.png){kind=logo}
AI is now woven into the vendor ecosystem that powers your fintech. Your payment processor might use it to detect fraud. Your Know Your Customer (KYC) provider might use it to verify identities. Your loan servicer may use AI to predict delinquency or default.
Often, these capabilities are added quietly — without disclosure or fanfare — creating blind spots for fintechs. While AI brings innovation and efficiency, it also introduces new risks.
Even seemingly straightforward AI applications, like chatbots or document processing, can introduce issues that lead to regulatory violations, customer mistrust, and costly litigation.
As AI becomes more common in vendor services, fintechs need a new approach to identify, assess, and manage AI risk.
Not all AI risks are created equal. For example, a vendor that uses generative AI for research poses less risk than one whose AI interacts directly with your customers. Understanding where each vendor falls on this spectrum is essential to managing exposure effectively.
As you assess vendor AI, be aware of these risks:
Data privacy: AI systems rely on data — sometimes personal or sensitive information — to train their systems. Poor data protection can cause data breaches or privacy violations, putting your fintech at risk of GLBA or other privacy compliance failures.
Example: A payments vendor uses AI to detect fraud patterns but stores unencrypted transaction data. A breach exposes your customer’s payment information, damaging your reputation.
Bias and fairness: Your vendor’s AI systems may produce discriminatory outcomes, especially if it’s trained on skewed data. If decisions unjustly disadvantage protected classes, your fintech may face fair lending, ECOA, or UDAAP violations and reputational damage.
Example: Your credit decisioning vendor trains its AI model on historical lending data. It reflects decades of systemic bias and now the model denies loans to qualified applicants from certain zip codes, exposing your fintech to accusations of discrimination.
Explicability and transparency: Understand the decision making of your vendor’s AI product and be able to communicate to consumers why a decision was made. AI decisions also need to be auditable.
Example: A bank partner asks you to explain why a customer was denied a loan. Your vendor's AI made the decision, but they can't provide clear documentation of the factors that led to the denial. Without this transparency, you're unable to demonstrate compliance.
Poor AI performance: How often is your vendor’s AI system updated and upgraded? AI models become less accurate when market conditions, customer behaviors, and economic factors evolve. Increasing error rates can cause compliance failures and poor customer outcomes.
For example: Your fraud detection vendor's AI was trained on pre-pandemic transaction patterns. Post-pandemic shopping behaviors look "suspicious" to the outdated model, causing a spike in false positives that block legitimate customer transactions and drive complaints to your customer service team.
Little to no AI governance: AI policies and oversight indicate maturity and risk awareness. Without this in place, your vendor has no framework for how to responsibly use AI.
For example: A vendor has no documented AI governance framework, no review process for AI deployments, and no designated oversight for AI risks. When its chatbot provides incorrect information about loan terms to customers, there's no accountability structure or incident response plan, leaving your fintech to manage the fallout.
Traditional vendor risk assessments weren’t built for the AI era. It’s critical to ask the right technical questions to ensure your fintech company remains protected.
Accurately assessing your vendors’ AI usage requires subject matter expertise. Ensure the expert reviewing the material has the appropriate credentials and certifications to review the vendor’s answers and documentation.
The expert should then rate the vendor’s AI risk and provide recommendations about next steps. If you don’t have these capabilities in-house, a vendor risk management platform can provide the needed expertise.
Fintechs need to explain automated decisions, especially for lending, credit, and fraud detection.
Ask vendors to provide documentation on how outputs are generated. Be wary of vague answers like “proprietary algorithms” without meaningful transparency. Although some vendors will use proprietary algorithms, there should still be explainability documentation.
Bad data can lead to biased outputs that may violate fair lending laws. A vendor should have clear data sourcing documentation, demographic representation, and information on how frequently data is refreshed.
Watch for red flags like a refusal to share training data or a lack of specifics about the type of data used to train the model.
In 2024, an AI-powered tenant screening tool settled a $2.3 million discrimination lawsuit over alleged bias against low-income renters. If your vendor’s AI shows bias in financial decisions, your fintech could face the consequences.
Look for regular bias audits, testing across protected classes, and remediation processes. If a vendor claims that its AI is neutral or has no formal bias testing program, it’s a red flag.
As real-world data changes, AI models degrade over time. What worked in 2023 likely doesn’t work in 2025. Your vendor should have automated performance monitoring, drift detection thresholds, and model retraining schedules.
A "set it and forget it" approach to AI maintenance can be dangerous. Ongoing monitoring maintains model accuracy and compliance.
When regulators, customers, or lawsuits ask questions, your fintech needs to prove that your vendor’s AI is compliant. Ask your vendor if it has comprehensive audit logs, decision documentation, and regular compliance reports. Don’t wait until you’re in the middle of an audit to obtain evidence and documentation.
The good news is that your fintech doesn’t have to evaluate vendors and their AI usage alone. There are tools for risk management that offer configurable risk assessments, centralized data and reporting, and continuous monitoring. Fintechs rely on software like Ncontracts to reduce vendor AI risks.
Ultimately, effective vendor AI risk management is about enabling innovation safely. When you understand the risks your vendors' AI systems introduce, you can make informed decisions about which partnerships support your growth and which require additional controls or reconsideration.
