INCORPORATING DATA MINING METHODS FOR ENHANCING FRAUD DETECTION WITHIN FINANCIAL SECTORS

Authors

  • Mrs. M. SHARADA Author
  • J. SRIMEDHA Author
  • U. POOJA Author
  • P. RAHUL Author
  • M. BALAKRISHNA Author

DOI:

https://doi.org/10.5281/zenodo.15575499

Abstract

Fraud detection is a scenario applicable to many industries such as banking and financial sectors, insurance, healthcare, government agencies and law enforcement and more. There has been a drastic increase in recent years, pushing fraud detection more important than ever. Hundreds of millions of dollars are lost to fraud every year. Upcoding fraud is one such fraud in which a service provider acquires additional financial gain by coding a service by upgrading it even though the lesser service has been performed. Incorporating artificial intelligence with data mining and statistics help to anticipate and detect these frauds and minimize costs. Using sophisticated data mining tools, millions of transcations can be searched to spot patterns and detect fraudulent transactions.This paper gives an insight into the various datamining tools which are efficient in detecting upcoding frauds especially in the healthcare insurance sector in India

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Published

02-06-2025

How to Cite

INCORPORATING DATA MINING METHODS FOR ENHANCING FRAUD DETECTION WITHIN FINANCIAL SECTORS. (2025). International Journal of Engineering Research and Science & Technology, 21(2), 1977-1982. https://doi.org/10.5281/zenodo.15575499