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Interpretable Machine Learning for Fraud Detection in Large-Scale Financial Transactions

Year: 2025

Type: article

Abstract: Financial fraud detection is characterized by extreme class imbalance and the need for high explainability. This study implements an interpretable framework using a dataset of 18 million transactions. By employing Logistic Regression with balanced class weights and extensive behavioral feature engineering, we achieved a ROC-AUC of 98.93% and a PR-AUC of 55.73%. The results demonstrate that transparent models, when supported by logical behavioral features, can effectively isolate fraudulent activities while providing clear risk scores for financial auditing.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.17980638

Author: Mariam Ali, Mariama

Institution: Virginia Tech

Language: English

Topic: Imbalanced Data Classification Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Reduced inequalities

Open Access status: green

OpenAlex ID: W7116097358

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