How Generative AI Impacts Your FI’s Risk Management Program

Written by Lara Miller | Jul 29, 2025 7:00:00 PM

Generative AI, or GenAI, is quickly becoming an integral part of the financial ecosystem, whether financial institutions (FIs) are using it directly or through their vendors. From streamlining daily tasks to analyzing and producing content, GenAI is a significant time and resource saver, as well as an innovative tool for improving products, services, and customer experiences.

However, these benefits don’t come without risks. From AI washing to advanced cybersecurity threats, financial institutions must tread carefully. Whether your FI is already exploring GenAI or just starting to consider its use, understanding the whole landscape — both the opportunities and the risks — is essential to ensuring your risk management program stands strong.

Table of Contents

What is generative AI?
GenAI opportunities
GenAI risk areas
GenAI risk management programs
GenAI risk management best practices

What is GenAI?

GenAI is one of the most used subsets of AI in the financial services space. Unlike traditional AI, which provides answers or outcomes based on structured data, GenAI produces outputs that resemble the data used to train it. For example, a bank or mortgage lender might create a GenAI-based customer service chatbot and train it to answer more complex questions using existing FAQs, customer data, and other internal sources.

Even if your institution hasn’t deployed GenAI (or any AI) internally, your vendors may be using it. According to The Ncontracts 2025 Third-Party Risk Management Survey, most FIs monitor their vendors’ AI usage by collecting documentation and adding usage language to contracts, both of which are strong controls to mitigate AI-related risks.  

Unlocking the potential of GenAI

AI has been a hot topic in the financial services sector for years. Following a 2025 AI-focused Executive Order and a Securities and Exchange Commission-hosted roundtable, there’s a continued emphasis on the responsible use of technology.

For financial institutions with lean teams managing dozens — or even hundreds of vendors — GenAI tools can significantly boost efficiency by automating tasks, such as document processing, report drafting, or contract reviews. For example, Ntelligent Contract Assistant helps reduce manual workloads, flag potential risks sooner, and enable faster, more informed contract decisions. Savings like these could result in $200 billion and $240 billion in annual value for the global banking sector, according to The McKinsey Global Institute.

GenAI can also strengthen risk management by supporting dynamic risk assessments, predictive analytics, and scenario modeling. On the compliance side, it helps turn regulatory changes into actionable insights, keep policies aligned with the latest guidance, and prepare exam-ready documentation.

GenAI can also be a game-changer in research, analysis, and product development. Financial institutions can use GenAI to design new products, tailor services to specific customer needs, and develop solutions for complex financial challenges. Its ability to process massive volumes of data, news, and market information enables deeper investment research and analysis. By uncovering insights and identifying opportunities that traditional methods may miss, GenAI helps FIs make faster, more informed decisions.

Watch on Demand: Managing Third-Party AI Risk: What You Need to Know Today

Emerging GenAI risk areas

While GenAI can save financial institutions time, money, and resources, it comes with significant risks.

Operational risk: GenAI isn’t flawless — hallucinated or inaccurate outputs can still occur. Without proper training, employees may unintentionally expose proprietary data in open-source AI environments or place too much trust in AI-generated content for customer communications or strategic decisions.
Third-party risk: Without proper due diligence and monitoring, you may not know how your vendors are using AI. Review contracts for AI-related responsibilities and require vendors to notify you of any AI adoption or changes.
Compliance risk: AI-generated decisions can be biased or discriminatory, and black box models lacking explainability increase compliance risk. Sharing data with vendors also raises the risk of unauthorized access or misuse, and data quality within AI models can be inconsistent.
Reputation Risk: While customers are increasingly aware of AI, they also expect transparency. FIs face risks if misinformation or inappropriate content reaches customers, if AI-generated errors undermine trust, or if third-party vendors misuse AI in ways that damage the brand.
Cybersecurity Risks: GenAI and machine learning models are growing targets for cyberattacks. Data poisoning, where threat actors corrupt training data to compromise model performance and security, is increasingly common.

Integrating GenAI into your risk management strategy

Mitigating GenAI risks starts with strengthening your FI’s risk management framework. But how do you know where to start?

The Fintech Open Source Foundation’s (FINOS) AI Readiness Governance Framework is an open-source toolkit that helps FIs adopt and manage generative AI. Ideal for technical and risk teams, the guidance covers AI use across development, procurement, and operations, giving FIs a well-rounded resource for practical and responsible AI implementation.

Enforcement actions are also helpful resources for identifying gaps in your risk management program. For example, the Securities and Exchange Commission (SEC) charged two investment advisers with “making false and misleading statements about their use of artificial intelligence.” The enforcement action — and $400,000 in combined civil penalties — sent a clear message to financial companies: Ensure your AI claims are accurate, transparent, and well-documented.

As your institution evolves its use of GenAI, take a proactive approach to managing risks throughout the risk lifecycle:

Risk identification: Pinpoint where GenAI is being used, both internally and through vendors, and consider creating an AI inventory to help identify and manage AI risks. Stay updated on the latest AI-related regulatory updates so you don’t miss any notable changes. Automated compliance management software can help your FI stay updated on changes relevant to your organization’s size, resources, and geography.
Risk analysis: Once you identify a risk (or the potential risk), consider its impact on your institution. Update risk assessments as needed based on the inherent risk and controls in place, such as policies and procedures, cybersecurity measures, and employee training.
Risk treatment and mitigation: Guided by FI’s risk appetite, determine how you’ll approach GenAI risk across your organization by avoiding, mitigating, transferring, or accepting it.

Monitoring and review: It’s crucial to proactively reassess and address risks, taking early corrective action to prevent issues from becoming major problems. Regularly review AI-generated outputs, track performance, and ensure that vendor disclosures are up to date.
Communication. An often-overlooked step is to ensure that your team members are familiar with your institution’s GenAI risk management program, policies, and procedures. They should be communicated, consistently followed, and reinforced through training.

GenAI risk management best practices

As you evaluate your FI’s GenAI usage internally and via vendors, consider these best practices to build a solid risk management framework now and in the future:

Define your risk appetite: Setting boundaries is crucial to responsible use. Your board of directors and executive team should establish a clear, written risk appetite that guides your FI’s AI-related strategies and decisions.  
Prioritize explainability: Invest in GenAI solutions that offer transparency and clear reasoning behind outputs. Explainable AI supports trust, regulatory compliance, and effective risk assessment.
Ensure human oversight and control. Keep your leadership and stakeholders actively involved in critical decision-making processes, even when GenAI provides analysis or recommendations. A “human-in-the-loop” approach is crucial to preserving accountability and control.
Update risk assessments and controls: Take a dynamic approach to risk management by regularly updating your risk assessments, controls, and other risk management activities as needed, based on internal activities (such as the introduction of new products) and external events (including regulatory updates).
Don’t forget about your vendors: Your FI probably manages dozens or hundreds of vendors. Conduct regular evaluations to determine which vendors utilize GenAI, and how they test models and manage associated risks. Stay proactive.
Educate employees: Compliance is a team sport, and your FI can’t stay risk-ready unless all your staff are aware of their responsibilities. Ensure your employees understand the AI-related risks specific to their roles, the importance of responsible use and fostering a “responsible AI” culture, and when to escalate concerns.
Design for scalability and adaptability: Build a risk management program that can evolve alongside GenAI technologies and expanding organizational use cases. A flexible, forward-looking framework ensures long-term resilience.
Monitor continuously: Like any emerging technology, GenAI is constantly evolving. Stay ahead of the latest opportunities, risks, and cyber threats through ongoing monitoring.

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