ARTIFICIAL INTELLIGENCE IN FINANCIAL RISK MANAGEMENT: PREDICTIVE ANALYTICS AND ETHICAL CONCERNS

Abstract

The accelerating digitization of financial markets has rendered traditional risk management approaches increasingly inadequate to address the scale, speed, and complexity of emerging threats. Artificial intelligence (AI), anchored by predictive analytics, offers a transformative toolkit for identifying, assessing, and mitigating financial risks across credit, market, operational, and compliance domains. This paper examines AI’s role in reshaping financial risk management, highlighting both its predictive power and its ethical and regulatory challenges. Drawing on recent literature and case studies, the analysis shows how machine learning, deep learning, and natural language processing enable institutions to move from reactive to proactive risk management. AI-driven credit scoring integrates alternative data sources to enhance predictive accuracy and financial inclusion. Fraud detection systems classify transactions in real time, reducing false positives and preventing losses. Market and liquidity risk forecasting incorporates macroeconomic indicators and sentiment analysis to anticipate volatility spikes and liquidity squeezes. Operational risk management benefits from AI’s ability to monitor internal processes and parse unstructured data for early warning signals. However, the deployment of AI raises profound ethical concerns. Algorithmic bias, data privacy, model opacity, systemic risks, and diminished human oversight threaten fairness, transparency, and market stability. Regulatory frameworks such as the European Union’s Artificial Intelligence Act increasingly demand transparency, explainability, and accountability in high-risk AI applications like credit scoring. The paper argues for a balanced integration of AI-driven predictive analytics with robust ethical governance frameworks, including Explainable AI techniques, robust data governance policies, diversification of models, human-in-the-loop mechanisms, and complementary technologies such as blockchain. These measures transform ethical principles into operational practices, enabling institutions to harness AI’s predictive power responsibly. By embedding fairness, transparency, and accountability into AI systems, financial institutions can enhance resilience, efficiency, and inclusivity while maintaining public trust and regulatory compliance.