AI & Business Environment

In the current fast-changing financial system, risk forecasting is a vital tool in ensuring the ability of financial institutions to be sustained. The use of conventional risk assessment models that heavily rely on the past and traditional statistical methods tend to have constraints in terms of their ability to capture complexity, non-linearity, and volatility of the current financial markets. This paper examines the application of Artificial Intelligence (AI) to forecast financial risks and how it may be used to outperform the existing methods in terms of accuracy, efficiency, and flexibility. The study uses a quasi-experimental mixed-methods approach to research taking both quantitative and qualitative views. Quantitatively, two sets of models are used, namely, the Logistic Regression (the traditional approach to statistics) and the Random Forest (the AI approach), to the same financial dataset, historical credit, and market data.