Lightweight Machine Learning Model For Fraud Risk Management In Smes

• Author(s):

  Rutuja Pratibha | Sahil Ambre

• Keywords:

  Machine Learning, Fraud Detection, Enterprise Fraud Risk Management (EFRM), Small And Medium Enterprises (SMEs), Lightweight Models, Decision Trees.

• 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.

---

Other Details

• Paper id:

  IJSARTV11I6103769

• Published in:

  Volume: 11 Issue: 6 June 2025

• Publication Date:

  2025-06-10

Download Article