Journal article Open Access

Anomaly Detection Algorithms in Financial Data

Abhisu Jain; Mayank Arora; Anoushka Mehra; Aviva Munshi

Blue Eyes Intelligence Engineering and Sciences Publication(BEIESP)

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.

Files (255.3 kB)
Name Size
255.3 kB Download
Views 14
Downloads 11
Data volume 2.8 MB
Unique views 14
Unique downloads 11


Cite as