Fairness, Accountability, and Transparency in Financial AI: Addressing Bias through Responsible Regulation and Auditable Design

Financial Artificial Intelligence, Algorithmic Fairness, Accountability in AI Systems, Transparency and Explainability, Ethical AI Governance, Bias Mitigation in FinTech, Fairness Impossibility, Regulatory Compliance

AI systems increasingly shape decision-making in financial services, particularly in credit scoring, insurance underwriting, and fraud detection. While these systems improve efficiency and predictive accuracy, they may also reproduce and amplify existing social and economic inequalities. This paper examines how fairness, accountability, and transparency (FAT) principles can be systematically integrated into financial AI systems. Using a conceptual and critical approach, the study synthesises literature on algorithmic bias, analyses the regulatory frameworks of the EU AI Act, GDPR, and US fair lending law, and incorporates fairness impossibility results from Chouldechova (2017) and Kleinberg et al. (2017). The analysis identifies key sources of bias, including data imbalance, proxy variables, measurement error, label contamination, and feedback loops. It further highlights that fairness metrics such as demographic parity, predictive parity, and equalised odds cannot be satisfied simultaneously in many contexts, creating unavoidable trade-offs for regulators and practitioners. To address these challenges, the paper proposes a FAT Lifecycle Framework covering six stages of AI development and governance. The framework offers practical guidance for organisations and regulators seeking to operationalise FAT principles across the full AI lifecycle while supporting equitable access to financial services.