Interpretable Electronic Transfer Fraud Detection with Expert Feature Constructions

Yu Yen Hsin*, Tian Shyr Dai, Yen Wu Ti, Ming Chuan Huang

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

3 Scopus citations


Since the magnitude of financial frauds grow rapidly with low clearance rates, detecting and avoiding frauds has been a tremendous challenge for financial institutions. Both the detection performance and interpretability are critical for fraud detection to profile the fraudsters' modus operandi and to spot vulnerabilities of financial systems/processes. Traditional rule-based approaches yield poor detection performances. Recent machine learning methods basically generate recency, frequency, and temporal features to extract patterns from raw transaction data. On the other hand, this paper generates behavioral and (financial organization's) segmentation features based on financial expertise and characteristics solely belonging to (non)-fraudulent accounts. While inputting aforementioned features into different models and using accumulated features from past literature generate unstable prediction results, our features generate the best and stable results for the decision-tree-base approach like Extreme Gradient Boosting and Light Gradient Boosting Machine. By using Kolmogorov-Smirnov test, we discover the instable predictive results are caused by vastly different distributions of features that reflects the fast-changing modus operandi in the training/testing sets. Thus, generating training/testing sets by random sampling (compared to chronological separation) is improper for modeling time varying data. Combining XGBoost with our expertise-based features provides clear causal-effect between features and fraudulent labels for further interpretations. The high precision and recall rates allow banks to save screening labor costs and identify frauds without interfering with normal transactions. The quality of our features can be examined by showing that they occupy three out of the five most important features under the ranking procedure in a premium finance publication by Butaru et al. [Journal of Banking and Finance (72) 218-239 (2016)].

Original languageEnglish
JournalCEUR Workshop Proceedings
StatePublished - 2021
Event2021 International Conference on Information and Knowledge Management Workshops, CIKMW 2021 - Gold Coast, Australia
Duration: 1 Nov 20215 Nov 2021


  • Boosted decision tree
  • Electronic transfer fraud detection
  • Feature engineering
  • Interpretability


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