Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS
Thinn Wai, Thinn
Unknown Affiliation
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Optimized Feature Engineering for Transaction Fraud Detection Using Sequential and HMM-Based Features Wai Thar, Kaung; Thinn Wai, Thinn
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2025 No. 1 (2025): Proceedings of 2025 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2025i1.529

Abstract

Fraud detection in financial transactions remains a major challenge because fraudulent activities are extremely rare—often described as finding a “needle in a haystack”— and must be detected in real time. This study presents a hybrid feature engineering framework that integrates lightweight sequential indicators with Hidden Markov Model (HMM)-based behavioural features to improve accuracy and interpretability. Using the PaySim dataset containing 2.77 million transactions (0.2965% fraud), we extracted 22 sequential and 14 HMMbased features, from which 28 highly discriminative variables were retained. To address class imbalance, a batch-wise SMOTETomek approach was applied, expanding 1.94 million clean samples to 3.86 million balanced samples. Experimental results show that HMM-based features alone yield moderate performance (ROC AUC = 0.778, F2 = 0.051), but the combined ensemble of tuned XGBoost and LightGBM achieves superior accuracy (ROC AUC = 0.9983, F2 = 0.8431, MCC = 0.827). SHAP analysis identifies HMM-derived entropy and state likelihoods, together with transaction amount dynamics, as key predictors. The results demonstrate that optimized feature engineering plays a crucial role in achieving accurate, scalable, and interpretable fraud detection.
Co-Authors Wai Thar, Kaung