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Adaptive Fraud Detection in Online Transactions Using Machine Learning Techniques

Year: 2025

Type: article

Abstract: Financial institutions suffer substantial losses dueto fraudulent online transactions. Traditional fraud detectionmethods often fail to identify evolving fraud patterns due todataset imbalance and high false negative rates. This paperproposes an adaptive fraud detection system integrating a valueat-risk (VaR) metric with machine learning techniques. Thesystem uses historical simulation to estimate potential fraudrelated losses and employs K-Nearest Neighbors (KNN) to classifyfraudulent transactions. The proposed approach enhances frauddetection accuracy while minimizing false negatives, providing aneffective fraud prevention framework for financial institutions.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.15066268

Author: VELIDI, SRIRAM CHOWDARY

Language: English

Topic: Imbalanced Data Classification Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W6949478176

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