Artificial Intelligence-Driven Financial Risk Assessment and Predictive Decision-Making

Abstract

Artificial intelligence (AI) is transforming how financial institutions identify, measure and respond to risk. Machine-learning models now support credit underwriting, fraud detection, market risk monitoring and early-warning systems. Deep learning and natural language processing extract signals from transaction data, text and alternative data sources. This article examines AI-driven financial risk assessment and its role in predictive decision-making. It reviews the evolution from traditional statistical models to machine-learning, deep-learning and generative AI approaches. It also surveys applications in credit, fraud, market and operational risk, and examines the challenges of explainability, fairness, data quality and model governance. Adoption is advancing rapidly. A 2024 survey by the Bank of England and the Financial Conduct Authority found that 75 percent of United Kingdom financial firms were already using AI, up from 58 percent in 2022, and that 81 percent of those using AI applied some form of explainability method. In India, rapid digitisation has expanded both opportunities and risks. Unified Payments Interface (UPI) transactions reached about 131 billion in 2023–24, while frauds reported by banks rose from 13,564 cases in 2022–23 to 36,075 in 2023–24. A simulation on 20,000 synthetic firms illustrates the potential gains from AI. A gradient-boosting model achieved an area under the ROC curve of 0.872, against 0.814 for logistic regression, and identified substantially more defaulters at the same alert rate. The article proposes a governance framework that combines predictive power with transparency, fairness and human oversight, and offers recommendations for institutions and regulators.