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Volume 12 Issue 07
July 2026
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Author(s)
Rutuja Pratibha Sahil Ambre
Abstract
This Paper Presents A Lightweight Machine Learning Model For Enterprise Fraud Risk Management (EFRM) In Small And Medium Enterprises (SMEs). Traditional Fraud Detection Systems Are Often Too Resource-intensive For SMEs, Which Face Constraints In Computational Power. We Propose A Model Using Decision Trees, Optimized For Accuracy And Efficiency, Tested On A Synthetic Fraud Detection Dataset. The Results Show That The Model Achieves 85% Accuracy, 80% Precision, And 75% Recall, Demonstrating Its Potential For Efficient Fraud Detection Without Heavy Computational Demands. This Approach Offers SMEs An Accessible Solution For Fraud Risk Management.
Keywords
Machine Learning
Fraud Detection
Enterprise Fraud Risk Management (EFRM)
Small And Medium Enterprises (SMEs)
Lightweight Models
Decision Trees.
Paper ID
IJSARTV11I6103769
Publication Date
June 10, 2025
Research Area
Finance