Building the Bank of the Future: An AI-First Blueprint

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An AI-first organization accelerates AI adoption by avoiding traditional siloed use cases. According to Bal Shukla from Infosys, financial institutions are particularly focused on moving beyond the experimentation phase to scale both traditional and generative AI across the enterprise.

These institutions face significant challenges due to economic uncertainty and rapid technological advancements, particularly since the Spring Bank Run of 2023. In response, banks are prioritizing four key areas: liquidity management with a balanced portfolio view, including commercial real estate (CRE); enterprise protection with anti-fraud and cybersecurity measures; operational resiliency and sustainability with climate risk and green products; and overall balanced risk management.

Addressing these risks will enhance institutional resilience, improve service delivery efficiency, and increase customer loyalty. To achieve this, artificial intelligence (AI) must leverage decades of accumulated data to reimagine business processes. By becoming AI-first, financial institutions can navigate challenges and explore innovative business models within the open finance ecosystem, positioning themselves as trusted economic orchestrators.

Advancements in AI and generative AI have significantly impacted institutions like JP Morgan, which use these technologies to enhance their digital, data, and cloud infrastructure. Generative AI applications range from streamlining software development and managing adverse media (Deutsche Bank) to analyzing Federal Reserve speeches, detecting fraud (JPMorgan), and providing personalized financial advice and recommendations (Morgan Stanley).

Nearly 25% of American financial institutions already use AI solutions that generate business value, with spending on generative AI projected to increase by 67% from 2023 to 2024. While only a few institutions have made early investments, adopting an AI-first strategy is crucial for growth and efficiency. This approach benefits all stakeholders by enabling faster, smarter customer decisions, amplifying employee potential, and allocating more capital to low-risk shareholders.

An AI-first financial institution fully leverages data and AI to automate tasks, streamline workflows, enhance products and services, and differentiate itself through efficiency and ethical decision-making. A value-based approach can harness the power of digital and cloud technologies to evolve quickly with complete transparency and auditability, meeting the changing expectations of customers, regulators, shareholders, and broader communities.

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

An AI-first institution excels in handling and interpreting large volumes of data. Building this foundation is critical and involves modernizing technology infrastructure, managing talent and change, and preparing enterprise data for AI. Executives often cite unusable data as a primary challenge, so establishing an effective data estate is essential. This ensures that data assets are available, accessible, discoverable, and of high quality.

By leveraging customer data, institutions can develop a comprehensive 360-degree view of customers, understanding their needs and preferences to shape business strategies. This data availability facilitates quicker user interactions, faster decision-making, and the creation of predictive policies.

The core supports back and middle office operations, including credit scoring, regulatory compliance, customer service, and fraud detection. Integrating AI across these operations drives efficiency and enables ‘autonomous automation,’ from complex task execution to project management. AI algorithms can identify areas for improvement, optimize resource allocation, and streamline processes, enhancing operational decision-making.

AI systems assess credit, market, and operational risks, track regulatory changes, and integrate compliance into operational decisions to avoid fines. They analyze customer data to better assess creditworthiness, reducing default risks. AI also evaluates internal processes and predictive maintenance, forecasting system issues for preemptive action. For CRE risks, AI can analyze historical and real-time data to create “what if” scenarios, helping institutions identify concentration risks and determine actions for loan diversification, thereby strengthening financial resilience and security.

This layer augments front-office operations by personalizing sales and marketing at scale, deepening client relationships, and improving portfolio management and product design. Generative AI enhances the productivity of contact center representatives by answering customer queries quickly and accurately. For example, Discover Financial Services uses generative AI in its contact center for this purpose.

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 uses generative AI to assist over 10,000 financial advisors in providing personalized financial advice and recommendations.

AI-based products can employ synthetic customers—human-like avatars with personalities and knowledge—to interact with prospects. These avatars, based on design personas, have unique relationships with the bank and leverage factual knowledge and customer personality traits. Synthetic customers help create tailored proposals and demonstrate value aligned with specific customer needs, aiding employees in accurately answering customer questions.

While AI has the potential to re-engineer every 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 approach will help financial institutions achieve higher margins, create new revenue streams, design better products, and become more productive.

Lastly, talent is crucial. Embracing AI and future innovations requires a culture that encourages continuous learning and adaptability. This new AI era demands diverse skill sets, so training and adapting resources for effective collaboration with AI systems are necessary. There will be a focus on conflict resolution, trust-building, and machine “unlearning.” New roles will emerge to keep pace with AI advancements, fostering a culture of continuous learning and adaptability, making institutions agile and prepared for future developments.