AUDITING AI IN FINANCE: A FRAMEWORK FOR INTERPRETABLE COMPLIANCE IN ALGORITHMIC DECISIONS

The adoption of Artificial Intelligence (AI) and Machine Learning (ML) into financial services has transformed decision-making processes, providing increased efficiency, predictive accuracy, and scalable capabilities. Nevertheless the fact that many AI models are hard to understand makes it hard for companies to follow the rules, be open, and be responsible. Financial regulators are putting more and more stress on the need for systems that can be explained and audited to lower the risks of bias, unfair treatment, and unethical behavior in algorithmic decision-making. Even though there are tools that can help people understand how AI works, there are still no structured frameworks that connect AI auditing with financial rules. This research seeks to establish a thorough framework for auditing AI systems in finance, emphasizing interpretable compliance. The proposed framework incorporates three essential dimensions: (i) data audit, which tackles concerns of quality, bias, and privacy; (ii) model audit, which emphasizes interpretability, fairness, and explainability through advanced XAI techniques; and (iii) outcome audit, which guarantees decision transparency, accountability, and compliance with legal and ethical standards, including the GDPR, EU AI Act, Basel III, and national financial regulations. The study employs a hybrid methodology, integrating a conceptual framework with case-based validation, and deriving insights from applications in credit scoring, fraud detection, and risk assessment. The results are anticipated to make a theoretical contribution by connecting AI interpretability research with financial auditing, and a practical contribution by providing a structured guide for auditors, financial institutions, and regulators. The framework's main goal is to find a balance between the often conflicting goals of accuracy, fairness, and transparency. This will make sure that AI is used in finance in a way that is both new and responsible