Generative AI in Financial Services: Navigating Compliance, Risk, and Innovation

Generative AI is rapidly transforming the financial services landscape, offering unprecedented opportunities for innovation, efficiency, and customer engagement. Yet, for banks, insurers, and capital markets firms, the journey from proof of concept (POC) to production is uniquely complex. The sector’s stringent regulatory environment, heightened data security requirements, and risk sensitivity demand a disciplined, industry-specific approach to AI adoption. This page explores how financial institutions can safely and successfully operationalize generative AI, balancing innovation with compliance and risk management.

The Promise and Peril of Generative AI in Financial Services

Financial services organizations have long leveraged AI for operational use cases—fraud detection, risk scoring, and process automation. Generative AI, however, introduces a new paradigm: creative, conversational, and adaptive systems capable of generating text, code, and insights at scale. Early applications include:

• Fraud detection: AI models that synthesize transaction data and behavioral patterns to flag anomalies in real time.
• Customer service: Virtual assistants and chatbots that provide personalized, 24/7 support, reducing call center costs and improving satisfaction.
• Product personalization: AI-driven recommendations for financial products, tailored to individual customer profiles and life events.

The potential is vast, but so are the risks. Unlike traditional AI, generative models can hallucinate, generate biased or non-compliant outputs, and introduce new vectors for data leakage or regulatory breaches. In a sector where trust and compliance are paramount, these risks must be proactively managed.

From Experimentation to Production: Why Most POCs Stall

Many financial institutions have launched generative AI pilots, but few have scaled them to production. Common barriers include:

• Longer-than-expected timeframes: Building a robust, compliant AI ecosystem can take months or even years.
• Unexpected costs: Model licensing, infrastructure upgrades, and user adoption can drive up expenses.
• Siloed efforts and shadow IT: Decentralized experimentation leads to inefficiencies and security risks.
• Stakeholder uncertainty: Black-box models and unclear ROI make it hard to secure buy-in from risk, compliance, and business leaders.

The solution? A disciplined approach to risk management, governance, and cross-functional collaboration—supported by actionable frameworks and real-world lessons.

Navigating the Five Pillars of Generative AI Risk in Financial Services

1. Model and Technology Risk

Key Questions:

• What business problem are we solving, and what are the technical and regulatory constraints?
• How will we monitor and manage model performance, drift, and cost over time?

Best Practices:

• Evaluate model performance and cost trade-offs early and often. For example, a less expensive model may suffice for customer service, while fraud detection may require a more advanced (and costly) model.
• Design for portability and future-proofing, so you can switch models or providers as the landscape evolves.
• Monitor for rapid model updates and plan for ongoing evaluation.
• Balance accuracy, speed, and cost to meet business and compliance needs.

2. Customer Experience Risk

Key Questions:

• How do we ensure relevant, accurate, and unbiased outputs?
• How do we design intuitive, frustration-free user experiences?

Best Practices:

• Use prompt engineering to split complex requests and reduce hallucinations.
• Embed additional context and structure into prompts for more accurate responses.
• Provide pre-set options or suggestions to help users get relevant results.
• Leverage human-centered design to make tools intuitive for non-experts.
• Continuously monitor user feedback and iterate.

3. Customer Safety and Compliance Risk

Key Questions:

• What safeguards are in place to prevent the model from generating harmful, biased, or non-compliant content?
• How will we monitor and address misuse or unintended consequences?

Best Practices:

• Implement multi-layered defenses, including red teaming, prompt reviews, and constitutional AI (where the model self-assesses its outputs for safety and compliance).
• Establish clear processes for user feedback and incident response.
• Continuously evaluate and improve risk mitigation strategies.
• Document all risk assessments and mitigation steps for regulatory review.

4. Data Security and Privacy Risk

Key Questions:

• What types of data are being used, and are they subject to privacy regulations (e.g., GDPR, CCPA, sector-specific rules)?
• How will we protect sensitive or personal data?

Best Practices:

• Avoid using sensitive or personal data whenever possible, especially in training and inference.
• Implement data masking, pseudonymization, and anonymization techniques.
• Ensure transparency and obtain consent for data use.
• Use sandboxed environments for model training and inference.
• Partner with trusted technology providers for robust security.
• Regularly audit data access, usage, and sharing.

5. Legal and Regulatory Risk

Key Questions:

• Are we operating in a high-risk category (e.g., credit decisioning, AML, insurance underwriting)?
• How do we ensure compliance with evolving AI laws and regulations (e.g., EU AI Act, SEC, FINRA, FCA)?

Best Practices:

• Build cross-functional teams with legal, compliance, data, and technology expertise to monitor the regulatory landscape.
• Document your generative AI use cases, data sources, and risk assessments to demonstrate compliance.
• Establish processes for regular review and updates as laws and regulations change.
• Engage with legal counsel and industry groups to stay informed about new and emerging AI regulations.

Real-World Example: Generative AI in Transaction Banking

A leading global bank sought to unlock working capital for corporate clients by embedding generative AI into its transaction banking platform. The solution: a no-code, AI-powered dashboard that aggregates real-time balances across multiple banks and ERPs, provides proactive liquidity forecasts, and offers pre-approved working capital finance—all within a secure, compliant environment.

Risk Management in Action:

• Model and Technology: Multiple models were evaluated for accuracy, cost, and hallucination rates. Synthetic data was used to test edge cases and bias.
• Customer Experience: The dashboard was designed with guided prompts and visualizations, making it intuitive for finance teams.
• Customer Safety: AI outputs were reviewed for compliance with financial regulations and internal policies.
• Data Security: No sensitive client data was used in model training; all integrations were sandboxed and encrypted.
• Legal and Regulatory: The solution was documented and reviewed by legal and compliance teams, ensuring alignment with global banking regulations.

Building Cross-Functional Teams for Safe, Scalable AI

Generative AI success in financial services is not just a technology challenge—it’s an organizational one. The most effective programs are built on cross-functional teams that bring together strategy, product, experience, engineering, data, risk, and compliance. This approach ensures:

• Risks are identified and addressed early
• Solutions are designed with the end user and regulator in mind
• Governance and compliance are embedded from day one

Checklist for Cross-Functional AI Teams:

• Define clear roles and responsibilities
• Establish regular communication and feedback loops
• Involve risk and compliance early and often
• Empower domain experts to shape use cases and guardrails

Accelerating Time to Value: Lessons from the Field

• Start with high-value, low-risk use cases (e.g., customer service chatbots, report summarization) before tackling high-risk applications (e.g., credit decisioning).
• Invest in change management and upskilling to bridge the AI talent gap and foster a culture of responsible experimentation.
• Monitor and measure outcomes to demonstrate ROI and inform future investments.
• Plan for scalability and continuous improvement as models, regulations, and business needs evolve.

The Path Forward: Balancing Innovation and Risk

Generative AI is not a one-and-done project—it’s an ongoing journey. The most resilient financial institutions are those that:

• Foster a culture of responsible experimentation and learning
• Empower employees to understand and manage AI risks
• Continuously update governance frameworks as technology and regulations evolve

At Publicis Sapient, we’ve learned that the key to de-risking generative AI is not to eliminate risk, but to manage it intelligently—balancing innovation with safety, speed with governance, and ambition with accountability. By following these best practices, financial services leaders can move from POC to production with confidence, unlocking the full value of generative AI while protecting their customers, their data, and their brand.

Ready to accelerate your generative AI journey?
Connect with Publicis Sapient’s financial services and AI risk management experts to start building your roadmap to safe, scalable, and successful AI deployment.