How Generative AI Impacts Your Financial Organization’s Risk Management Program

Generative AI, or GenAI, is quickly becoming an integral part of the financial ecosystem, whether financial organizations 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 organizations 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  
• What regulators say about GenAI
• GenAI risk management strategies
• GenAI risk management best practices  
• FAQ

Struggling to keep up with regulatory changes? Nquiry delivers cited, auditable answers to complex regulatory questions in minutes. Learn more. 

What is Generative AI (GenAI)?

GenAI is one of the most used subsets of AI in the financial services space. Where predictive AI draws on structured data to answer "what is likely to happen," GenAI generates new outputs that mirror the data it was trained on — answering, in effect, "what can I create." A bank or mortgage lender might build a GenAI-powered customer service chatbot, for example, training it on existing FAQs, customer data, and internal resources to handle more complex questions. 

Even if your organization 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 financial organizations 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.   

Related: AI in Financial Services: Best Practices and Red Flags   

What GenAI Can Do for Financial Organizations

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. For example, Nquiry, an AI-powered compliance tool, helps financial organizations answer complex regulatory compliance questions. 

GenAI can also be a gamechanger in research, analysis, and product development. Financial organizations 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 organizations make faster, more informed decisions. 

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

Where GenAI Introduces Risk

While GenAI can save financial organizations 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. 

Related: How to Manage Third-Party AI Risk: 10 Tips for Financial Institutions 

What Regulators Say About GenAI

The April 2026 interagency model risk guidance from the OCC, Federal Reserve, and FDIC replaced the SR 11-7 framework that had governed model risk since 2011. Generative and agentic AI are explicitly out of scope, with regulators acknowledging that these technologies require a different approach. A formal request for information on AI use in banking is expected soon. Until then, financial organizations are responsible for maintaining their own governance practices, as well as adhering to evolving state AI requirements.   

Enforcement actions offer another window into regulatory expectations. For example, the 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.  

Related: Leveraging Enforcement Actions to Strengthen Your Compliance Program 

Building a GenAI Risk Management Strategy

In the absence of a comprehensive GenAI framework, industry resources can help fill the gap. 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. The Cyber Risk Institute's free AI Adoption State Questionnaire is also worth bookmarking, as it maps directly to the frameworks examiners are already referencing. Other popular resources include the Financial Services AI Risk Management Framework (FS AI RMF), NIST AI Risk Management Framework (AI RMF) and COSO’s Achieving Effective Internal Control Over Generative AI (GenAI).

GenAI and The Risk Lifecycle

Once you have a sense of where your organization stands, the next step is building governance around the full 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. 

Related: What is the Risk Management Process? 

• 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 organization's GenAI risk management program, policies, and procedures. They should be communicated, consistently followed, and reinforced through training. 

Related: AI Is Already Costing Financial Institutions Millions: Here's How to Manage the Risks 

GenAI Risk Management Best Practices

As you evaluate your organization'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 organization'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. 
• Build and maintain an AI inventory: Knowing where AI lives in your organization is increasingly a baseline regulatory expectation. Document every internal AI use case and flag which vendors are using it, how, and with what controls.
• 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.   

FAQ

How do predictive AI, generative AI, and agentic AI differ?

Predictive AI, generative AI, and agentic each represent a different capability. 

Predictive AI analyzes historical data to forecast outcomes, such as credit risk scores, fraud detection, and churn prediction.  It has long been used across financial services, particularly in lending. Because those outputs directly affect customers, existing compliance obligations around fair lending and adverse action apply. 

GenAI generates content, including text, summaries, images, and code. The risk profile is different, and so is the appropriate governance. Human review of outputs, clear policies on approved use cases, and data handling controls matter more than the validation frameworks designed for decisioning models. 

Agentic AI goes a step further. Where predictive AI produces an output and generative AI creates content, agentic AI plans, decides, and acts. An example of agentic AI is a system that autonomously reviews vendor documentation and updates a risk record without any prompt is agentic AI. The April 2026 interagency model risk guidance (SR 26-2) described these systems as "novel and rapidly evolving," signaling that regulators recognize agentic AI operates differently than the decisioning models the guidance built to govern. What appropriate governance looks like in practice is still taking shape. 

How do I know if my vendors are using AI?

An AI audit is a good place to start. Many vendor agreements address AI use in ways that don't get flagged during procurement. Searching for terms like "machine learning," "automated decisioning," and "artificial intelligence" can surface what's already there. Collecting documentation and adding AI-specific language to contracts are strong controls, but ongoing monitoring matters too. Also, require vendors to notify you when their AI practices change. 

What's shadow AI, and why does it matter?

Shadow AI refers to AI tools employees use without approval or oversight from IT, risk, or compliance teams. For example, someone may use a consumer-grade tool to summarize a document or research a regulatory question because it's fast. The problem is that when data flows through a platform with no contractual protections or audit trail, you have exposure that won't show up on a risk assessment until something goes wrong. 

Are some types of AI better suited for financial services than others?

Not all AI is built — or trained — the same way. General-purpose AI is built for broad use, not regulatory accuracy. For compliance management, that distinction matters. AI purpose built for compliance draws from curated regulatory content and shows its sources, so you can verify what you're relying on. 

Nquiry brings purpose-built AI to compliance research, so your team gets accurate, sourced answers fast.