AI Compliance for Fintechs and Financial Institutions: Pre-Deployment Requirements [2025 Guide]

Written By Ashlie Tarpley
By Tarpley Law PLLC | Tarpley Templates and Legal Research (TTLR) - 10+ years advising financial services executives, federal regulators, and leading credit unions on fintech compliance and regulatory strategy

Financial institutions are deploying AI at an unprecedented pace—but regulators are watching closely. In 2023, the FTC and CFPB jointly obtained a record-breaking $15 million settlement against TransUnion for algorithmic failures in its tenant screening reports that violated the Fair Credit Reporting Act and hampered consumers' ability to obtain housing. Meanwhile, state-level AI disclosure laws went into effect in Colorado, California, and Connecticut, with more states considering enacting their own legislation.

For fintechs and financial institutions, the question isn't whether to adopt AI—it's how to do so without triggering compliance violations, data breaches, or regulatory scrutiny.

After advising financial companies and federal regulators on issues including financial services compliance and privacy for over a decade, I've identified five critical areas that determine whether your AI integration will succeed or expose your institution to significant legal and operational risk. This guide is Part 1 of a two-part series, focusing on what you must address before deployment; Part 2 will cover long-term AI governance and strategic implementation.

AI broadly refers to machine-based systems that perform cognitive functions like learning, reasoning, and decision-making. This includes machine learning algorithms that have powered fraud detection for decades, as well as newer generative AI systems like ChatGPT and Claude that create human-like outputs. With the increased digitalization of banking, AI technologies have transformed operations from credit decisioning to compliance monitoring and contract automation.

Of course, AI adoption in financial services isn't just about innovation; it's about compliance and responsible implementation under some of the nation's strictest regulatory frameworks. And what matters for compliance isn't the type of AI you use—it's how these systems impact your regulated operations.

Here’s what you need to address before deployment.

 In This Guide:

  • Regulatory and Compliance Readiness

  • Data Governance and Privacy Controls

  • Vendor and Third-Party Oversight

  • Operational Integration and Internal Controls

  • Security and Incident Response

  • Frequently Asked Questions

1. Regulatory and Compliance Readiness

“Integrating AI means embedding it within your compliance framework, not adjacent to it.”

AI doesn't exist in a regulatory vacuum. Whether you're using AI for customer service, credit decisioning, fraud detection, or compliance monitoring, you're operating under a complex web of federal and state regulations that govern how financial institutions can collect, use, and automate decisions with customer data. Many institutions are discovering that automation introduces new compliance touchpoints under frameworks like:

  • The Gramm-Leach-Bliley Act (GLBA);

  • The Bank Secrecy Act (BSA/Anti-money laundering (AML));

  • The Federal Trade Commission’s (FTC) unfair and deceptive acts or practices standards (UDAP);

  • The Consumer financial Protection Bureau’s (CFPB) unfair, deceptive, or abusive acts and practices (UDAAP) standards;

  • Fair lending laws including the Equal Credit Opportunity Act (ECOA) and the Fair Housing Act (FHA) and their corresponding regulations;

  • The final rule for the Quality Control Standards for Automated Valuation Models and implementing regulations; and

  • State-level UDAP, privacy, and AI laws.

In addition to various laws and regulations, federal agencies have issued guidance concerning the use of algorithms in financial services. In 2011, the Federal Reserve Board and the OCC issued SR 11-7, Supervisory Guidance on Model Risk Management. In 2024, the CFPB issued guidance on the use of algorithms in relation to consumer reporting and credit decisioning. The Trump Administration rescinded the CFPB’s 2024 guidance, but companies should still pay close attention to the underlying statutes and regulations, i.e. ECOA, Regulation B, and the Fair Credit Reporting Act (FCRA).

Companies will also want to consider the following questions:

  • Are your AI tools producing results that can be audited or explained to regulators?

  • How will your compliance team document the logic behind AI-driven decisions, such as fraud alerts, loan eligibility, or credit risk scoring? In some cases, AI researchers have not been able to understand the reasoning behind AI model outputs. So, it may be inappropriate to use AI models with opaque reasoning to perform regulated activities at your institution.

  • Have you identified state laws that apply to your business activities? Will you need to update your disclosures to satisfy state laws? Companies are increasingly using generative AI to perform customer service functions. Would state law require you to disclose this fact to customers? Do you need to tell customers whether you’re using their sensitive data to train your AI model (e.g. call logs with private financial information)?

  • If your institution operates internationally or you target customers abroad, have you assessed cross-border implications under laws like the EU AI Act, GDPR, or UK GDPR?

The takeaway: integrating AI means embedding it within your compliance framework, not adjacent to it.

Need help auditing your AI compliance framework? Download the FREE AI Regulatory Checklist for Financial Institutions. → Get Your Free Checklist Now!

2. Data Governance and Privacy Controls

“Sound data governance structure is non-negotiable.”

AI systems thrive on data, relying on it both for training and generating information. This dependency exists because today's AI models use probability to produce outputs. For example, when generating a response to a query, an AI model analyzes data to determine the next probable word in a sentence. As the model processes more data, it becomes better at making these predictions and, theoretically, more accurate at responding to queries correctly or appropriately.

The data maintained by financial institutions is often protected by law. Financial institutions that provide AI with access to sensitive data may run the risk that it is disclosed to an unauthorized third party or used without required consents. It could be potentially catastrophic, for example, if an AI chatbot spit out a customer’s sensitive information in response to another customer’s request.

Sound data governance structure is non-negotiable. Companies should:

  • Map out which data AI tools will access: customer, transaction, employee, or vendor information.

  • Identify the circumstances under which AI tools will access data.

  • Assess and address potential bias and limitations in data.

  • Determine how you will ensure the quality of the data.

  • Review whether you have appropriate legal authority to use AI tools under the circumstances and determine whether you have appropriate consents, disclosures, and retention policies in place.

  • Evaluate how your AI tools handle sensitive or regulated data (e.g., social security numbers, account numbers, financial identifiers, credit information, or consumer complaints).

  • Establish internal rules about which data may be used to train AI models and which must remain restricted — especially when dealing with consumer financial data. Make sure you know where all training data is stored in case a regulator asks you to produce it or show where AI training data comes from. 

  • Ensure data shared with vendors is encrypted, anonymized where possible, and governed by a robust data processing agreement.

Negotiating AI vendor contracts? Get attorney-drafted Data Processing Agreement templates at TTLR. → Shop Templates.

3. Vendor and Third-Party Oversight

“You cannot outsource your responsibility to comply with laws even when using third-party AI platforms.”

For fintechs and financial institutions alike, most AI integrations involve third-party platforms or APIs. Regulators are clear – you cannot outsource your responsibility to comply with laws. This principle becomes especially critical when you consider that approximately one-third of data breaches can be traced back to third-party vendors. Understanding how your vendor's AI handles your data isn't just good practice—it's essential risk management.

Your organization should take the following steps when working with AI vendors:

  • Update your risk tiering framework to reflect vendor AI use. Consider: AI type, service criticality, data sensitivity, and potential impact of flawed models or data misuse.

  • Vet AI vendors under your third-party risk management or FFIEC frameworks.

  • Contractually require vendors to provide transparency into model performance, security standards, and data-handling practices.

  • Ask vendors to identify the AI tools they will use to process your data.

  • Ask vendors to disclose whether they will use your organization's data to train their AI models.

  • Ask vendors where they process sensitive data—in the U.S. or abroad.

  • Ensure agreements address IP ownership, data usage limits, confidentiality, and indemnification for errors or breaches.

  • Avoid limitations on liability that would cap your organization's ability to recover the full amount of damages resulting from a data breach.

  • Implement a review cadence for vendor-provided models to identify bias, drift, or compliance gaps over time.

Robust vendor oversight ensures that innovation doesn't come at the cost of accountability.

Need strategic counsel for AI vendor negotiations and third-party risk assessments? Schedule a consultation with Tarpley Law PLLC. → Book Now.

4. Operational Integration and Internal Controls

“Improve efficiency by pairing AI speed with human judgment.”

While AI can enhance efficiency and detect patterns humans might miss, regulatory frameworks require meaningful human oversight—particularly for high-impact decisions involving credit, fraud, or compliance. The question isn't whether AI will replace human judgment, but how your organization will integrate the two effectively. The success of your AI integration will also depend on your organization's operational readiness. Before deployment:

  • Identify which internal workflows will change and where human oversight remains essential.

  • Integrate AI outputs into compliance review, audit, and escalation channels.

  • Establish quality assurance checkpoints for high-impact decisions (credit, fraud, or compliance reports).

  • Develop clear documentation so teams understand when and how to question or override AI recommendations.

  • Train relevant employees to make sure they understand how they should work with AI systems and monitor output.

  • Promptly address deficiencies in AI systems or gaps in internal policies and procedures.

The goal is to improve efficiency by pairing AI speed with human judgment.

5. Security and Incident Response

“Institutions that take proactive security and governance measures will be better positioned to defend both their decisions and their reputations.”

AI introduces new exposure to technical and legal risk. From model poisoning to prompt injection, AI systems can expose institutions to novel forms of data leakage. Data misuse can open companies up to federal and state enforcement actions in addition to private lawsuits. Companies may also take a reputational hit if a customer’s data is mishandled. Financial institutions need to adapt their protocols to mitigate new risks. Companies should:

  • Conduct security testing of AI interfaces and APIs before launch.

  • Update incident response plans to include AI-related vulnerabilities and misuse scenarios.

  • Run incident response exercises to test your institution’s readiness to handle security incidents.

  • Maintain logs for model interactions and output audits to support post-incident reviews.

  • Ensure coordination among your enterprise data management, information security, compliance, and legal teams on AI governance.

Institutions that take proactive security and governance measures will be better positioned to defend both their decisions and their reputations.

Frequently Asked Questions: AI Compliance in Financial Services

  • Description text goes AI systems used by financial institutions are subject to various federal and state regulations, including:

    • GLBA for data privacy and security

    • BSA/AML requirements

    • ECOA and FHA for fair lending

    • FTC UDAP standards

    • CFPB UDAAP standards

    • The Quality Control Standards for Automated Valuation Models and implementing regulations

    • State-level AI disclosure laws like those in Colorado, California, and Connecticut

    • State-level privacy laws like those of California

    Additionally, institutions should review federal guidance on algorithmic decision-making systems.

  • It depends on your jurisdiction and use case. While federal law does not currently mandate blanket AI disclosure, several factors require transparency:

    • State laws: States like California, Colorado, and Connecticut require disclosure when AI is used in certain customer-facing applications, particularly automated customer service

    • Credit decisions: ECOA requires adverse action notices that explain credit denials, which may necessitate explaining AI-driven decisions

    • Fair lending compliance: Institutions must be able to provide specific reasons for credit decisions, which can be challenging with black-box AI models

    • Training data usage: If you are using customer data (like call logs with financial information) to train AI models, privacy laws may require disclosure and consent

    Best practice: Review applicable state laws and implement clear disclosure policies before deploying customer-facing AI systems.

  • AI vendor due diligence should follow your third-party risk management framework with AI-specific enhancements:

    • Identify AI tools: Require vendors to disclose exactly which AI systems will process your data

    • Data processing location: Confirm where sensitive data is processed (U.S. vs. international) and whether it complies with data residency requirements

    • Model transparency: Assess whether the vendor can explain model logic, especially for regulated activities like credit decisioning

    • Security standards: Verify encryption protocols, access controls, and incident response capabilities

    • Contractual protections: Ensure agreements include IP ownership, data usage limits, audit rights, and appropriate indemnification without liability caps that prevent full recovery

    • Ongoing monitoring: Establish review cadences to identify model drift, bias, or compliance gaps over time

    Remember: Regulators are clear that you cannot outsource compliance responsibility, even when using third-party AI platforms.

  • AI systems can access customer data only when you have proper legal authority, which requires:

    • Clear business purpose: Data access must align with legitimate operational needs

    • Appropriate consent: Privacy notices must disclose AI use if required by applicable law

    • Access controls: Implement role-based restrictions limiting AI access to necessary data only

    • Data quality assessment: Ensure training data is accurate, representative, and free from bias

    • Retention policies: Establish rules for how long AI systems can retain accessed data

    • High-risk data requires extra protection: Social security numbers, account numbers, credit information, and consumer complaints need encryption, anonymization where possible, and robust data processing agreements with vendors.

    Critical rule: Establish internal policies about which data may be used to train AI models and which must remain restricted, especially consumer financial data covered by the GLBA.

  • No. While AI can enhance efficiency and detect patterns humans might miss, regulatory frameworks require meaningful human oversight, particularly for:

    • High-impact decisions: Credit approvals, fraud alerts, and compliance reports need human review checkpoints

    • Model validation: Humans must verify AI outputs for accuracy, bias, and regulatory compliance

    • Escalation protocols: Staff must be trained to question or override AI recommendations when appropriate

    • Audit trails: Organizations must document the reasoning behind AI-driven decisions for regulatory examination

    • Deficiency remediation: When AI systems show discrimination, bias, or errors, human teams must promptly address these issues

    The goal: to pair AI speed with human judgment, creating efficient systems that remain accountable, explainable, and compliant with fair lending and consumer protection laws.

  • AI introduces several novel security and legal risks that traditional systems do not face:

    • Model poisoning: Attackers can corrupt training data to manipulate AI behavior

    • Prompt injection: Malicious inputs can trick AI systems into revealing sensitive data

    • Data leakage: AI models trained on customer data might inadvertently expose that information in responses

    • Vendor breaches: Third-party AI platforms represent a significant attack surface (approximately one-third of breaches originate from vendors)

    • Compliance violations: Data misuse can trigger federal and state enforcement actions and private lawsuits

    • Reputational damage: Customer trust erodes quickly when AI systems mishandle financial information

    Mitigation strategies: Conduct security testing before and after launch, update incident response plans for AI-specific scenarios, maintain comprehensive audit logs, and coordinate between data management, security, compliance, and legal teams on AI governance.

Ready to Build Compliant AI Systems?

Don’t let compliance gaps slow your innovation. Get the legal foundation right from the start. Whether you need ready-to-use contract templates or comprehensive regulatory counsel for your AI integration, I can help.

Ashlie Tarpley
Next

The Best Contracts are Simple