Artificial Intelligence-Based Predictive Models for Financial Risk Assessment

Abstract

In banking, insurance, and investment management, where precise prediction of credit risk, market volatility, and default probability is vital for informed decision-making, financial risk assessment is a critical function that plays a significant role. Models of risk assessment that are considered traditional frequently rely on linear assumptions and narrow feature sets, which may not be able to adequately capture the complexity of contemporary financial systems. The development of predictive models that are able to analyze massive, complicated, and dynamic financial datasets has been made possible by recent advancements in artificial intelligence. Using machine learning and deep learning approaches to analyze credit risk, market risk, and operational risk, artificial intelligence-based prediction models for financial risk assessment are being developed. These models are centered on the evaluation of credit risk. In this context, the capacity of models that are routinely used, such as decision trees, support vector machines, ensemble approaches, and neural networks, to recognize nonlinear patterns and hidden risk factors is highlighted. In addition to this, it examines the difficulties associated with regulations, the interpretability of models, and the quality of the data. When compared to traditional methods, predictive models that are based on artificial intelligence offer enhanced accuracy and adaptability. This is especially true in financial situations that are highly volatile and data-intensive. An emphasis is placed in the conclusion of the study on the necessity of artificial intelligence systems that are transparent, resilient, and morally aligned in order to assist responsible financial risk management.