AI-Driven Predictive Analytics for Financial Risk Assessment and Decision-Making

Abstract

Financial institutions are moving from periodic, rule-based risk assessment to continuous, data-driven prediction. Artificial intelligence (AI) and machine learning (ML) now support credit scoring, fraud detection, market risk forecasting, corporate distress prediction, and systemic risk monitoring. This review synthesizes research published up to 2023 on how AI-driven predictive analytics improves financial risk assessment and how its outputs are turned into decisions. It covers three linked themes: (1) the predictive techniques used across financial risk types, from ensemble learning to deep sequence models and language models; (2) the decision support mechanisms, including explainability and workflow integration, that make predictions usable by risk officers, lenders, and regulators; and (3) the data and platform foundations, such as secure hybrid cloud storage and configurable enterprise workflows, on which these systems depend. The review also draws on predictive decision support, cloud data management, and customer relationship management (CRM) research from healthcare, a domain that shares finance's high stakes, sensitive data, and regulatory scrutiny. The evidence shows that ML models, particularly gradient-boosted ensembles and deep learning, often outperform traditional statistical models in accuracy. Their value in practice, however, depends on interpretability, data quality, handling of class imbalance and concept drift, fairness, and governance under model risk management rules. The review proposes an integrated framework linking data foundations, predictive analytics, explainable decision support, and workflow-driven action, and it outlines directions for future research.