Risk Management Based on Machine Learning

Abstract Efficient and effective risk management is a crucial factor for organizations as it enables in identifying and responding to potential threats that may impede the achievement of strategic organizational objectives. It helps informed decision-making and enhances overall performance. This article examines existing risk management practices and explores the feasibility of integrating machine learning to augment them. This research explores different machine-learning algorithms that can be utilized in risk management. The initial step involves identifying and categorizing risks from multiple government institutions, identifying 24 risks and 139 corresponding risk indicators. Daily, weekly, monthly, and annual data were generated for the indicators when there was a lack of historical data to assess these risks, and they were analyzed based on the domain knowledge. Using these indicators, a risk evaluation index was developed, and a calculation formula was formulated to determine each risk's probability and impact scores. To validate the findings, diverse data scenarios were created and processed through advanced algorithms, including Support Vector Machine, Gaussian Naive Bayes, Multinomial Naive Bayes, Decision Trees, and Random Forest. Metrics such as accuracy, precision, recall, F1-score, and Cohen's kappa were employed to present the results, with the Support Vector Machine demonstrating superior performance in detecting risks over other algorithms. Though this study is based on the Turkish experience, it can be generalized to predict potential risks for different countries and organizations.