From Explainability to Accountability: The FinTRACE-AI Framework for Algorithmic Risk Governance in FinTech

Artificial intelligence is increasingly embedded in financial technology, particularly in credit scoring, fraud detection, anti-money laundering monitoring, robo-advisory services, and digital risk pricing. While these applications improve efficiency and expand access to financial services, they also create algorithmic risks that cannot be managed through technical explainability alone. Model opacity, biased data, discriminatory outcomes, privacy leakage, weak audit trails, third-party model dependence, and unclear responsibility for automated decisions may lead to consumer harm, credit risk distortion, reputational damage, and regulatory failure. This raises a central research question: how can FinTech governance move from explaining algorithmic decisions to assigning responsibility, enabling audit, ensuring compliance, and correcting harmful outcomes?

To answer this question, this paper develops the FinTRACE-AI Framework, a responsible AI governance framework for algorithmic risk in FinTech. The framework is grounded in responsible AI theory, model risk management, financial consumer protection, and accountability-based governance. Methodologically, the paper adopts a conceptual synthesis and comparative regulatory analysis, drawing on AI governance principles, banking model risk management practices, and emerging risk-based regulatory approaches to high-impact AI systems. Unlike existing explainability-centred AI governance models, FinTRACE-AI treats algorithmic risk as a full lifecycle governance problem. It links five dimensions: Transparency, Responsibility, Auditability, Compliance, and Enforcement. These dimensions are organised into three operational layers. The technical layer focuses on data provenance, model documentation, fairness testing, explainable outputs, robustness checks, and post-deployment monitoring. The institutional layer assigns internal responsibility through model ownership, independent validation, audit functions, human oversight, consumer protection, and redress mechanisms. The regulatory layer connects financial institutions and technology providers with algorithmic impact assessment, supervisory reporting, regulatory sandbox testing, third-party oversight, and corrective enforcement.

The framework is applied analytically to AI-based credit scoring. The paper shows how biased training data can generate financial exclusion, how opaque feature selection can weaken consumer challenge rights, how model drift can distort credit risk assessment, and how outsourced or vendor-based models can create accountability gaps. FinTRACE-AI translates these risks into concrete governance requirements, including pre-deployment bias testing, adverse impact assessment, model cards, audit trails, responsibility matrices, human review procedures, consumer explanation channels, and regulatory escalation mechanisms.

This study contributes to the literature in three ways. First, it shifts the focus of FinTech AI governance from explainability as a technical property to accountability as an institutional and regulatory process. Second, it integrates responsible AI, model risk management, and financial regulation into a single governance framework tailored to algorithmic financial decision-making. Third, it provides a practical pathway for financial institutions, regulators, and technology providers to identify, trace, audit, and correct algorithmic risks before they become systemic or consumer-protection failures. The paper therefore positions ethical AI governance as a core component of financial risk management and financial stability in the digital finance ecosystem.