AI and Data Transformation in Banking

Damilola Ojo Senior Managing Director
Connie Chan Senior Managing Director
Fred Liebler Principal
Vladimir Burtaev Managing Director

Executive Summary

Artificial intelligence (“AI”) and data are reshaping the financial industry, yet many banks struggle to turn potential into game-changing impact. The gap between innovation and execution is massive, held back by data hurdles, tech immaturity, talent shortages and siloed operations that stifle efficiency. But for those who crack the code, the rewards are transformative.

By embedding AI into critical banking functions like fraud detection and loan management, institutions can supercharge efficiency, hyper-personalize customer experiences and unlock new revenue streams. But true success isn’t just about adopting cutting-edge tech; it requires strong data governance, strategic execution and an AI-driven culture. A structured, value-first approach ensures AI investments align with business goals, regulatory requirements and market shifts — while avoiding costly tech overspending.

Banks that master AI won’t just keep up — they’ll lead, gaining a sharp, competitive edge in an increasingly digital and regulated world. The time to act is now.

FIGURE 1: Illustrative value at stake for successful AI and data programs

A Common Situation

Globally, financial institutions are facing unprecedented challenges, including increasing fraud and default rates, shrinking profit margins and tightening regulatory pressures. Rapid digitalization has also heightened customer expectations, pushing banks to offer seamless, personalized experiences while maintaining compliance with evolving regulations. At the same time, the competitive landscape is being reshaped by agile fintech startups leveraging cutting-edge technology to capture market share. In response, banks are turning to AI and data-driven solutions to enhance risk management, streamline operations and unlock new revenue streams. These organizations face several common challenges that hinder progress:

• Limited and Slow Value Extraction: Many banks struggle to generate meaningful impact from their AI initiatives in a timely manner, leaving significant potential untapped.

• Blurred Accountability: AI spans the entire banking value chain, often leading to unclear ownership — e.g., in Shared Services, where overlapping front- and back-office functions can result in inefficiencies and duplication.

• Data Quality Challenges: Fragmented, outdated or inconsistent data weakens AI-driven insights and hinders effective implementation.

• Legacy Systems and Technical Debt: Outdated infrastructure and entrenched legacy technology slow down AI adoption and digital transformation.

• Talent Gaps: A shortage of AI and data specialists creates execution roadblocks, limiting banks’ ability to scale AI-driven innovation.

Unlocking AI’s full potential in banking requires a structured, scalable approach that aligns executive vision with operational execution. A successful strategy combines a top-down perspective, ensuring leadership aspirations and long-term market ambitions are clearly defined, with a bottom-up approach that addresses functional and operational realities. By embedding AI-driven insights and strong data governance into core processes, banks can navigate challenges like fragmented data, legacy systems and talent gaps. However, success hinges on a proven business case and tangible action plan that reflect the organization’s readiness — ensuring compliance, enhancing efficiency and driving sustainable growth in an increasingly digital financial landscape.

Our Approach to Unlock AI’s Full Potential

With a track record of working with over 300 financial institutions across all major markets, we have helped financial institutions navigate complex challenges and drive measurable impact across the entire banking value chain. Through years of hands-on experience, we have developed a tailored, battle-tested approach to designing and executing AI and Data Transformation programs that don’t just generate insights, but deliver real business value. Our method is built on deep industry expertise, a clear understanding of market dynamics and a structured roadmap that balances strategic vision with operational execution capabilities.

In the following steps, we outline our proven approach — one that systematically tackles common challenges, ensures seamless implementation and unlocks AI’s full potential to future-proof banking operations.

1. Define a Bold Yet Pragmatic AI Vision

AI success starts at the top. We partner with executives to define a bold, business-aligned vision that strikes the right balance between innovation and tangible impact. Our deep industry expertise, combined with proven market insights and AI-specific strategic frameworks, ensures that this vision is both ambitious and executable. In a recent engagement with a leading bank, we conducted in-depth interviews with more than 30 senior executives and benchmarked against more than 50 regional banks to craft a data-driven, market-tested AI strategy — setting the foundation for long-term success.

2. Pinpoint the Highest-Value Opportunities

Not all AI use cases are created equal. We focus on identifying the most impactful and feasible AI opportunities — whether in automated cross-selling, fraud detection, risk management or hyper-personalized customer experiences — ensuring maximum business value. Our approach prioritizes high-value, high-execution potential initiatives that align with strategic goals and operational realities. For instance, in a recent engagement with a mid-sized bank, we streamlined more than 800 potential use cases and more than 2,000 reports, ultimately selecting and prioritizing 30 high-impact AI initiatives. These ranged from enhanced data accessibility to advanced customer value management and CX-driven innovations, creating a clear roadmap for transformation.

3. Build a Business Case That Delivers

AI must deliver measurable impact. We help financial institutions prioritize and execute AI initiatives that strike the right balance between quick wins and long-term transformation, ensuring every investment is directly tied to business value. Our approach is both rigorous and results-driven, combining bottom-up financial modeling with top-down industry validation to create AI roadmaps that are both ambitious and financially sound. For a leading regional bank, we developed a five-year business case featuring about 100 high-impact AI use cases and more than 200 cost line items, meticulously estimated from the ground up. These were validated through Proof of Concept (“PoC”) pilots and RFPs, and benchmarked against industry leaders, ensuring achievable results while keeping costs under control.

4. Create Scalable AI Foundations

Strong governance, modern infrastructure and skilled talent are non-negotiable for AI success. We guide banks in building scalable frameworks and selecting the right technologies and infrastructure that enable sustainable growth in the long run while keeping costs optimized. Equally important is the operating model — a critical foundation for seamless AI integration and execution. For a recent client, we developed a comprehensive AI Operating Model, which included an organizational design for more than 100 Full-Time Equivalents (“FTEs”), blending centralized functions with specialized units across business divisions. This ensured a robust, adaptable structure to drive AI success across the enterprise.

5. Turn Strategy into Action

A clear roadmap transforms AI ambition into tangible results. We craft structured, actionable implementation plans that are fully aligned with business objectives, supported by comprehensive change management strategies that drive data literacy, cultural adoption and sustainable, long-term success. By working closely with clients, we develop “Initiative Cards” that define clear ownership, outline dependencies, specify prerequisites and set KPIs and milestones to measure success. This structured approach ensures that strategy is not just a vision, but a reality — driving focused execution and delivering impactful results at every step.

Delivering Measurable Impact

We have applied this structured approach across various banks, successfully helping financial institutions achieve:

• 10%-25% productivity improvements across key banking processes by streamlining operations, reducing manual interventions and automating high-volume tasks with AI-driven solutions.

• 4%-12% revenue uplift through enhanced customer segmentation, data-driven pricing strategies and personalized product recommendations that improve cross-selling and retention.

• 300+ basis points increase in return on equity (ROE) through improved top and bottom line and by optimizing capital allocation, reducing risk exposure through predictive analytics and improving efficiency in asset and liability management.

• 30%-70% reduction in fraudulent activities through improved risk and compliance control by applying more efficient, AI-driven credit risk assessments and automatized early warning systems for other risks.

Key Learnings

To future-proof banking capabilities, you need a bold yet pragmatic vision. What these successful visions have in common are multiple key principles that drive AI and data transformation success, including:

1. Executive sponsorship and governance: A strong business sponsor, supported by a cross-functional steering committee, is essential for swift decision-making and sustained momentum.

2. Strategic leverage of existing assets: Building on prior efforts enhances efficiency, accelerates delivery and fosters stronger stakeholder engagement.

3. Prioritizing foundational capabilities: A robust, centralized data infrastructure is non-negotiable — organizations must resist the temptation to prioritize high-visibility AI use cases at the expense of core enablers.

4. Proactive alignment with Technology leadership: Clear agreements on trade-offs and dependencies in the Tech and Data roadmap are critical to ensuring seamless execution.

5. ROI-driven investment narrative: A compelling, well-defined value case is crucial to securing the necessary funding and sustaining executive buy-in.

6. Dedicated transformation and change leadership: A well-resourced change management function is a key enabler for successful implementation, ensuring adoption and long-term impact.

By integrating these learnings into their transformation journeys, financial institutions can overcome barriers, unlock the potential of AI and data, and position themselves for sustained growth and innovation. At FTI Delta, we remain committed to guiding organizations toward a data-driven future leveraging the full potential of AI.

The views expressed herein are those of the author(s) and not necessarily the views of FTI Consulting, Inc., its management, its subsidiaries, its affiliates or its other professionals.

FTI Consulting, Inc., including its subsidiaries and affiliates, is a consulting firm and is not a certified public accounting firm or a law firm.