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Council Post: Evolving Spectrum of Gen AI in Banking Sector

As highlighted by Jamie Dimon, CEO of JPMorgan Chase, in his recent annual letter, a pivotal application of generative AI at his bank is in boosting employee productivity, including in areas such as software engineering.

Banking as an industry has been one of the early adopters of Traditional AI in applications like Credit Score calculations and Risk assessment, Fraud detection, Customer Behavior Prediction, Algorithmic Trading etc. Generative AI is reshaping the Banking landscape in major ways, but the use cases are quite varied depending on the type and size of the Bank.

Transformative Impact of Generative AI in Bigger Banks

Bigger Banks are Leveraging their vast amount of customer data primarily to improve productivity and customer service.

As highlighted by Jamie Dimon, CEO of JPMorgan Chase, in his recent annual letter, a pivotal application of generative AI at his bank is in boosting employee productivity, including in areas such as software engineering. An Accenture forecast predicts that by 2028, the banking industry will see a 30% increase in employee productivity due to AI integration. 

Notable implementations in this domain include:

Streamlining Report Generation: OCBC Bank has implemented a generative AI chatbot for its 30,000 employees to facilitate tasks that traditionally consume considerable time, such as composing investment research reports and formulating customer responses. This initiative has led to a reported 50% gain in productivity during its trial phase.

Automating Interaction Summaries: Morgan Stanley has introduced ‘Debrief,’ a tool that automatically generates summaries of business interactions and phone conversations, simplifying documentation and follow-up processes.

Enhancing Regulatory Compliance Reporting:Generative AI is revolutionizing the way banks handle regulatory compliance by enabling real-time monitoring and reporting. This innovative technology efficiently condenses lengthy and complex regulatory documents, such as the continuously evolving Basel III guidelines, into concise, actionable summaries. This capability significantly reduces the need for extensive manual reviews traditionally performed by compliance teams. For example, Citibank employs generative AI to distill the voluminous 1089-page US Capital Regulations into digestible formats. This allows its risk and compliance teams to effectively understand and implement these regulations across various jurisdictions, ensuring timely compliance and facilitating necessary adaptations in bank operations.

These advancements signify a shift towards more efficient and agile operational frameworks within the banking sector, driven by generative AI.

Enhancing Customer Service with Generative AI

Generative AI is significantly enhancing customer service within the banking industry through two primary methods:

Conversational Banking:Advanced chatbots are transforming customer interactions by handling routine inquiries such as checking account balances, reviewing transaction histories, and managing transactions. These AI-powered chatbots provide autonomous, human-like responses around the clock, which not only improves customer experience but also frees up human agents to focus on more complex issues, such as fraud detection and prevention. For example, Wells Fargo’s virtual assistant, Fargo, adeptly manages a majority of everyday banking queries, ensuring efficiency and responsiveness.

Empowering Front Office Executives: Generative AI is also proving invaluable in empowering front office executives with deeper and faster insights into their customers’ profiles. At Morgan Stanley, an AI assistant equips 16,000 financial advisors with the tools to quickly access and synthesize personalized responses based on an extensive array of data, including customer data, transaction histories, investment portfolios, and financial goals, alongside a vast database of over 100,000 research reports. This advanced support not only enhances the advisors’ capacity to provide informed and strategic advice but also ensures that the guidance is highly tailored and objective, especially in complex investment scenarios.

Transformative Impact of Generative AI in Smaller Banks

Smaller banks, including community banks, are harnessing the power of generative AI to fundamentally redefine their operating models and expand customer engagement.

Innovations in Service Models: According to Christopher Naghibi, CEO of First Foundation Bank, generative AI is paving the way for an increased array of self-service options for routine banking inquiries and tasks. This technological advancement is set to drastically reduce the long queues at teller stations, transforming small bank branches into more efficient spaces dominated by interactive screens. This shift not only enhances customer experience but also significantly reduces the physical footprint and operating costs of the banks.

Enhanced Digital Interactions: Many traditional in-bank services are transitioning to  smart applications like Agent IQ. These platforms are designed to initially resolve customer queries through AI-driven interactions. If a customer’s needs exceed the capabilities of the AI, the system seamlessly escalates the issue by connecting the customer with a human banker, who then becomes the primary point of contact. This integration of AI and human expertise facilitates a personalized banking experience in the mass market, achieving greater customer satisfaction without the burden of increased costs.

Smaller Bankers can therefore get freed up from routine tasks and focus more on the personal relationship building with the local communities, which would be so much valuable for the first time homebuyer to get a mortgage or the small local business finance its operations.

Conclusion

While the initial deployments of generative AI in banking have been promising, caution is necessary due to several significant considerations. Ensuring robust cybersecurity and keeping pace with evolving AI regulations are critical to safeguard customer data and meet compliance standards. Generative AI poses ethical challenges, such as lack of interpretability and the potential for generating misleading information, which could magnify issues seen with traditional AI. 

Thus, embedding a human-in-the-loop in decision-making processes is essential to mitigate these risks. Additionally, with many generative AI applications focusing on customer service, banks must prioritize generating and maintaining high-quality, comprehensive data. Failing to do so could lead to increased customer dissatisfaction, undermining the benefits of AI in enhancing customer interactions. Overall, the integration of generative AI into banking must be managed carefully to ensure it adds value while adhering to ethical and regulatory standards.

Picture of Biswanath Banik
Biswanath Banik
Biswanath is an innovative Data and AI leader with 16 years of experience in shaping and delivering transformative business impact across Banking, Insurance, FinTech and Healthcare sectors in APAC and North America. A former Management Consultant with PwC USA and Oliver Wyman Singapore, currently Biswanath heads the Data Science team at Kredivo Group, a FinTech unicorn in SEA. His work revolve at the intersection of Data, Strategy, Product, and Customer Experience. He has proven track record in application of Data Science with consumer facing companies on Transformation, Value Creation and Growth topics. Biswanath holds the prestigious MS in Analytics from Northwestern University USA. He has lived in 3 different countries and worked in 10+ countries so far. Currently he is based in Singapore (PEP holder)
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