Detecting Financial Fraud: Performance Evaluation of Machine Learning Algorithms

Introduction

The paper “Performance of Different Machine Learning Algorithms in Detecting Financial Fraud” aims to evaluate the effectiveness of various machine learning algorithms in detecting financial fraud. The authors, Alhanouf Abdulrahman Saleh Alsuwailem, Emad Salem, and Abdul Khader Jilani Saudagar, conducted an experiment using a dataset from Saudi Arabia’s Ministry of Commerce and Investment.

Methodology

The study compared six machine learning algorithms:

• Decision Tree (DT)
• Random Forest (RF)
• Support Vector Machine (SVM)
• Gradient Boosting (GB)
• K-Nearest Neighbors (KNN)
• Neural Network (NN)

The authors used metrics such as accuracy, precision, recall, F1-score, and area under the ROC curve (AUC) to evaluate the performance of each algorithm.

Results

Algorithm Performance Comparison

The results show that the Random Forest algorithm performed best in detecting financial fraud, followed closely by the Support Vector Machine algorithm.

Key Points

• The study used a dataset from Saudi Arabia’s Ministry of Commerce and Investment containing 10,000 transactions.
• Six machine learning algorithms were compared.
• Metrics such as accuracy, precision, recall, F1-score, and AUC were used to evaluate the performance of each algorithm.
• The Random Forest algorithm performed best in detecting financial fraud.

Conclusion

The study provides valuable insights into the effectiveness of machine learning algorithms in detecting financial fraud. The findings have implications for financial institutions in Saudi Arabia, which can use machine learning algorithms to improve their anti-money laundering (AML) systems and detect fraudulent transactions more effectively.