Big Data in Financial Fraud Detection: A Machine Learning Approach

This paper looks at how Big Data and Machine Learning can help detect financial fraud moreeffectively. Older fraud detection systems often miss new and complex fraud patterns. In this study,three different models—Logistic Regression, Random Forest, and Neural Network—were tested usingreal transaction data. The Neural Network gave the best results in catching fraud accurately. Thesefindings suggest that banks should start using smarter, AI-based systems to fight financial fraud.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-06-15
DOI
https://doi.org/10.5281/zenodo.15668194
Primary Topic
Imbalanced Data Classification Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

Big Data in Financial Fraud Detection: A Machine Learning Approach

Zenodo (CERN European Organization for Nuclear Research)
Imbalanced Data Classification Techniques
article

Big Data in Financial Fraud Detection: A Machine Learning Approach

article en

Abstract

This paper looks at how Big Data and Machine Learning can help detect financial fraud moreeffectively. Older fraud detection systems often miss new and complex fraud patterns. In this study,three different models—Logistic Regression, Random Forest, and Neural Network—were tested usingreal transaction data. The Neural Network gave the best results in catching fraud accurately. Thesefindings suggest that banks should start using smarter, AI-based systems to fight financial fraud.

Zenodo (CERN European Organization for Nuclear Research)
Peace, Justice and strong institutions
Openalex Percentile: Top 75%
Imbalanced Data Classification Techniques
DOIOpen Access PDFOpenAlex
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Big Data in Financial Fraud Detection: A Machine Learning Approach · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS