Vol. 5 No. 1 (2025): Issue 5
Articles

Financial Fraud Detection through K-means Clustering-based unsupervised Learning

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  • Aarav Mehta,
  • Priya Nair,
  • Osman Kaya,
  • Vikram Singh
Aarav Mehta
School of Data Science and Forecasting, Devi Ahilya Vishwavidyalaya, Indore, 452001, India
Priya Nair
Centre for Artificial Intelligence and Applied Research, Savitribai Phule Pune University, Pune, 411007, India
Vikram Singh
Department of Computational Science, Gandhigram Rural Institute, Dindigul, 624302, India

Published 2025-04-17

Keywords

  • Financial Fraud,
  • Fraud Detection,
  • Unsupervised Learning,
  • K-means Clustering,
  • Anomalous Patterns

How to Cite

Mehta, A., Nair, P., Kaya, O., & Singh, V. (2025). Financial Fraud Detection through K-means Clustering-based unsupervised Learning. Economic and Financial Research Letters, 5(1). Retrieved from https://ojs.mri-pub.com/index.php/EFRL/article/view/127

Abstract

Financial fraud is a prevalent issue that poses significant economic threats globally. With the rapid advancements in technology, traditional fraud detection methods are becoming inadequate, necessitating the development of more effective and efficient approaches. The current research landscape in financial fraud detection predominantly relies on supervised learning techniques, facing challenges such as imbalanced datasets and limited scalability. To address these limitations, this paper proposes a novel approach utilizing K-means clustering-based unsupervised learning for financial fraud detection. The innovative framework aims to enhance detection accuracy and scalability while reducing false positives. By leveraging unsupervised learning, the model can detect anomalous patterns without labeled training data, thereby improving fraud detection performance and adaptability in dynamic financial environments.

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