Modeling Fraud Detection in Community Development Banking Through Machine Learning
Keywords:
Fraud Detection, Community Development Banking, Machine Learning, Deep Learning, SMOTE, Decision Tree, Random Forest, AUC-ROC, Class Imbalance, Precision, Recall.Abstract
Fraud detection in the banking sector, particularly in community development banking, has become a critical concern with the rise of digital financial services. This study explores the application of machine learning (ML) models for detecting fraudulent transactions in community development banking. The models evaluated in this study include decision trees, random forests, k-nearest neighbors (KNN), support vector machines (SVM), and deep learning (artificial neural networks - ANN). Data preprocessing techniques, such as handling missing values, feature scaling, and addressing class imbalance using SMOTE (Synthetic Minority Over-sampling Technique), were applied to ensure the models' effectiveness. The models were evaluated using performance metrics such as accuracy, precision, recall, F1-score, and AUC-ROC. The results indicate that the deep learning model outperformed traditional machine learning models, achieving the highest accuracy (94.5%) and recall (95.2%) rates. Despite higher computational costs, deep learning demonstrated superior performance in detecting fraud while minimizing false positives and false negatives. The study also highlights the significant improvement in recall and overall model performance after balancing the dataset with SMOTE. The findings emphasize the potential of deep learning in fraud detection while suggesting the need for trade-offs between model accuracy and execution time for real-time applications in community development banking. This study provides valuable insights for developing robust and efficient fraud detection systems using machine learning in the financial sector.
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