Enhancing Enterprise Risk Management and Internal Audit Practices by Applying Machine Learning Models

Organizations are currently in a stage where the volume of financial transactions and data is constantly growing. The same goes for risks associated with the use of data for risk management and strategic decision-making. The likelihood of transactional errors generally increases with data volume and process complexity, while fraud, although less frequent, may have more severe financial, compliance, and reputational consequences for organizations. Continuous auditing practices and well-established enterprise risk management (ERM) processes, combined with AI-driven pattern recognition, trend analysis and segmentation, can enhance timely detection and proper investigation of suspicious transactions. In areas with large volumes of transactions, the audit sampling process may be a lengthy process and pose a detection risk. Using machine learning (ML) models to support critical business processes could prove effective in managing enterprise risk overall. The current study offers new perspectives on managing risk and assurance with ML model output for flagging possible risky transactions within ERP (SAP) systems data. The study population consists of 69,158 finalized billing records extracted from the SAP production environment of a private sector organization, which covers a six-month operational period. The dataset was divided into an 80/20 train–test split, yielding 55,326 training and 13,832 test instances across six classification categories. The study examines the ML methods’ outcomes from billing datasets and their applicability in enhancing audit, assurance, and ERM processes by evaluating output data results from two supervised classification algorithms—multinomial logistic regression (SoftMax regression) and XGBoost—against various criteria generally accepted as risky in audit engagements. Model performance was assessed using accuracy, precision, recall, F1-score, ROC-AUC, and average precision (AP) from precision–recall curves. The results confirm that XGBoost achieves 99% overall accuracy with a macro F1-score of 0.965, outperforming logistic regression (macro F1 = 0.863), and that ML output allows early investigation and follow-up procedures to minimize the risk of fraud and errors and optimize risk management activities, thus strengthening internal control frameworks.