Financial institutions have been facing tough challenges between economic uncertainty and an unprecedented technology-powered speed of change, especially since the Spring Bank Run of 2023.

Banks are now prioritizing four key areas: liquidity management with a balanced portfolio view including commercial real estate (CRE), enterprise protection with anti-fraud
and cybersecurity, operational resiliency and sustainability with climate risk and green products. Overall, balanced risk management is the ultimate goal for banks.

Addressing these risks will make institutions more resilient, deliver services efficiently and build stickiness with customers. But this requires, artificial intelligence (AI) to connect the dots from the decades of accumulated data and reimagine business processes. By becoming an AI-first financial institution, they can navigate challenges and become a trusted orchestrator of the economy by exploring innovative business models with open finance across the ecosystem.

Advancements in AI and generative AI have significantly influenced institutions like JP Morgan that leverage these technologies to enhance their digital, data and cloud infrastructure. Generative AI finds diverse applications, from streamlining software development and managing adverse media (Deutsche Bank) to analyzing Federal Reserve speeches and detecting fraud (JPMorgan), and even offering personalized financial advice and recommendations (Morgan Stanley).

Almost 25% of American financial institutions already use business value generating solutions, with generative AI spending up 67% from 2023 to 2024. While only some institutions have made early investments, going AI-first is an imperative for growth and efficiency. It also has a direct bearing on all connected stakeholders – enabling faster, smarter customer decisions, amplifying employee potential, and distributing higher capital to low-risk shareholders.

But, what does an AI-first financial institution look like?

An AI-first financial institutions fully leverages data and AI to automate tasks, streamline workflows, enhance products and services, and differentiate against peers with utmost efficiency and ethical decision-making. A value-based approach can leverage the existing power of digital and cloud to evolve quickly with complete transparency and auditability. This is especially important with changing expectations of the stakeholders, such as customers, regulator, shareholders, and broader communities.

An AI-first strategy focuses on three key layers – foundation, core, and growth.

1. AI-first foundation: An AI-first institution excels in handling and interpreting data volumes. Laying the foundation becomes critical. This includes modernizing technology, infrastructure, and AI operations; managing talent and change; and making the enterprise data-ready for AI.

Yet, executives say their primary challenge is unusable data. Institutions must first establish an effective data estate, ensuring data assets are available, accessible, discoverable, and of high quality.

Equipped with customer data, institutions use AI to develop a 360-degree customer view to understand their needs and preferences, and shape business strategies that in- turn provide insights on customers. Data availability for downstream processes instantly leads to quicker user interactions, faster decision-making, and predictive policies.

2. AI-first core: It supports back and middle office operations, including credit scoring, regulatory compliance, customer service, and fraud detection.
AI integration across operations drives efficiency and ‘autonomous automation’ — from complex task execution to project management. AI algorithms identify improvement areas, optimize resource allocation, and rationalize processes, enhancing operational decision-making. AI’s capability in scenario modelling provides leaders with predictive and data- driven insights for planning and decision-making.

AI offers capabilities to assess credit, market, and operational risks. AI-driven systems are adept at tracking regulatory changes, seamlessly integrating compliance into operational decisions to avoid fines. AI scrutinizes customer data to better assess creditworthiness, minimizing default risks. AI’s internal process evaluation and predictive maintenance mitigate operational risks, forecasting system issues for pre-emptive action. In the case of risks relating to CRE, AI can act on the historical and real-time structured and unstructured data to create “what if” scenarios. This can help institutions identify concentration risk earlier and determine actions for loan diversification. This strengthens financial institutions’ resiliency and security.

3. AI-first growth: It augments front-office operations by personalizing sales and marketing at scale, deepening client relationships, and improving portfolio management and product design.

Generative AI can improve the productivity of contact center representatives by answering customer queries quicker and accurately. For example, Discover Financial Services is using generative AI at its contact center to answer customer queries quickly.

AI’s ability to analyze and interpret customer data is key to offering tailored financial services and products. AI-analyzed customer feedback and market trends help create innovative financial products and continuously improve services. Morgan Stanley, for instance, is using generative AI to help its 10,000+ financial advisors answer investor queries on personalized financial advice and recommendations.

AI-based products can use synthetic customers — human- like avatars with personality and knowledge —to interact with prospects. These avatars, based on design personas, possess a story, goal, and unique relationship with the bank, leveraging factual knowledge and personality traits of the customer base (current or intended).

Synthetic customers help create tailored proposals for each customer and demonstrate value that aligns with customers’ specific needs. It helps employees learn about product features, benefits, and services, and answer customer questions accurately and confidently.

While AI can potentially re-engineer each function and business segment, institutions must consider privacy, security, and ethical implications. Responsible design principles should guide AI integration, with human oversight in high-risk use cases. This will help financial institutions achieve higher margins, create new revenue streams, design better products, and become more productive.

Lastly, talent is key. To be truly an AI-first financial institution, the culture around embracing AI and future innovations needs to be encouraged across organizations. Since this AI era demands diverse skill sets, training and adapting resources for effective collaboration with AI systems are necessary. There will be focus on conflict resolution, trust-building, and machine “unlearning”. New roles will emerge, keeping pace with AI advancements. AI fosters a culture of continuous learning and adaptability, making institutions agile and adaptable to future advances as this new era unfolds.

About Author

Bal Shukla leads AI and business transformation for the financial services segment at Infosys. He drives transformation across business sub-segments, including global banks, super-regional/national and regional banks, focusing on P&L.

As a Forbes Council Member and AI evangelist, Bal is a design thinker and futurist with a platform- based mindset. With over 20 years of experience, he drives business and IT domain-based blueprint and strategy, in line with macroeconomic and mega trends. He develops purposeful business cloud hybrid platforms, products, and services across 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.

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