Application of Machine Learning Techniques to Fraud Detection in Financial Transactions
DOI:
https://doi.org/10.31181/jidmgc21202647Keywords:
Machine Learning, Financial Fraud Detection, Online Transactions, Blockchain, Imbalanced Data, Anomaly DetectionAbstract
The rapid expansion of e-commerce and online financial transactions has made financial fraud a serious global concern. Conventional methods based on fixed rules are often unable to detect complex fraud patterns. This study examines machine learning techniques as a more adaptive approach to identifying fraudulent transactions. The methods considered include logistic regression, random forest, XGBoost, support vector machines (SVM), bidirectional long short-term memory (BiLSTM), bidirectional gated recurrent units (BiGRU), and hybrid models. Evidence from the reviewed studies indicates that these models can achieve high fraud-detection accuracy when combined with data preprocessing, effective feature selection, and methods for handling class imbalance. Integrating machine learning with technologies such as blockchain and applying optimization methods have also improved the security and reliability of financial systems. These approaches can reduce false positives while supporting real-time detection and the processing of large volumes of transaction data.
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