EXPLAINABLE ARTIFICIAL INTELLIGENCE FOR FINTECH INCIDENT RESPONSE WITH AUDITABLE DIAGNOSTIC EVIDENCE

Financial technology services depend on interconnected microservices, where failures can propagate across payment, identity, ledger, and risk components. Incident response must restore service while preserving a defensible account of diagnostic decisions. This article develops an explainable, human-supervised incident-response framework that links telemetry, ranked causal hypotheses, counterfactual tests, privacy controls, and tamper-evident evidence. It specifies a controlled comparison between standard dashboard-led investigation and an AI-assisted workflow in a synthetic fintech testbed. Outcomes include time to correct diagnosis, top-k accuracy, false leads, audit completeness, and unauthorised action rate. A prespecified regression framework is proposed to estimate workflow effects while accounting for incident and analyst variation, with suitable distributional models for time, counts, and bounded audit scores. The supplied material contains mock-data code rather than observed trial data; therefore no treatment effect is asserted. The contribution is a reproducible protocol and an operational definition of evidence-preserving diagnosis, suitable for prospective evaluation. The framework treats speed, diagnostic validity, privacy, and reviewability as joint design constraints rather than interchangeable benefits.