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Published June 30, 2021 | Version v1
Journal article Open

Anomaly Detection Algorithms in Financial Data

  • 1. Department of Computer Science and Engineering, Vellore Institute of Technology, Vellore (Tamil Nadu), India.
  • 1. Publisher

Description

The main aim of this project is to understand and apply the separate approach to classify fraudulent transactions in a database using the Isolation forest algorithm and LOF algorithm instead of the generic Random Forest approach. The model will be able to identify transactions with greater accuracy and we will work towards a more optimal solution by comparing both approaches. The problem of detecting credit card fraud involves modelling past credit card purchases with the perception of those that turned out to be fraud. Then, this model is used to determine whether or not a new transaction is fraudulent. The objective of the project here is to identify 100% of the fraudulent transactions while mitigating the incorrect classifications offraud.

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Is cited by
Journal article: 2249-8958 (ISSN)

Subjects

ISSN
2249-8958
Retrieval Number
100.1/ijeat.E25980610521