Gen AI Expected to Bring Big Changes to Banking Sector

While banks have primarily focused on enhancing productivity in their initial generative AI pilots, the technology holds the potential to significantly change job functions and customer interactions and pave the way for creating entirely new business models, according to McKinsey Global Institute.

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Banks have historically used AI applications derive data-driven insights and foster agile decision-making. A McKinsey report found that the emergence of generative AI will significantly accelerate the maturity and overall impact of AI in banking. McKinsey predicts substantial opportunities in the banking sector with an annual potential “value” gain ranging from $200 billion to $340 billion, equivalent to a 9% to 15% improvement in operating profit. McKinsey says this economic impact is expected to benefit all banking segments and functions, with the corporate and retail sectors expected to experience the most substantial gains, reaching $56 billion and $54 billion, respectively.

Fraud and Productivity Applications

Analysts expect three major areas to benefit from generative AI: customer service, risk management and operational efficiency. AI will also help banks offer innovative financial solutions and advisory services, improve financial data analysis and reporting, and enhance collaboration and learning, McKinsey predicted.

In customer service, generative AI is changing the dynamics of client interaction for financial institutions. Through automated, personalized responses and enhanced advisory services, this technology is redefining the customer experience. Banks are increasingly adopting LLMs, including OpenAI’s ChatGPT, to enrich customer engagement.

In terms of risk management, gen AI is improving fraud detection and risk assessment through its advanced analytics capabilities.

Moreover, gen AI has a favorable impact on operational efficiency. This technology is accelerating claims processing in the insurance sector and streamlining loan approvals in banking. Accenture’s research indicates that LLMs could impact up to 90% of working hours within the banking industry, potentially leading to a 30% increase in employee productivity by 2028. Gen AI is especially effective in automating compliance and underwriting processes, and it plays a pivotal role in these operational advancements.

Gen AI in Action

Beyond its theoretical potential, there are several examples to demonstrate how gen AI is being applied through real-world use cases across multiple functions.

• Commonwealth Bank Australia (CBA): CBA uses gen AI in its call centers to assist staff in navigating complex customer queries by analyzing more than 4,500 policy documents in real-time. Quick and accurate information helps them address customer queries more effectively and efficiently, enhancing overall customer experience.
• HDFC ERGO: HDFC ERGO General Insurance has established a center of excellence for gen AI in collaboration with Google Cloud. This initiative focuses on creating hyper-personalized experiences, improving processes and driving cost efficiencies.
• NASDAQ: NASDAQ is using gen AI in the areas of risk management and fraud detection for financial crime prevention and the development of AI-driven order types, including Dynamic M-ELO to enhance financial security.
• JPMorgan Chase: JPMorgan Chase filed a trademark application for IndexGPT, a ChatGPT-like LLM service for investment advice, marks a significant stride in personalized financial advisory and delivering customized financial solutions. It aims to offer customers tailored investment strategies by analyzing and selecting securities aligned to their specific needs.




• Bloomberg: BloombergGPT, a 50-billion parameter LLM, is trained on financial documents curated by the company over the last four decades. The LLM aims to enhance data analysis and reporting in finance. This tool assists in complex tasks such as sentiment analysis, entity recognition and news classification, highlighting gen AI’s prowess in handling specialized, domain-specific data.
• Morgan Stanley Wealth Management: Morgan Stanley Wealth Management has undertaken a strategic initiative with OpenAI to process and synthesize content to enhance knowledge management and collaborative learning. This initiative aids financial advisors in assimilating and processing extensive data and insights, thereby enhancing their ability to serve clients more effectively.




• European Central Bank: The European Central Bank is exploring the use of gen AI for various functions, including policy decision-making and document analysis. These use cases can be instrumental in enhancing the efficiency and accuracy of policy analysis and mitigating financial oversight.

Although generative AI is still in its infancy, these use cases shed light on the broad spectrum of applications, spanning from customer service to risk management and personalized advisory to policy analysis. As the technology matures, its incorporation will become imperative for BFSI companies seeking a competitive edge and the ability to redefine industry benchmarks in service excellence, operational efficiency and innovation.

However, adoption of gen AI in banking comes with its set of challenges, particularly cultural, ethical and operational. Businesses have to safeguard against the ongoing threat of AI hallucinations, with gen AI models producing inaccurate and/or unintended output. Understanding what causes them and how to deal with them is crucial as the industry increasingly relies on AI and its responsible deployment.