Department of Computer Engineering

Efficient risk management is a crucial factor for organizations as it assists in identifying and controlling potential threats that may impede the achievement of objectives. It helps inform decision-making and enhances overall performance. This thesis examines existing risk management practices and explores the feasibility of integrating machine learning to augment them. The research delves into various machine learning algorithms used in risk management. The initial step involved 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 to assess these risks, and they were analyzed based on 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, and F1 score were employed to present the results, with the Support Vector Machine demonstrating superior performance in detecting risks over other algorithms.