Feature engineering and explainable machine learning for credit card fraud detection: evidence from temporal transaction data

Avaz qizi, Mahmudova Dilnoza

Innovative Multidisciplinary Journal of Applied Technology · 2026-yil

Annotatsiya

Credit card fraud detection is challenging because individual transaction attributes may not reveal whether current activity is unusual relative to previous behavior. This study evaluates an expanded feature representation for XGBoost under temporal holdout evaluation. Two identically configured models were compared on 1,852,394 transactions: a baseline using four original variables and an engineered model adding ten historical, temporal, geographic, demographic, and merchant-related features. Average Precision increased from 0.6762 to 0.9299, a difference of +0.2537 (paired class-stratified bootstrap 95% CI: [0.2349, 0.2712]). F1-score, MCC, precision, recall, ROC-AUC, and Recall@5% also improved. SHAP ranked transaction amount, seven-day historical average amount, transaction hour, 24-hour transaction frequency, and city population highest. Permutation analysis supported the predictive utility of the current-amount-to-historical-average ratio. Historical features excluded current and simultaneous transactions; test merchant risk used training labels only. The findings support richer feature representation in the evaluated setting. However, all ten engineered features were introduced jointly, preventing attribution of the total improvement to transaction-history features or any single variable. Single-holdout evaluation and unmodeled fraud-label delays limit generalization.

Maqola ma’lumotlari
MualliflarAvaz qizi, Mahmudova Dilnoza
JurnalInnovative Multidisciplinary Journal of Applied Technology
NashriyotТДТУ Қўқон филиали
ISSN2995-486X, 2995-486x
Nashr sanasi2026-10-07
Jild4
Son10
Betlar42-48
TilIngliz
Ilmiy sohaTexnika
DOI10.51699/q78r8348
Indeksga qo‘shilgan2026-10-10

DOI: 10.51699/q78r8348 · Maqolaning asl sahifasi

Kalit so‘zlar

credit card fraud detection, feature engineering, transaction history, XGBoost, explainable machine learning, SHAP, imbalanced classification

Ilmiy soha

Texnika

Iqtibos keltirish

APAAvaz qizi, Mahmudova Dilnoza (2026). FEATURE ENGINEERING AND EXPLAINABLE MACHINE LEARNING FOR CREDIT CARD FRAUD DETECTION: EVIDENCE FROM TEMPORAL TRANSACTION DATA. Innovative Multidisciplinary Journal of Applied Technology, 4(10), 42-48. https://doi.org/10.51699/q78r8348
GOST (OAK)Avaz qizi, Mahmudova Dilnoza FEATURE ENGINEERING AND EXPLAINABLE MACHINE LEARNING FOR CREDIT CARD FRAUD DETECTION: EVIDENCE FROM TEMPORAL TRANSACTION DATA // Innovative Multidisciplinary Journal of Applied Technology. – 2026. – T. 4, № 10. – P. 42-48. – DOI: 10.51699/q78r8348.
MLAAvaz qizi, Mahmudova Dilnoza. “FEATURE ENGINEERING AND EXPLAINABLE MACHINE LEARNING FOR CREDIT CARD FRAUD DETECTION: EVIDENCE FROM TEMPORAL TRANSACTION DATA.” Innovative Multidisciplinary Journal of Applied Technology, 4(10), 42-48, 2026. https://doi.org/10.51699/q78r8348
ChicagoAvaz qizi, Mahmudova Dilnoza. “FEATURE ENGINEERING AND EXPLAINABLE MACHINE LEARNING FOR CREDIT CARD FRAUD DETECTION: EVIDENCE FROM TEMPORAL TRANSACTION DATA.” Innovative Multidisciplinary Journal of Applied Technology, 4(10), 42-48 (2026). https://doi.org/10.51699/q78r8348
HarvardAvaz qizi, Mahmudova Dilnoza (2026) ‘FEATURE ENGINEERING AND EXPLAINABLE MACHINE LEARNING FOR CREDIT CARD FRAUD DETECTION: EVIDENCE FROM TEMPORAL TRANSACTION DATA’, Innovative Multidisciplinary Journal of Applied Technology, 4(10), 42-48. https://doi.org/10.51699/q78r8348
BibTeX
@article{avazqizimahmudovadilnoza2026,
  title = {FEATURE ENGINEERING AND EXPLAINABLE MACHINE LEARNING FOR CREDIT CARD FRAUD DETECTION: EVIDENCE FROM TEMPORAL TRANSACTION DATA},
  author = {Avaz qizi, Mahmudova Dilnoza},
  journal = {Innovative Multidisciplinary Journal of Applied Technology},
  year = {2026},
  volume = {4},
  number = {10},
  pages = {42-48},
  doi = {10.51699/q78r8348},
  url = {https://doi.org/10.51699/q78r8348}
}

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