AI-Enabled Financial Integrity Engines: Explainable Models for Transparent Risk Assessment and Human-Centered Oversight

As financial systems get increasingly digitized, organizations are encountering increasingly high risks of algorithmic opacity, regulatory non-compliance, auditability gaps, and loss of institutional trust. Though artificial intelligence is now a mainstream instrument used in the assessment of financial risk, anomaly detection, and predictive analytics, its fast mainstreaming has also revealed structural vulnerabilities in governance arch design that is based on black-box models and automation-driven logic. These are the problems that highlight the increased significance of financial integrity systems that can entrench explainability, regulatory compliance, and human-centered oversight, as opposed to technology-based AI deployment strategies. This paper takes a governance-based approach to study the transformations occurring in transparency, risk containment, and integrity results in financial systems with the help of AI-enabled Financial Integrity Engines. The study, based on institutional economics and explainable AI theory, takes AI not as an independent decision-maker, but as an embedded governance mechanism, and the quality of which is determined by explainability, compliance-by-design, and human-in-the-loop control mechanisms.