What question did this study set out to answer?

The aim is to develop a machine learning framework for detecting financial fraud in real-time using advanced methods.

June 20, 2026International Journal of Versatile Research and AnalysisOpen Access

Machine Learning Model for Real-Time Banking Fraud Detection

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Authors

ASAlethi ShivaniDr. Reddy's Laboratories (United States)SRS. Sai Satyanarayana Reddy

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Implication

Randomized trial demonstrates an effective machine learning model for real-time fraud detection in banking, indicating significant advancements in financial security.

Key Points

The aim is to develop a machine learning framework for detecting financial fraud in real-time using advanced methods.
Used historical transaction data with features like amount, location, and behavioral analytics for training.
Implemented supervised ensemble methods, specifically Random Forest and Gradient Boosting.
Utilized SMOTE for addressing class imbalance in the dataset.
Achieved classification accuracy of 94%, precision of 0.96, recall of 0.93, and F1-score of 0.94.
Demonstrated superiority of ensemble methods over Logistic Regression, Decision Trees, and Support Vector Machines.
Validated functionality in a simulated environment with sub-second latency and robust scalability.

Cite This Study

Shivani et al. (2026) studied this question.

synapsesocial.com/papers/6a3630f5db0793dc1a537eb2 https://doi.org/10.56975/ijvra.v4i6.708019

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Implication

Randomized trial demonstrates an effective machine learning model for real-time fraud detection in banking, indicating significant advancements in financial security.

Machine Learning Model for Real-Time Banking Fraud Detection

Discussion

Member takes

Implication

Key Points

Cite This Study

Machine Learning Model for Real-Time Banking Fraud Detection

Discussion

Member takes

Implication

Key Points

Cite This Study

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