Corporate Financial Fraud Identification based on Random Forest Algorithm
Qi Liu, Sheng Wang · 2025
Corporate financial fraud is a rising threat, eroding economic stability, investor trust, and regulatory compliance. Traditional fraud detection methods, including manual inspections and rule-based approaches, are becoming insufficient in addressing the growing complexity of modern fraudulent activities. These methods tend to experience high false-positive rates, late detection, and restricted adaptability, rendering them inefficient in detecting changing fraud patterns. To address these challenges, this study proposes a Machine Learning (ML)-based approach utilizing the Random Forest (RF) algorithm for the detection of corporate financial fraud. The Random Forest model is selected due to its capacity to manage complex financial information, identify non-linear relationships, and achieve high classification accuracy. Through the utilization of multiple decision trees, the model efficiently differentiates between fraudulent and legitimate transactions, significantly enhancing fraud detection rates. Experimental outcomes confirm that the model performs better than conventional fraud detection methods, by attaining the accuracy of 0.98 precision of 0.97, recall of 0.965 and F1-score of 0.974. Also, feature importance analysis reveals key financial indicators, including revenue anomalies, unusual variations in liabilities, and discrepancies in cash flow, that play a role in fraud. The research evidence indicates that RF algorithm can be an effective means of fraud detection incorporates, presenting better accuracy, efficiency, and scalability. Through this method, useful information for auditors, regulators, and banks is achieved, allowing effective prevention of frauds and supporting strong corporate governance. Through integrating ML practices, this paper assists in strengthening financial transparency and accountability, effectively reducing the menace of financial fraud risks.