2025 marks a turning point for financial services firms as they move beyond testing AI pilots to real-world implementation. Leaders are no longer debating if generative AI should be adopted but how to do so effectively while mitigating risk.

Financial services operate under stringent regulatory scrutiny. This makes security a top priority—especially regarding customer data, transaction records, and proprietary financial insights. Private deployments—whether on-premises or in a virtual private cloud (VPC)—are becoming the preferred choice to ensure compliance while maximizing AI’s potential.

Even after going private, transitioning from AI pilots to production—and capturing ROI—is rarely a straight path. But we've got you. We've uncovered key approaches to make it easier and faster.

Quick wins: The power of internal first steps

The most successful financial firms start with high-impact internal use cases that deliver immediate efficiency gains. These applications not only prove GenAI’s value but also build trust among employees and stakeholders. Many begin by using GenAI to search through large amounts of data in various formats, summarize it, and answer questions in natural language.

For example, insurance companies with high call center turnover can use GenAI to equip customer service representatives with instant access to policy information. Instead of spending 20 minutes searching for answers on credit card travel insurance, an AI-powered system can retrieve key details in seconds, improving response times and reducing costs and putting the customer’s coverage limits, eligible benefits, exclusions, and other key details at the agent’s fingertips. It could go on to support the rep with guidance on filing claims or more specific coverage details depending on the customer’s upcoming travel plans.

By focusing on such practical applications, financial firms establish a strong foundation for AI adoption before tackling more ambitious customer-facing solutions.

Competitive edge: Using GenAI to differentiate while managing risk

As GenAI adoption accelerates, financial firms that successfully integrate AI into their operations will gain a unique competitive advantage. However, differentiation requires more than just implementing AI—it’s about leveraging proprietary data and workflows to build specialized and customized applications.

Consider fraud detection, a prime example of GenAI’s potential to drive value. Fraud costs credit card firms and insurance companies hundreds of billions of dollars annually. Traditional machine-learning models already play a role in detecting fraud, but GenAI enhances this process by identifying complex patterns earlier and reducing false positives. Large language models (LLMs) can now be customized by specifically training them on non-fraudulent and fraudulent activity, annotating and flagging each one accordingly so the model becomes highly proficient and accurate at flagging activity that requires review.

Mastercard, for example, has doubled its detection rate of compromised cards while reducing its false positive rate by 200%. Insurers, too, can tailor AI models to regional fraud trends, strengthening risk mitigation strategies.

The intensely competitive nature of the industry and the price sensitivity of most of its products mean that many financial institutions are looking to customize their models to give them a unique edge over their rivals.

Planning for scale from day one

The firms that thrive in the AI-driven financial landscape are those that plan for enterprise-wide adoption from the outset.

This means thinking about:

Infrastructure investments: Ultra-low latency networks and high-performance computing for applications like algorithmic trading.
Data governance: Ensuring validation and storage requirements for sensitive data such as mortgage applications, or the establishment of separate internal networks for the distribution of sensitive financial information (e.g., credit scores and income verification).
Employee training and change management: Upskilling teams to use AI effectively while maintaining high service standards.
Regulatory compliance and risk management: Establishing clear requirements and systems to safeguard proprietary data and ensure regulatory compliance.

The most successful organizations we work with plan for scale from day one, even if they're starting small.

The path forward

The financial sector is poised for an AI-driven transformation, but success won’t be determined by who adopts GenAI the fastest. Instead, it will belong to the firms that implement AI strategically—balancing innovation with compliance, proving value through internal efficiencies, and planning for scale from the start.

The organizations that succeed won't necessarily be the ones with the biggest AI budgets or the most ambitious projects. They'll be the ones that plan deliberately, measure carefully, and scale intelligently.

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De-risking AI in financial services: From pilots to profit