Analysis of Fraud Prediction and Detection Through Machine Learning

Title

Analysis of Fraud Prediction and Detection Through Machine Learning

Creator

Upreti K.; Sharma A.; Khatri V.; Hundekari S.; Gautam V.; Kapoor A.

Description

In today's world the rate of fraudulent activities has significantly elevated, because of which a need for a competent system is required. Among all the fraudulent activities insurance fraud has the most dominating rate of growth. Fraud studies have suggested, that upon identifying the similar characteristics of a fraudulent claim with the claimants, a system of forensic and data-mining technologies for fraud detection can be set up. In this, seek to define fraud and fraudster, and look at the types of fraud and followed by the consequences of fraud to financial systems. As fraud is getting widespread these days epically in the health care insurance system, dealing with this problem has become a necessity. Unsupervised machine learning algorithms such as K-Means clustering along with supervised algorithms used in machine learning, like support vector machines, logistic regression, design trees etc. can play a very vital role in binary class classifications, which would ultimately help in identifying and outreaching the desired goal of fraudulent detection. In the end, this paper specifies the best or the most appropriate model that could be used in the given dataset to produce the most accurate results, based on certain parameters of confusion metrics like accuracy, precision, and specificity. 2023 IEEE.

Source

2023 International Conference on Network, Multimedia and Information Technology, NMITCON 2023

Date

2023-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Genetic Algorithm (GA); Insurance fraud; K-means; Machine Learning

Coverage

Upreti K., CHRIST(Deemed to Be University), Dept. of Computer Science, Ghaziabad, India; Sharma A., Dr. Akhilesh das Gupta Institute of Technology and Management, Department of Cse, Delhi, India; Khatri V., Bpit, Department of Cse, Delhi, India; Hundekari S., Mit Adt University, Pune, India; Gautam V., University of Warwick, United Kingdom; Kapoor A., Department of Cse, Maharaja Surajmal Institute of Technology, Delhi, India

Rights

Restricted Access

Relation

ISBN: 979-835030082-6

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/NMITCON58196.2023.10276042

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175403067&doi=10.1109%2fNMITCON58196.2023.10276042&partnerID=40&md5=e09459c4b53ad6885e518b4937ab3c49