Intelligent Risk, Fraud, and Compliance Analytics in Modern Financial Services

Abstract

Financial organisations increasingly use artificial intelligence to detect fraud, assess creditworthiness, forecast investment conditions, monitor portfolios, and automate regulatory reporting. This critical narrative review examines the decision applications through which artificial intelligence creates operational value in banking, insurance, payments, capital markets, and financial technology. It compares machine learning, natural language processing, deep learning, expert systems, fuzzy logic, and hybrid approaches, while emphasising that application performance depends on data quality, governance, explainability, and continuous oversight. Evidence reported in the source systematic review shows particularly strong associations for machine learning, regulatory compliance, fraud detection, risk analytics, and credit scoring. The article discusses how artificial intelligence changes each stage of the financial decision process, from data ingestion and signal detection to recommendation, review, and reporting. It also evaluates common weaknesses, including false positives, concept drift, historical bias, fragmented datasets, opaque models, and over-automation. The review concludes that artificial intelligence can improve speed, coverage, and predictive precision, but institutions must match the model to the decision, maintain human accountability, and monitor performance across changing market and regulatory conditions.