METHOD FOR DETECTION OF FRAUDULENT BANKING TRANSACTIONS BASED ON TECHNOLOGIES OF APPLICATION OF MACHINE LEARNING TOOLS AND INFORMATION PROTECTION IN DISTRIBUTED-PARALLEL USE IN THE PROCESS OF IT PROJECT MANAGEMENT

Abstract

The rapid growth of digital financial transactions has created a complex environment for ensuring banking security. Detecting fraudulent transactions is increasingly challenging due to the diversity and evolving nature of fraud schemes. Traditional monitoring approaches based on manual analysis and simple rule-based systems are inefficient because of high labor costs, numerous false positives, and limited adaptability to emerging types of fraud.

Advances in machine learning enable the development of adaptive fraud detection systems capable of automatically identifying complex patterns of suspicious behavior. Ensemble learning methods and techniques for handling class imbalance significantly improve transaction classification accuracy while reducing the workload on security services. The scarcity of labeled fraudulent examples limits conventional supervised models, and synthetic data generation methods such as SMOTE can create additional training samples, enhancing model performance.

This study presents a method for detecting and classifying fraudulent banking transactions using a Random Forest classifier enhanced with SMOTE and integrated into a distributed-parallel processing framework suitable for IT project management. The approach includes modular data processing, model training, validation, and testing, providing scalability and adaptability. Experimental evaluation on a real-world bank transaction dataset shows high effectiveness: F1-score 0.83, precision 0.86, recall 0.79, and area under the ROC curve 0.93, outperforming logistic regression, decision trees, and standard Random Forest. The method detects approximately 79% of fraudulent transactions while maintaining a low false positive rate of 4.5%, demonstrating practical applicability in operational banking environments.

The research contributes scientifically by advancing machine learning algorithms for anomaly detection in highly imbalanced financial data and developing adaptive detection systems. Practically, it reduces financial losses, improves transaction security, and increases the efficiency of bank security operations, enhancing trust in digital financial services. The proposed method provides a robust foundation for the development of intelligent and adaptive fraud detection systems in the financial sector.