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The ROI of AI in Financial Services: Navigating Value Creation by Nathan Stevenson, CEO, ForwardLane

On a crisp morning in October 2023, the executive team of a major European bank gathered to review their €50 million AI investment portfolio. Despite considerable spending on everything from chatbots to risk models, they struggled to answer a seemingly simple question: What returns were they actually generating? This scenario plays out in boardrooms worldwide as financial institutions grapple with measuring and maximizing their AI investments.

The stakes could not be higher. With McKinsey estimating potential global economic value of $13 trillion by 2030 from AI, financial institutions are pouring unprecedented resources into AI initiatives. Yet many struggle to move beyond simplistic ROI calculations to truly understand and optimize their returns.

The Evolution of AI Value Measurement

Consider the experience of Morgan Stanley, which implemented an AI-driven wealth management advisory system in 2022. Initial ROI calculations focused solely on cost reduction through automated portfolio management. However, the most significant value emerged unexpectedly through improved client retention and increased share of wallet – benefits that traditional ROI metrics would have missed entirely.

“Traditional ROI calculations fail to capture AI’s multifaceted impact” explains Erik Brynjolfsson. “Organizations need frameworks that capture both immediate financial returns and longer-term strategic value creation”. This insight aligns with the ROI Institute‘s comprehensive methodology, which demonstrates how value creation occurs across multiple levels, from initial reactions to long-term business impact.

Understanding the Value Chain

The journey from AI implementation to value creation follows a natural progression that successful institutions have learned to track and optimize. Take the case of DBS Bank in Singapore, which transformed its customer service operations through AI. Their success came from carefully monitoring and optimizing each stage of value creation.

The process begins with stakeholder reaction – how employees, customers, and partners initially respond to AI systems. When Australia’s Commonwealth Bank launched its AI-powered customer service platform, they discovered that early user reactions predicted long-term adoption rates with surprising accuracy. Negative initial responses from front-line staff signaled implementation challenges that, if addressed early, could be transformed into opportunities for improvement.

Learning and capability building form the next crucial link. Zhang’s comprehensive study of AI in banking demonstrates that successful institutions invest heavily in developing both technical and business capabilities. JPMorgan Chase, for example, committed to training 30,000 employees in AI fundamentals, recognizing that technical implementation alone would not drive value.

The Application Challenge

The critical transition from learning to application separates high-performing AI implementations from the rest. Goldman Sachs’ experience with AI-powered trading algorithms illustrates this challenge. Despite sophisticated technology and well-trained teams, initial returns disappointed until they focused explicitly on measuring and optimizing how traders actually used the AI tools in their daily work.

The ROI Institute’s ROI Methodology® provides specific frameworks for measuring this crucial transition. “The gap between knowing and doing represents one of the biggest risks to AI ROI”, notes Patricia Anderson. “Organizations need systematic approaches to bridge this gap, ensuring that learning translates into changed behaviors and improved processes.”

Converting Impact to Financial Value

The path from application to business impact often takes unexpected turns. Consider UBS’s implementation of AI for wealth management. While designed primarily for portfolio optimization, the system’s greatest value emerged from its ability to identify and prevent potential client departures, saving billions in assets under management.

This complexity makes converting impact to financial value challenging but crucial. The ROI Methodology) provides frameworks for monetizing benefits across multiple dimensions:

• Direct cost savings and revenue increases
• Operational efficiency gains
• Strategic value creation
• Risk mitigation benefits

HSBC’s experience implementing AI for fraud detection illustrates the importance of comprehensive value measurement. Beyond direct fraud prevention savings of $100 million annually, the system generated substantial value through improved customer trust, reduced operational risk, and enhanced regulatory compliance.

Implementation Success Factors

Even with clear frameworks for measurement, implementing AI successfully requires careful orchestration. The MIT Sloan AI Success Study(4) identifies organizational alignment as the critical factor – ensuring technology, processes, and people work in concert toward clear objectives.

Bank of America’s success with its AI-powered virtual assistant, Erica, demonstrates the importance of this alignment. By establishing clear success metrics and engaging stakeholders across the organization, they achieved both impressive adoption rates and measurable business impact.

The Ethical Dimension

The relationship between ethical AI implementation and financial returns grows increasingly clear. When a major European bank faced backlash over biased lending algorithms, the incident highlighted how ethical considerations directly impact ROI. Organizations that proactively address fairness, transparency, and privacy typically achieve higher sustained returns than those focused solely on technical performance.

Looking Ahead: The Generative AI Era

As we enter the generative AI era, measuring and maximizing ROI becomes even more critical. Early experiments with generative AI in financial services suggest its impact will be even more transformative – and harder to measure – than previous AI applications. Some institutions are already exploring new frameworks for capturing value from generative AI applications in areas like product development, risk assessment, and client communication.

Conclusion

Success in measuring and maximizing AI ROI requires striking a careful balance between rigorous measurement and practical implementation. Organizations must move beyond simplistic calculations while avoiding overly complex measurement systems that consume more value than they track.

The Institute for Innovation Development is an educational and business development catalyst for growth-oriented financial advisors and financial services firms determined to lead their businesses in an operating environment of accelerating business and cultural change. We operate as a business innovation platform and educational resource with FinTech and financial services firm members to openly share their unique perspectives and activities. The goal is to build awareness and stimulate open thought leadership discussions on new or evolving industry approaches and thinking to facilitate next-generation growth, differentiation, and unique client/community engagement strategies. The institute was launched with the support and foresight of our founding sponsors — Ultimus Fund Solutions, NASDAQ, FLX Networks, TIFIN, Advisorpedia, Pershing, Fidelity, Voya Financial, and Charter Financial Publishing.