Generative AI in financial services

These LLMs could respond to threats and synthesise complex data into clear guidance that professionals can act on. Gen AI’s pattern recognition capabilities could improve the surveillance capabilities of older forms of AI. The GalaxIA project brings together a cross-functional team of over 100 experts specialising in AI, security, cloud computing, business strategy, user experience, development, data science and architecture. CaixaBank, one of Spain’s leading financial institutions, has launched the second phase of its ambitious generative artificial intelligence (AI) initiative, dubbed GalaxIA. Available at Shinhan Bank branches across South Korea, the AI bank tellers can be found at digital desks and smart kiosks. They are capable of handling 64 different consultation tasks often performed at ATMs, including deposits, credit loan applications, and deposit-backed loan executions.

Discover how EY insights and services are helping to reframe the future of your industry. While artificial intelligence and generative AI continue to grab the headlines this year, what challenges and opportunities will marketers see next… Dig deeper into GenAI insights, in-person or onlineToday’s announcement was made during Money20/20, branded the largest global fintech event enabling payments and finserv innovation, convening in Las Vegas, Oct. 27 – 30. Attendees are invited to engage with SAS experts on GenAI and other topics throughout the conference at Booth 3703.

We are committed to delivering accurate, unbiased news and information to our audience, and we will continue to uphold our ethics and principles in all of our work. Fintech companies must therefore implement effective security measures to navigate and protect sensitive financial data and maintain customer trust. KPMG combines our multi-disciplinary approach with deep, practical industry knowledge to help clients meet challenges and respond to opportunities. With bank technology leaders suggest they are inundated with requests from the business for genAI support.

Our community is about connecting people through open and thoughtful conversations. We want our readers to share their views and exchange ideas and facts in a safe space. BizClik – based in London, Dubai, and New York – offers services such as Content Creation, Advertising & Sponsorship Solutions, Webinars & Events. Gen AI gives programme managers a possible tool with which to communicate with participants about their desires in real-time, enabling better matching of people to rewards. Its conversational powers could also guide users through sometimes complicated programmes. Data-synthesising Gen AI solutions could promise advice unencumbered by emotions or wishful thinking.

Transforming Contract Management In Banking And Enterprises With Generative AI

Model documentation refers to the detailed recording of how AI systems make decisions, including the data sources used and the decision-making processes involved. This documentation becomes crucial for audit trails and regulatory compliance. To capitalize on the most promising opportunities from adaptive banking, banks will need several key building blocks to leverage the natural language orchestration and product manufacturing capabilities of Gen AI. Banks in the region have long embraced FinTech and are well positioned to rapidly incorporate innovation generated through the FinTech hubs in Dubai, Abu Dhabi, Doha, Riyadh and Cairo.

Treasury outlook from the Oversea-Chinese Banking Corporation (OCBC) pointed out that as recent inflation readings had boosted the Fed’s confidence on bringing down inflation, rate-cut odds shifted to the dovish side. Bank of America (BofA) is forecasting a first rate cut in December, despite a growing possibility of an additional one in September. The US Fed’s decision to keep interest rates higher for longer continued to benefit Hong Kong banks’ performance in 2023, with notable increases in net interest margins (NIM) and operating profit.

BANKING EXCHANGE FLY-IN CONFERENCE

For starters, nearly half (49%) of all companies in our EXL study said they’ve encountered challenges with AI explainability and lack of leadership buy-in. Cost or budget concerns, lack of resources, and legacy systems were also noted as key issues. More broadly, gen AI could transform compliance and security measures, enabling firms to meet regulatory requirements more efficiently while reducing the cost and effort involved in combating financial fraud and managing risk.

The findings in the study show that these processes are ones that are primed for optimization, suggesting that there are plenty of unrealized opportunities to drive new growth. The story is similar with generative AI (GenAI), as nearly half (47%) reported already using it, the most common uses being for product/service development (58%) and customer care/service (46%). Another 38% said they plan to incorporate it into their business within the next 24 months. Notably, among top finance firms in the study, 85% said their boards are involved in the decisions about the use of GenAI. To get a sense of exactly where banks and lenders are with AI, EXL surveyed 98 senior executives at the leading financial services firms in the U.S.

Another critical challenge for the industry’s rollout of AI is ensuring the quality of data, which can either accelerate progress or lead to false starts if mishandled. The summit will also explore new data sources, security considerations and strategies for permissioning access to sensitive information. Of this issue, Chris Harrison, Industry Executive at Oracle, says “the strategic risk of not engaging in generative AI projects is greater than the operational and compliance risks.”

The new Generative AI solutions from Temenos enable users to perform natural language queries, generating unique insights and reports swiftly. This reduces the time required for business stakeholders to access critical data. The technology is transparent and explainable, ensuring that users and regulators can easily verify the results produced. With a robust security framework, these solutions are set to transform banking efficiency, operations, and product management.

The research found that, while 80% have implemented AI to some degree and have expressed plans for continued and aggressive implementation over the next two to three years, over half (55%) are using it in a narrow band of functions. Hyper-personalization – Banks and others are leveraging AI and non-financial data to better create and target highly personalized offerings. You can foun additiona information about ai customer service and artificial intelligence and NLP. This is shifting the paradigm in FS from a reactive service to one that is truly intuitive and responsive. ChatGPT It now handles two-thirds of customer service interactions and has led to a decrease in marketing spend by 25%. Rather than reactively engaging when customers have a request or issue, it could eventually anticipate and proactively reach out to customers before they even know something is wrong. Today, leading banks know this, which is why we are now entering an era of increased competition in banking – as financial institutions race to provide the best customer experience.

Generative AI has the potential to transform AML and BSA programs by automating complex tasks, improving detection capabilities, and enhancing regulatory compliance. Despite the challenges of transparency, governance, and data privacy, the integration of AI offers substantial benefits in terms of operational efficiency and regulatory compliance. Financial institutions must continue to innovate and adapt to leverage the full potential of AI, ensuring that their compliance programs remain robust, transparent, and effective in addressing evolving regulatory requirements.

Internally oriented use cases for generating content and automating workflows (e.g., knowledge management) are typical­­­­ly good starting points. Banks can use GenAI to generate new insights from the data they

collect on buying habits, trade patterns and internal tax

compliance and to createadditional revenue streams. Over time, banks should develop a comprehensive vision for the business, incorporating the full innovation portfolio and be ready to pivot in an agile way as AI technology continues to evolve rapidly. The aged, heavily-customized technology architectures in place at many banks today, with all their workarounds and poor data flows, are a barrier to AI implementation. Recognizing these constraints, a significant proportion of survey respondents said they did not believe their institution had the correct technological infrastructure and capabilities to implement GenAI.

• Today, banks of all sizes have access to a considerable amount of customer data that’s processed and stored on a daily basis, from credit history to buying activity.
• The fact is, tomorrow’s financial service winners and losers may be determined, in large part, by how effectively they’re able to deploy and scale GenAI applications today.
• Daniel Pinto, JPMC’s President and COO, recently estimated that gen AI use cases at the bank could deliver up to $2 billion in value.
• Generative AI can also automate time-consuming tasks such as regulatory reporting, credit approval and loan underwriting.

He acknowledged that distributed-ledger technology is going to play a significant role in financial services, and urged banks to be ready to interweave DLT into their overall operations. HKMA’s initiatives include Project Ensemble, a grouping of banks and fintechs aimed at providing a layer of interoperability for tokenized deposits or stablecoins. HKMA has found that tracing certain keywords on Twitter and other platforms show how the SVB collapse was narrated in real time. That’s a backtest, but it suggests such monitoring could help bankers and regulators appreciate the scale of an unfolding drama.

Adding Gen AI to existing processes helps banks convert customer call to data, search knowledge repositories, integrate with pricing engine for quotations, generate prompt engineering, and provide real-time audio response to customers. This, in turn, improves user experience as it minimizes the wait time for the customer, reduces redundant and repetitive questions, and improves interaction with the bank. With GenAI technologies such as Google’s Vertex AI Search, and Google Conversational AI, financial service staff can do more than query multiple databases, and pull relevant insights in near real-time. Suddenly, complex data becomes accessible and useful, in time to make a difference. With the ability to analyze customer preferences and behaviors, a GenAI-powered digital agent can recommend financial products and services that are tailored to individual customer needs. Ultimately, that digital agent could customize pricing in real-time, delivering competitive offers to target customers, such as preferential lending rates, based on an enhanced measurement of their credit risk.

• This adoption advances the ongoing digital transformation of the banking industry.
• AI-powered contract management solutions help comply with regulatory standards and mitigate risks effectively.
• Starting with cost, potential users of the technology stand to benefit greatly from a combinatorial effect caused by three powerful forces.
• With the threat of cyberattacks a leading concern for banks and FIs, AI applications must be made as simple as possible.
• And challenger banks have doubtless upped the stakes, especially in customer service and with product innovations such as Buy Now, Pay Later (BNPL).
• Build confidence, drive value and deliver positive human impact with EY.ai – a unifying platform for AI-enabled business transformation.

The many banks that need to update their technology could take the opportunity to leapfrog current architectural constraints by adopting GenAI. However, for GenAI to be useful in the workplace, it needs to access the employee’s operational expertise and industry knowledge. Recent research from EY-Parthenon reveals how decision-makers at retail and commercial banks around the world view the opportunities and challenges of GenAI, as well as highlighting initial priorities. How does banking stack up to other sectors in the use and adoption of GenAI? With experience in both the institutional and the startup side, Kundu brings his knowledge of data, AI, and how organizations work to discuss how genAI is impacting finance. Shameek Kundu discusses the implications of these changes with DigFin‘s Jame DiBiasio.

Our survey confirms this pattern, as 45% of participants have emphasized that identifying use cases and inadequate focus on Gen AI initiatives are among the primary obstacles when implementing Gen AI. Unlike traditional virtual models, these AI bank tellers are modeled after five actual Shinhan Bank employees. These employees were filmed in a dedicated AI studio to develop high-quality virtual humans with lifelike appearances and movements.

CARY, N.C., Oct. 28, 2024 /PRNewswire/ — A new report on the use of generative AI in banking finds that financial services leads other industries in implementing the technology. A recent survey found that 17% of banking leaders have fully integrated GenAI into their regular processes. Further, 3 in 5 currently use GenAI to some degree – and nearly all the rest plan to begin soon. But the study confirmed that banks are already realizing GenAI gains across the business. As large language models (LLMs) continue to advance, GenAI is emerging as a key tool in helping bank compliance professionals stay more current on the regulatory landscape, and ultimately optimize their risk and compliance programs.

When building an operating structure to support GenAI capabilities, put in place ways to track and measure value, outcomes, and ROI. Determine how to build fluency with GenAI ChatGPT App across your business, with training, talent acquisition, and partnerships. Finally, establish ground rules for accountability and the ethical use of your GenAI tools.

Nearly one-third (29%) is already using this form of GenAI, and another 33% said they are actively considering it. In this age of digital disruption, banks must move fast to keep up with evolving industry demands. Generative AI is quickly emerging as a strategic tool to carve out a competitive niche. With unique insight into a bank’s most resource-heavy functions, risk and compliance professionals have a valuable role in identifying the best areas for GenAI automation. While GenAI has tremendous potential, there are emergent risks, especially in areas such as data confidentiality, GenAI hallucination, bias, toxicity and cyber security.

But if the cost base for GCC banks is similar to their international counterparts’—staff compensation at global banks makes up half the cost-base on average, Moody’s Investors Service estimates—they may wish to accelerate GenAI integration. Regardless of the potential upheaval, Saxena thinks the latest innovations could quickly up banks’ compliance programs, where generative AI’s speed and accuracy could contain reputational exposure to issues such as money laundering, etc. Whether through automation or augmentation, Accenture expects dramatic results in the back, middle, and front offices,  predicting 25% of all staff will be impacted by both. The UAE is backing AI at the government level, with the minister for AI—a position created in 2017—noting in February that nine banks and nine other financial institutions are using blockchain solutions. In time, use-cases could expand to include robo-advisers and customer-facing chatbots in private banking, wealth management and insurance, HKMA said. Addressing the “black box” issue involves implementing explainable AI techniques that provide insights into model behavior and decision-making processes.

Human involvement is most important for strategic tasks (37%), improving internal processes (34%) and customer experience (29%). We worked with a professional services firm to implement an AI-driven contract management solution to handle a huge magnitude of client contracts. It automated the extraction and review of key contract terms, reducing the need for manual intervention and allowing the firm to reallocate resources to more strategic tasks. From the team’s point of view, the technology has so far been helpful during the process of product design for programmers, those inhouse with banks or at third-party fintechs. While at the same time, it’s moving slowly towards an integration into products themselves, with pilot projects being tested out, he shared. It is therefore clear that while AI has the capacity to revolutionise banking and financial services, it’s important to have a rigorous understanding of its best use, and the right systems in place to support AI integration.

With more progress on the horizon,  financial services firms need to be able to implement technological advancements cost  effectively, to benefit from scale and innovation. After all, a significant amount of financial gen ai in banking service organizations’ marketing, onboarding, customer service, and regulatory reporting involves repetitive content creation. When this work is completed by humans, the potential for errors often exists.

It highlights key  considerations for implementing Gen AI systems, which includes the need for high quality  data, fit-for-purpose technology and the ability to distinguish between Gen AI models. The  paper also provides an overview of how to construct Gen AI use case portfolios and  identify optimal use cases based on requirements. It also discusses key risks and  mitigants related to data, systems, cyber security, dependency and sustainability, which  are familiar to the industry. Financial institutions are implementing Gen AI solutions across multiple business functions. These range from customer service automation to fraud detection systems and regulatory compliance tools. The technology differs from traditional AI systems in its ability to generate new content rather than simply analyse existing data.

For example, Synthesia utilizes an AI platform to create high-quality video and voiceover content tailored for financial services, while Deriskly provides AI software aimed at optimizing compliance in financial promotions and communications. The ability of LLMs to model sequences and make probabilistic decisions enables their application in complex analytical tasks. They can generate comprehensive reports by synthesizing information from multiple sources, summarize lengthy regulatory documents, and identify patterns indicative of compliance risks. These capabilities enhance the efficiency and accuracy of compliance processes, allowing financial institutions to respond proactively to regulatory requirements and potential risks. Additionally, LLMs can assist in training and onboarding by generating educational materials and interactive simulations for employees.

You don’t want to be building use cases for the BlackBerry when the iPhone is coming. Amplifying this rapid expansion in hardware productivity is a dramatic improvement in the software – the algorithms that account for the magic of generative AI. We’re also seeing a proliferation of more economical midsize and small language models, which require less training and less compute.

AI facilitates seamless collaboration among contract negotiation and review stakeholders. Advanced NLP algorithms enable real-time analysis of contract terms and conditions, identifying potential areas of contention or ambiguity. He told FA that the team is now in talks with a leading Chinese bank in terms of system applications, where “over 80%” of the conversations have been around building a resilient and secure platform. Financial institutions have been pushing forward a more general level of digitisation across functions, apart from cutting-edge technology developments such as AI. At the same time, he also noted that as timing and pace of a rate cut remain uncertain, banks should plan their strategies accordingly.