How InferTrust™ provides verifiable records for AI fraud detection decisions, supporting fair lending compliance, customer dispute resolution, and regulatory ex
AI-powered fraud detection systems make high-volume, high-consequence decisions: blocking transactions, freezing accounts, flagging customers for investigation, and triggering suspicious activity reports. Each of these actions affects a real person, and each carries regulatory and legal implications. When a fraud detection decision is wrong, the consequences range from customer attrition to discrimination claims, regulatory penalties, and litigation.
InferTrust™ Financial captures each AI-assisted fraud determination with the full decision context: the fraud model version, the risk scoring methodology, the threshold and policy that triggered the action, and the resulting determination. When a transaction is blocked or an account is flagged, the record provides cryptographic proof of why the AI made that determination at that moment.
Regulators examine fraud detection systems for disparate impact and discrimination. When a regulator or examiner asks why a particular demographic group experiences higher false positive rates, the organization needs more than aggregate statistics. InferTrust™'s decision-level records enable analysis of individual determinations against the policies and thresholds that produced them, supporting fair lending compliance at the decision level.
When a customer disputes a blocked transaction or account freeze, the institution needs to explain what triggered the action. InferTrust™'s records provide the verifiable evidence: the specific risk factors the AI identified, the threshold that was exceeded, and the policy that governed the response. This evidence supports faster, more accurate dispute resolution and reduces customer friction.