What question did this study set out to answer?

To improve accuracy in detecting fraudulent transactions using machine learning techniques.

March 25, 2026Iconic Research and Engineering Journals

Enhanced Fraud Detection in Financial Transactions using Machine Learning

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Advanced machine learning techniques improve fraud detection accuracy in financial transactions, suggesting better security measures.

To improve accuracy in detecting fraudulent transactions using machine learning techniques.
Implemented supervised learning algorithms such as Random Forest, XGBoost, and Logistic Regression.
Used data preprocessing techniques to clean and prepare transactional data.
Applied feature engineering to enhance model performance.
Managed class imbalances to improve detection rates.
Compared ensemble-based models with conventional classifiers.
Ensemble-based models achieved higher prediction accuracy than traditional classifiers.
Significantly reduced false positives in fraud detection.
Enhanced security measures strengthened transaction integrity.
Lowered financial losses in digital banking through improved detection.

Monika et al. (2026) studied this question.

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