The AI-First organization accelerates AI adoption by avoiding traditional siled use cases. Financial institutions are particularly looking to move beyond experimentation and scale both traditional and generative AI in the enterprise, according to Infosys’ Bala Shukla.
Financial institutions have faced significant challenges between economic uncertainty and the unprecedented pace of technology-driven change, especially following the spring 2023 bank run.
Currently, banks give priority to four key areas: liquidity management, taking into account a balanced portfolio, including commercial real estate (CRE), enterprise fraud protection
and cybersecurity, operational resilience, and sustainability with climate risks and green products. In general, balanced risk management is the ultimate goal of banks.
Addressing these risks will make institutions more resilient, deliver services more efficiently, and strengthen customer loyalty. But this requires artificial intelligence (AI) to connect the dots of decades of accumulated data and reimagine business processes. By becoming an AI-driven financial institution, they will be able to solve problems and become a trusted enabler of the economy by exploring innovative open finance business models across the ecosystem.
Advances in AI and generative AI have significantly impacted institutions such as JP Morgan that use these technologies to improve their digital, information and cloud infrastructure. Generative AI has applications ranging from optimizing software development to managing hostile environments (Deutsche Bank) analyze the speeches of the Federal Reserve and fraud detection (JPMorgan) and even offers personalized financial advice and guidance (Morgan Stanley).
Almost 25% of US financial institutions already use solutions that create business valueWith spending on generative artificial intelligence grew by 67% from 2023 to 2024. While few institutions have made early investments, adopting AI first is imperative for growth and efficiency. It also has a direct impact on all stakeholders, enabling faster and smarter customer decisions, empowering employees and distributing more capital to low-risk shareholders.
But what does an AI-focused financial institution look like?
AI-focused financial institutions take full advantage of data and AI to automate tasks, streamline workflows, improve products and services, and differentiate themselves from peers with maximum efficiency and ethical decision-making. A value-based approach can leverage existing digital and cloud capabilities to drive rapid growth with full transparency and auditability. This is particularly important given the changing expectations of stakeholders such as customers, regulators, shareholders and wider communities.
The AI First strategy focuses on three key levels: foundation, basicAnd height.
1. AI-first fund: An AI-focused institution excels at processing and interpreting large volumes of data. Laying the foundation becomes critical. This includes modernizing AI technology, infrastructure and operations; talent and change management; and preparing enterprise data for AI.
Still, executives say their top priority is unusable data. Institutions must first create an effective data warehouse, ensuring that data is available, accessible, affordable, and of high quality.
With customer data in hand, institutions are using AI to create a holistic view of customers to understand their needs and preferences, and shape business strategies, which in turn provide customer insights. Making data available to downstream processes instantly results in faster user interactions, faster decision making, and policy prediction.
2. AI-first core: It supports back and middle office operations including credit scoring, regulatory compliance, customer service and fraud detection.
Integrating AI into all operations increases efficiency and “autonomous automation,” from complex tasks to project management. AI algorithms identify areas for improvement, optimize resource allocation, and streamline processes to improve operational decision making. AI’s scenario modeling capabilities provide leaders with predictive and data-driven information for planning and decision-making.
AI offers capabilities to assess credit, market and operational risks. AI-powered systems can monitor changes in regulations, easily integrating compliance into operational decisions to avoid fines. AI carefully studies customer data to better assess creditworthiness and minimize the risk of default. Internal process assessment and AI predictive maintenance reduce operational risk by anticipating system issues for proactive action. For CRE risks, AI can act on historical structured and unstructured data in real time to create “what-if” scenarios. This can help institutions identify concentration risk earlier and determine credit diversification actions. This improves the stability and security of financial institutions.
3. AI-powered growth: It expands front office operations by personalizing sales and marketing at scale, deepening customer relationships, and improving portfolio management and product design.
Generative AI can improve the productivity of contact center representatives by responding to customer queries faster and more accurately. For example, Discover financial services uses generative artificial intelligence in its contact center to quickly respond to customer inquiries.
The ability of artificial intelligence to analyze and interpret customer data is key to providing personalized financial services and products. Customer feedback and market trends, analyzed using artificial intelligence, help create innovative financial products and continuously improve services. Morgan Stanleyfor example, uses generative artificial intelligence to help its more than 10,000 financial advisors respond to investor requests for personalized financial advice and recommendations.
AI-based products can use synthetic clients—humanoid avatars with personality and knowledge—to interact with potential customers. These designer-based avatars have a story, a purpose and a unique relationship with the bank, leveraging the factual knowledge and personality of the customer base (current or prospective).
Synthetic clients help create customized offers for each client and demonstrate value that meets specific client needs. This helps employees learn about product features, benefits, and services, and answer customer questions accurately and confidently.
While AI has the potential to redesign every function and business segment, institutions need to consider the privacy, security and ethical implications. Principles of Responsible Design should guide the integration of artificial intelligence with human oversight in high-risk use cases. This will help financial institutions achieve higher profits, create new revenue streams, develop better products and become more productive.
Finally, talent is critical. To become a truly AI-first financial institution, a culture of AI adoption and future innovation must be fostered across all organizations. Since the era of artificial intelligence requires diverse skill sets, training and adaptation of resources are necessary to effectively collaborate with artificial intelligence systems. The focus will be on resolving conflicts, building trust and unlearning machines. New roles will emerge to keep pace with AI advances. AI promotes a culture of continuous learning and adaptability, making institutions flexible and adaptable to future advancements as the new era dawns.
about the author
Head of Artificial Intelligence and Business Transformation, Financial Services, Infosys
Shukla Ball leads AI and business transformation in the financial services segment at Infosys. He drives transformation across all sub-segments of the business, including global banks, super regional/national and regional banks, with a focus on P&L.
As a Forbes board member and AI evangelist, Bel is a design thinker and platform futurist. With over 20 years of experience, he develops business and IT-based plans and strategies in line with macroeconomic and megatrends. It develops focused business cloud hybrid platforms, products and services for retail and commercial banking, risk management, payments, personalization and digital products. He is recognized for developing data-driven business platforms and ecosystem partnerships with competitive advantages through cloud technologies, advanced analytics, fintech alliances and a collaborative culture.
