Assessing the Role of Artificial Intelligence in Financial Systems Risk Analysis and Decision Making Processes

Abstract

Along with the unpredictability of credit, the advanced complexity of the world's financial networks, and the vulnerability of multiple operating systems to risk, these factors pose significant challenges for traditional risk evaluation frameworks and strategic management constructs. The positive use of Artificial Intelligence (AI) by financial institutions in risk analytics to forecast and improve the efficiency of risk-based decision-support systems has been highly successful. The main objective of this research was to look at the various aspects influencing the risk of financial systems and decision-making by analyzing the impact of AI to detect and quantify risks. A quantitative research framework was developed using financial performance indicators, market volatility measures, credit risk variables, and operational data gathered from 150 financial institutions across the banking, insurance, and investment sectors. To analyze the data, the different types of risk management frameworks that rely on statistical models were compared with AI and machine learning models using predictive and classification algorithms. On average, AI models predicted risk in the systems to be 92.4% accurate, which is 18.7% better than risk management practices. The models also reduced the occurrence of risk by 21.3%. AI models in decision framework systems and strategic moves in financial risk management reduced response time by 26.8% and the time spent on valuation and risk management to forecast investment risk by 17.5%. AI, in this case, has proven to be most useful to assist financial institutions to manage financial risk. The use of intelligent analytics in developing financial management options will contribute to effective methods of risk management.