Anomaly Detection Algorithms in Financial Data

  • Jain A
  • Arora M
  • Mehra A
  • et al.
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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.




Jain, A., Arora, M., Mehra, A., & Munshi, A. (2021). Anomaly Detection Algorithms in Financial Data. International Journal of Engineering and Advanced Technology, 10(5), 76–78.

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