Risk Reducing AI Use Cases for Financial Institutions

In capital markets, GenAI is revolutionizing trading, risk management and compliance. Financial institutions are exploring the potential of generative AI to enhance their operations while navigating a regulatory landscape that emphasizes caution and due diligence. Regulatory bodies are concerned with the ethical implications, transparency, and accountability of AI systems. As such, financial institutions must balance innovation with regulatory compliance, ensuring that AI applications are transparent, auditable, consistent, and align with existing legal frameworks. The current atmosphere reflects a cautious optimism, with institutions actively seeking ways to harness AI’s benefits while mitigating potential risks.

GlobalData finds that 70% of insiders believe that Generative AI will have a positive impact on the banking industry in the next three years. Having developed a greater understanding of AI since its rapid emergence over the past few years, banks are increasingly finding valuable use cases for the technology, most notably improving customer experiences and fraud detection. This acknowledgment of AI’s limitations dovetails with the broader landscape of challenges that banks face, including cultural resistance and strategic alignment.

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For example, GenAI has the potential to support the hyper-personalization of offerings, which helps drive customer satisfaction and retention, and higher levels of confidence. Acquisitions and joint venture opportunities can help banks build new or enhance existing GenAI-focused ecosystems and deliver new products and solutions more quickly. The business case for such deals should be based on a careful assessment of capabilities and with results from initial use cases. Given the newness of GenAI and the limited tech capabilities of many banks, acquisitions or partnerships may be necessary to access the necessary skills and resources. GenAI’s ability to work with unstructured data makes it easier to connect and share data with third parties via ecosystems. Half (51%) of banks said they prefer partnerships as their go-to-market approach for GenAI use cases, as opposed to in-house development.

In addition, building “knowledge graphs” from existing institutional expertise will allow GenAI to extract valuable insight. 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.

The dual nature of AI in cybersecurity, the ethical dilemmas posed by AI-driven decisions, and the imperative for data privacy underscore the need for a balanced approach. By investing in talent development, fostering research and innovation, and cultivating strategic partnerships, the banking sector can mitigate generative ai use cases in banking these challenges and seize the moment to redefine financial services. In consumer banking, it elevates service delivery and customer interaction, investment banking sees more streamlined research and financial modeling, while corporate and SMB banking benefits from enhanced business lending and risk management.

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In addition, references should be provided to the material that was used for producing outputs. Financial institutions face a complex regulatory environment that demands robust compliance mechanisms. The integration of generative AI, particularly LLMs, offers transformative potential to automate compliance processes, detect anomalies, and provide comprehensive insights into regulatory requirements. TUATARA also helped leading cooperative bank BS Brodnica continue to challenge the status quo in customer service. The organization, which was one of the first cooperative banks in Poland to offer digital banking services, looked to harness AI automation to give its customers access to instant, high-quality support.

• “It enables executives to get information in a comprehensive way faster, which allows you to make your decisions faster and quickly move toward execution,” Donahue said.
• These are the puzzles that chief information officers, chief technology officers, and data leaders who oversee their firms’ AI strategies are expected to solve.
• Schumer emphasized that the regulations should focus on protecting workers, national security, copyright issues and protection from doomsday scenarios.
• Among the use cases for gen AI at Bank of America outlined by Bajwa is improving developer efficiency and productivity within the bank’s large engineering organization of more than 10,000 developers.
• Similarly, many banks have been pursuing industry verticalization and deposit retention strategies, as well as seeking new and diversified revenue streams.
• This is a significant departure from traditional methods of drug development, which are often slow, expensive, and fraught with failure.

Editors would then need to write additional content to flesh out the articles, pushing the search for unique sources of information lower on their list of priorities. In past automation-fueled labor fears, machines would automate tedious, repetitive work. GenAI is different in that it automates creative tasks such as writing, coding and even music making. For example, musician Paul McCartney used AI to partially generate his late bandmate John Lennon’s voice to create a posthumous Beatles song. In this case, mimicking a voice worked to the musician’s benefit, but that might not always be the case. Member firms of the KPMG network of independent firms are affiliated with KPMG International.

There is no question that generative AI will disrupt many banking processes and how banks interact with their customers. However, if the technology is deployed in smart ways, it will allow banks to serve customers better while also improving their economics. A recent Bain & Company survey of banks indicated that over 80% of respondents plan to upgrade their data architecture in the next three years.

“These are big legacy organizations that have a lot of data, a wide range of potential use cases and money to spend on compute power,” Mousavizadeh said. AI is transforming customer service through chatbots and virtual assistants, providing personalized and efficient client engagement. These AI systems can handle a wide array of queries, from account information to complex financial advice. Model benchmarking provides a standardized approach to evaluating AI performance, ensuring that models meet regulatory and operational standards. Documentation involves maintaining detailed records of model development, training, validation, and deployment processes. LLMs like Granite from IBM, GPT-4 from OpenAI, are designed to intake and generate human-like text based on large datasets.

At BBVA, we want to further promote our role as pioneers when it comes to innovating in financial services and we are therefore firmly committed to exploring the potential of this technology. We believe that generative AI, when used safely and responsibly, is a game-changer in how we support our customers in their decisions and offer personalized services. It also happens to stimulate creativity among our employees,” explains Ricardo Martín Manjón, Global Head of Data at BBVA. Data and technology are the key levers of transformation at BBVA, which for over a decade has been running specific development centers for advanced analytics and artificial intelligence, now known as AI Factories, in Spain, Mexico and Türkiye.

Information technology

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. 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.

These include tokenization, virtual products and digital wallets, electronic transactions, straight-through transaction processing and product accounting, as well as sophisticated cloud-based risk and financial crime detection models. As the banking industry increasingly moves towards digitisation, the adoption of advanced AI technologies becomes crucial. GenAI, with its ability to synthesise and generate content, offers unparalleled opportunities to automate complex processes, provide ChatGPT personalised customer experiences, and strengthen security measures. It’s where the productivity gains get to a point where you can start to do things you never thought possible. With genAI and a host of other complementary technologies applied, one could theoretically start to run a continuous close. Hook some visualization tools up to that data, and CEOs and decision-makers could tap into a real-time dashboard of key financial, compliance, risk and cost metrics, for example.

Artificial intelligence (AI) will undoubtedly be the most transformative technological force for businesses in the coming years. Bank of America‘s virtual financial assistance service, Erica, has seen considerable use since its launch in 2018. The service has recorded over 2 billion interactions, assisting ChatGPT App 42 million customers. The first billion interactions took four years, but the second billion were achieved in just 18 months, indicating a significant increase in client engagement. Erica has handled over 800 million enquiries, providing personalised insights and guidance over 1.2 billion times.

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In cases like this, where generative AI is taking on repetitive, boring tasks, banks could get productivity gains of around 20%, Abbott estimates. “You can take the Fannie and Freddie mortgage underwriting policies and tell a large language model to act like a loan underwriter and look for all the red flags against the policies that might be in a loan,” he said. “It’ll take in all the documentation from the loan, the policy, and start looking for those red flags. There’s still a human in the loop, because you have to be assured from a model risk management perspective that you’re doing the right thing.” The report noted that call center agents are typically treated rudely by irritated customers. “Top workers are generally not paid for their contributions to the training data that AI systems use to capture and disseminate their skills,” the report noted. “Yet, without these contributions, AI systems may be less effective in learning to resolve new problems. Our work therefore raises questions about how workers should be compensated for the data they provide to AI systems.”

Swamy and Jermyn are two of the thought leaders on the AI 100 list, which was curated by data scientists at H2O.ai (an AI software company) and Evident (an AI research firm). An emerging challenge for Wall Street firms now is closing the gap between the staff and the technology, and some firms are finding a “bit of friction” with adoption, Accenture’s Smith said. It’s a “lack of available use cases rather than a deliberate decision not to,” a fundamental analyst at one of the world’s biggest hedge funds told BI.

Ensuring that their AI systems do not violate privacy, prevent bias from creeping in, and remain secure keeps enterprise CXOs awake at night. The new Generative AI solutions from Temenos enable users to perform natural language queries, generating unique insights and reports swiftly. 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.

They automate routine tasks such as processing documents and verifying information. AI co-pilots – Co-pilots that work alongside employees will streamline workflows and provide new insights, leading to significant productivity improvements. Citizens Bank for example, expects to see up to 20% efficiency gains through gen AI as it automates activities like coding, customer service and fraud detection. In the future, these co-pilots could tailor investment strategies in real-time or predict market trends, helping to fortify FS firms’ competitive edge and deliver differentiated client outcomes. This strategic realignment encompasses not just consumer-centric services but also aims to bolster risk management frameworks, optimize compliance procedures, and drive innovation in product development and financial advisory offerings.

Greater scrutiny demands that banks align themselves with responsible AI practices. While proofs of concept might work initially, the widespread application of use cases requires enhancements consistent with a larger scale, echoing DevOps principles. Generative AI also introduces novel requirements, from API management to vector databases to application hosting. As a result, an ecosystem of vendors tailored to address specific elements of the tech stack enabling AI and machine learning (ML) operations is taking shape. AI operating models can vary by the degree of centralization, particularly when prioritizing use cases and setting ethical standards (see Figure 4).

This ability enables accurate risk assessments, aiding banks in making more informed decisions regarding loan applications, investments and other financial operations. These AI capabilities help banks optimize their financial strategies and protect themselves and their clients. However, as we embrace AI’s opportunities, we must also navigate its challenges with foresight and responsibility.

At Man Group, a machine-learning tool can send brokers trades that could offer the best pricing on a specific stock or security based on historical execution data. Bank of America spent $3.8 billion on new technologies last year and is keeping pace this year as it progresses towards its goals of data hygiene for AI. Discover has also taken a measured approach to generative AI, ensuring safeguards are present and training is offered to employees. The development of GenAI extends NLP’s ability to process language content by being able to create new content. “GenAI represents a transformative leap in innovation, particularly in content creation,” he said. AI may be adopted faster by digitally native, cloud-based firms, such as FinTechs and BigTechs, with agile incumbent banks following fast.

In three months, Piotr hit the ground running, taking part in 1,000 conversations over two months. So far, the virtual assistant has achieved a 90% accuracy rate for satisfying support inquiries; a figure that’s expected to rise thanks to built-in learning capabilities. Natarajan says Capital One’s approach is to first understand the various use cases the company can explore with generative AI, then determine what data it can control that goes into the models. From there, Capital One figures out what it can construct to test and learn, as well as mitigate any unforeseen outcomes. To improve productivity and the claims experience, insurers will need to scale up the most promising initiatives.

Addressing the talent issue goes beyond merely employing AI specialists to identifying the groups that will be most affected by this shift and encouraging behavioral changes. Mobilizing change agents at all levels of the organization will help establish a “sponsorship spine” to promote behaviors aligned with the bank’s AI strategy. Recurring costs constitute a greater proportion of the total outlay for simpler setups, whereas more complex deployments require substantial upfront investments that may dwarf ongoing expenditures. Irrespective of the exact use-case scenario, maintenance, including continued LLM API usage, accounts for a significant portion of the cost. Nevertheless, European banks are trying to close the gap on AI adoption, including the sort of generative AI used by ChatGPT. Data-synthesising Gen AI solutions could promise advice unencumbered by emotions or wishful thinking.

Our latest 27th Annual CEO Survey indicated that leaders expect technology including GenAI and Machine Learning (ML) to be the centre of optimising costs, creating new revenue streams and improving the customer experience within their organisations. Middle East CEOs are also optimistic about the financial impact of GenAI, with 63% expecting the adoption of it in their organisation to increase revenue, while 62% said it would increase profitability. In the GCC, enthusiasm is even higher with two thirds expecting revenue increases and a similar number expecting profitability increases. While these statistics cover various industries, the banking sector specifically has been heavily reliant on technology since its inception. Now, they see genAI emerging and are asking themselves (and the rest of the business) how this new and disruptive technology might change their world for the better.

You can foun additiona information about ai customer service and artificial intelligence and NLP. As with any new technology, the advent of GenAI brings about a natural sense of curiosity and adjustment for our employees. We are determined to bring our employees along by focusing on the synergy between human and AI capabilities to leverage GenAI as a co-pilot. AI has been used in the financial industry to detect fraud for decades – an AI model can analyze thousands or even millions of transactions at a speed humans could never match. Generative AI is starting to be used to better understand fraud schemes – for instance, JPMorgan Chase uses generative AI to detect business email compromise attacks. A report from Accenture Research found that capital-markets roles are ripe for AI-related job displacement.

By analyzing large volumes of data and detecting patterns, anomalies, and correlations, fraud prevention officers can effectively identify fraudulent activities that may go unnoticed by manual methods. While Hong Kong currently lacks laws or regulations addressing GenAI, the city’s regulators have been trying to keep up with booming adoption of the technology by issuing non-binding guidelines. Using an AI chatbot to respond to employee searches for content in policies or about bank product offerings.