CatBoost for Fraud Detection in Financial Transactions

Financial fraud is an ever growing menace with severe consequences in the financial industry. Machine learning plays an active role in the fraud detection in financial transactions. However, fraud detection is still a challenging problem due to two major reasons. First, either fraudulent or non-frau...

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Bibliographic Details
Published in2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE) pp. 176 - 179
Main Authors Chen, Yeming, Han, Xinyuan
Format Conference Proceeding
LanguageEnglish
Published IEEE 15.01.2021
Subjects
CatBoost
Consumer electronics
Detection algorithms
Feature extraction
Feeds
Financial industry
Fraud Detection
Knowledge engineering
Machine learning
Memory Compression
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Summary:Financial fraud is an ever growing menace with severe consequences in the financial industry. Machine learning plays an active role in the fraud detection in financial transactions. However, fraud detection is still a challenging problem due to two major reasons. First, either fraudulent or non-fraudulent behaviors change fast and constantly. Secondly, currently online transactions happen so fast, which require detection algorithms to be efficient and accurate. This paper introduces a machine learning method based on CatBoost for fraud detection. To improve detection accuracy, we apply feature engineering to generate highly important features and feed them into CatBoost for classification. Another key contribution of our work is using memory compression to speed up detection. The performance of our method is evaluated on a publicly IEEE-CIS Fraud dataset, provided by Kaggle competition platform. The experimental results demonstrate that our model based on CatBoost has obtained optimal accuracy of 0.983.
DOI:10.1109/ICCECE51280.2021.9342475