Work

Credit Card Fraud Detection in Transaction By Using Random Forest Algorithm With Data Sampling

Year: 2026

Type: article

Abstract: There are various fraudulent activities detection techniques has implemented in credit card transactions have been kept in researcher minds to methods to develop models based on artificial intelligence , data mining, fuzzy logic and machine learning. Credit card fraud detection is significantly difficult, but also popular problem to solve. This paper we proposed an classification techniques which takes dataset of credit card transactions containing 284,807 transactions records which has a data imbalance problem which is the total number of a class of data (positive) is far less than the total number of another class of data (negative). In these we proposed various data sampling techniques applied on random forest algorithms and calculate the accuracy of the model based on various sampling techniques such as oversampling, undersampling and ROSE sampling.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.23839767

Authors: Shraddha Jain, Dr. Sunil Phulre, Dr. Bhupesh Gour

Language: English

Topic: Imbalanced Data Classification Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7224990502

FWCI:

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Related to: 10