponse times and simplify bureaucratic complexity.In fact,gen AI will become the new model for how customers interact with their bank or insurer.Popular enterprise gen AI use cases that can improve customer engagement include conversational copilots for data and document analysis and summarization,

18、and report generation.Chatbots or virtual assistants can now answer questions regarding investment ideas,portfolio performance,account information,and claims or fraud concerns.In wealth management,gen AI can support better investment decision-making and help improve the client experience.Gen AI will

19、 not replace financial services professionals who can develop client relationships and understand the complexities of client needs.Instead,it will deliver faster information and analytics,enabling client-facing employees from call center agents and branch managers to financial advisors and private b

20、ankers to serve customers more efficiently.“Gen AI presents opportunities to refine and redesign the customer experience,”says Patel.“Copilot-enabled user experiences will streamline and enhance consumer interactions with services provided by a bank,insurer or asset manager.Todays asset owners and m

21、anagers struggle to get deep and timely insights.Were already seeing gen AI transform the institutional client experience with custodian banks.”5FINANCIAL SERVICES DATA+AI PREDICTIONS 2024SPECIALIZED AI APPLICATIONS INTEGRATED WITH DATA SHARING WILL BE A COMPETITIVE DIFFERENTIATOREconomic volatility

22、 is driving financial institutions to seek new revenue streams.One potential source is data sharing.Financial services companies have robust client and transaction data that can power gen AI-enabled insights both within and outside their organization.For example,custodian banks are building applicat

23、ions that provide institutional clients with gen AI-powered assets and security servicing data,models and analytics.Payment companies are using transaction data and analytics to support their customers in tackling fraud and better managing risk.Exchanges and data providers are packaging their data w

24、ith AI applications to drive differentiation for their customers.The opportunities to reduce waste,streamline operations,reduce complexity and improve time-to-analytic-insight through a data-sharing ecosystem will be significant.But sharing such data easily and securely has been a challenge with tra

25、ditional data platforms.Financial services organizations need a modern data platform that allows them to to anonymize data and share it without moving or copying it,or risking the exposure of PII.Increasingly,financial institutions will monetize their data through apps and data marketplaces.The ones

26、 that do so successfully will gain a competitive advantage.“When it comes to enabling a next-generation data experience for our customers,data sharing will remove the need to physically move data in large volumes from vendors to customers and from customers to counterparties and regulators,”says Pat

27、el.“This opens up completely new opportunities for revenue streams from data sharing and better business decision-making.”When it comes to enabling a next-generation data experience for our customers,data sharing will remove the need to physically move data in large volumes from vendors to customers

28、 and from customers to counterparties and regulators.”RINESH PATEL,Global Head of Financial Services,SnowflakeMINING COMPLEX UNSTRUCTURED DATA WILL BE THE KEY TO UNLOCKING NOVEL ANALYTICS By harnessing the power of unstructured data,organizations will be able to transform their customer 360 initiati

29、ves,accelerate customer onboarding and better enact know your customer processes.”RINESH PATEL6Much of the worlds data is unstructured,and thats not going to change any time soon.Financial services companies that can harness that data for gen AI-enabled insights will reap the rewards.They will be ab

30、le to open up new analytics use cases in every subsector from banking and asset management to payments and insurance.From customer onboarding and claims management to due diligence and company valuations,banks and insurance companies spend a significant amount of time and resources looking for infor

31、mation in documents.Gen AI can help employees more effectively find and understand information in contracts,credit memos,reports and other unstructured PDF documents.Payment and insurance companies can use gen AI to analyze large volumes of unstructured data and deliver faster customer service throu

32、gh chatbots.The possibilities are endless.But traditional data management systems struggle to store and process vast troves of unstructured data ranging from emails and social media posts to scanned documents,video and audio recordings.A modern data platform with cloud-based storage and processing c

33、apabilities can scale to handle that data according to organizational needs.“So much information about the customer is locked in unstructured data such as call center transcripts,tax documents,application forms and social media posts,”says Patel.“By harnessing the power of unstructured data,organiza

34、tions will be able to transform their customer 360 initiatives,accelerate customer onboarding and better enact know your customer processes.”FINANCIAL SERVICES DATA+AI PREDICTIONS 20247STRONG DATA SECURITY WILL BE A DEFINING FACTOR FOR GROWTHData privacy and security concerns are top of mind among f

35、inancial services leaders,and rightfully so.In 2022,financial services companies were the second-most-targeted industry for cyberattacks that resulted in data compromises,behind only healthcare.And even as recently as November 2023,the U.S.financial services division of Chinese bank ICBC was hit by

36、a cyberattack that reportedly disrupted U.S.Treasury markets.This demonstrates how gen AI is a double-edged sword in the realm of financial services cybersecurity.On one hand,it can boost security by detecting risks and providing immediate automated responses.On the other,it can introduce new vulner

37、abilities.“Robust security and governance controls sit at the heart of any enterprise data strategy and now at the core of gen AI implementation,”says Patel.“Poor data control in these areas has real consequences.Were talking about noncompliance and spiraling costs,as well as poor customer experienc

38、e and damaged reputations.”This is why organizations will increasingly focus on security and governance capabilities to realize the value of gen AI while mitigating the risks.Leading financial institutions will rely on strong data foundations that share,secure and govern data throughout the entire b

39、usiness ecosystem as they build gen AI solutions.Theyll prioritize data solutions that work across clouds.Theyll also prioritize platforms with built-in,highly observable and easy-to-use security capabilities that strengthen the business while helping them more effectively respond to threats.FINANCI

40、AL SERVICES DATA+AI PREDICTIONS 20248FINANCIAL SERVICES DATA+AI PREDICTIONS 2024LEADERS WILL NEED TO STAY NIMBLE TO MEET QUICKLY SHIFTING REGULATORY REQUIREMENTSGen AI will face increasing regulatory scrutiny as governments worldwide try to stay ahead of concerns about data privacy,intellectual prop

41、erty rights and the misuse of AI-generated content.In the U.S.,in an effort to prevent firms from placing their interests ahead of their investors,the SEC recently proposed new rules that would require broker-dealers and investment advisers to take steps to address conflicts of interest associated w

42、ith their use of predictive data analytics and similar technologies to interact with investors.China published draft provisions for regulations that aim to ensure the healthy development and standardized application of gen AI.And the European Union is finalizing its A.I.Act,which aims to strengthen

43、rules around data transparency,human oversight and accountability.The quickly evolving landscape presents a challenge for companies looking to stay compliant with gen AI regulations.Businesses with strong compliance,AI governance and data security programs will be well-positioned to handle future po

44、licy changes and pivot their operations accordingly.Financial services companies that can keep their risk and compliance teams nimble to meet changing reporting needs will be poised to lead the industry.“How firms operationalize in the cloud is now also increasingly under regulatory scrutiny,with gl

45、obal organizations having to meet the demands of different regulators in different regions,many with potentially different regulatory reporting requirements,”says Patel.“Organizations with a wide geographical reach will have to account for these myriad reporting requirements.Theyll need to adjust th

46、eir data strategies or architectures to accommodate these jurisdictional requirements and spin up workloads accordingly.There is a new bar for doing business in the cloud.”How firms operationalize in the cloud is now also increasingly under regulatory scrutiny,with global organizations having to mee

47、t the demands of different regulators in different regions RINESH PATELFINANCIAL SERVICES DATA+AI PREDICTIONS 2024A STRONG DATA STRATEGY WILL DISTINGUISH INDUSTRY LEADERS FROM FOLLOWERSCompanies that stay ahead of the trends will be able to leverage gen AI for maximum growth.To do it,they need a str

48、ong data strategy that involves several key elements.Here are four of the most important:Access:Establish access to data from various sources.Data silos will only make outputs incomplete or inaccurate and require extra work to overcome.Quality:Ensure source data is consistent and standardized,and ex

49、pand the framework to include measures for issues such as bias.Governance:Develop strong governance frameworks that ensure the high quality of all data.Security:Focus on securing the enterprises data and protecting personal information while actively monitoring a fluid regulatory environment.A holis

50、tic approach to data management will help improve metadata,ensure consistency and raise the quality of what goes into an LLM and,thus,the quality of the output.910FINANCIAL SERVICES DATA+AI PREDICTIONS 2024LEVERAGING MODERN DATA CLOUD PLATFORMSA modern data cloud platform is critical to executing su

51、ccessfully on such a strong data strategy for gen AI.A handful of capabilities differentiate a platform that can be counted on to securely access,process,manage and analyze data from across the financial services ecosystem:Unified platform:A single platform helps enterprises break down data silos,al

52、lowing them to bring more workloads directly to their data.This includes the ability to run and fine-tune leading LLMs.Data security and governance:A built-in governance solution supports the stringent requirements of todays financial services organizations and allows customers to securely collabora

53、te on data across the enterprise while efficiently meeting regulatory requirements.Integration with AI and machine learning(ML)workloads:Developers need to be able to effortlessly register and deploy containerized data apps using a secure,managed infrastructure.Such additional flexibility drasticall

54、y expands the scope of AI,ML and app workloads that can be brought directly to data.Flexible access to leading LLMs:Customers benefit from a platform that enables direct access to leading open-source,commercial and other third-party external LLMs.Data analysis and building AI apps:A fully managed cl

55、oud data service empowers enterprises to discover,analyze and build AI apps in one place.Those who will stand out as leaders in the financial services industry will be those who choose a simple and cost-effective platform that meets the complex demands of data management while building a strong data

56、 foundation for gen AI success.Learn how the Data Cloud can power better business outcomes and help you prepare for whats ahead.ABOUT SNOWFLAKEOrganizations use Snowflakes Data Cloud to unite siloed data,discover and securely share data,power data applications and execute diverse AI/ML and analytic

57、workloads across multiple clouds and geographies.Organizations,including 647 of the 2023 Forbes Global 2000 as of October 31,2023,use the Snowflake Data Cloud to power their businesses.Learn more: 2023 Snowflake Inc.All rights reserved.Snowflake,the Snowflake logo,and all other Snowflake product,fea

58、ture and service names mentioned herein are registered trademarks or trademarks of Snowflake Inc.in the United States and other countries.All other brand names or logos mentioned or used herein are for identification purposes only and may be the trademarks of their respective holder(s).Snowflake may not be associated with,or be sponsored or endorsed by,any such holder(s).