TRUSTWORTHY AI RISK MANAGEMENT, ROBUSTNESS ASSESSMENT, AND REGULATORY COMPLIANCE IN FINTECH

Abstract

The use of Artificial Intelligence (AI) in financial technology (FinTech) has revolutionized digital financial services, offering automation in decision-making processes, fraud detection, credit risk assessment, customer support, and regulatory compliance. But there have been issues with transparency, fairness, robustness, cyber security and regulation accountability that created serious practice issues for trustworthy use of AI. This study looks at how reliable AI systems perform across the entire lifecycle, particularly lifecycle risk, robustness testing, and regulatory readiness, in the FinTech sector. An extensive evaluation framework was created to evaluate governance practices, data quality, model performance, model explainability, bias minimization, security controls, ongoing monitoring and compliance with emerging AI guidelines. A lifecycle management program was implemented and results from the experiments confirm that its effects on reliability and operational and compliance risk are significant. Model stability and prediction degradation for adversarial attacks, noisy inputs, and distribution shifts showed strong results of robustness testing, confirming the effectiveness of implementing lifecycle controls. In addition, through governance mechanisms, transparency and auditability increased were observed, which resulted in increased scores in financial services application readiness. Demographic bias was identified as a potential type of bias, and the results showed that it could be measurably improved after continuous bias checking and model re-calibration for an evaluation of fairness. The results also indicate that combining explainability with continuous risk monitoring processes increases trust towards the model without significantly affecting the predictiveness of the models. Overall, the proposed lifecycle-based approach offers a helpful roadmap for financial institutions looking to implement reliable AI systems that meet technical capabilities, ethical standards, and regulatory nuances. The findings align with the adoption of pro-active strategies in digital financial service containing AI governance to enhance resilience’s, customer confidence and sustainability potential.