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Explainable and Adaptive Machine Learning for Fraud Risk Detection in Bangladesh's Digital Banking Ecosystem — arXiv:2610.39556

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.39556
License (as archived): CC0-1.0
Archived: 2026-11-01T02:25:09.779Z
SHA-256 of the archived text: 394a7f7bce654bb1bcf47e4269b8ad14708dd905b4329e27d483acdcf5b8a363
Stable URL of this page: https://metraipolis.com/documento/dc_22bd375fa752

How to cite

Explainable and Adaptive Machine Learning for Fraud Risk Detection in Bangladesh's Digital Banking Ecosystem — arXiv:2610.39556 — arXiv (en), https://arxiv.org/abs/2610.39556 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_22bd375fa752 (Metraipolis archive dc_22bd375fa752, sha256 394a7f7bce654bb1bcf47e4269b8ad14708dd905b4329e27d483acdcf5b8a363)

BibTeX

@misc{dc22bd375fa752,
  title = {Explainable and Adaptive Machine Learning for Fraud Risk Detection in Bangladesh's Digital Banking Ecosystem — arXiv:2610.39556},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.39556},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_22bd375fa752; sha256 394a7f7bce654bb1bcf47e4269b8ad14708dd905b4329e27d483acdcf5b8a363},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.39556. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

The rapid expansion of digital banking and mobile financial services creates growing challenges for fraud detection, particularly under severe class imbalance and operational constraints. This study presents an explainable and adaptive machine-learning framework integrating chronological evaluation, leakage-conscious feature engineering, probability calibration, SHAP-based explainability, and adaptive risk prioritization. Experiments use the synthetic PaySim mobile-money benchmark and compare Logistic Regression, Random Forest, XGBoost, and LightGBM. XGBoost achieved the strongest fixed-threshold performance, with PR-AUC of 0.5658, ROC-AUC of 0.9068, and F1-score of 0.5369. Platt calibration reduced the test Brier score from 0.1141 to 0.0245, improving probability reliability. SHAP analysis identified temporal, destination-account, transaction-type, and amount features as important model drivers. An adaptive top-1% policy reduced false positives from 192 to 117, but recall declined from 0.4236 to 0.2122, demonstrating a clear alert-capacity trade-off. The results provide a reproducible methodological framework rather than evidence of deployment performance in Bangladesh, since PaySim is synthetic. Validation using governed real-world financial data is therefore an important direction for future research.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.LG, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2610.39556 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-01T02:12:44.174Z dalla pagina https://arxiv.org/list/cs.LG/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).