Asset Details
Do you wish to reserve the book?
Dynamic Calibration of Decision Thresholds for Financial Anomaly Detection: Verification With Payment Platform Information and Data
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Dynamic Calibration of Decision Thresholds for Financial Anomaly Detection: Verification With Payment Platform Information and Data
Do you wish to request the book?
Dynamic Calibration of Decision Thresholds for Financial Anomaly Detection: Verification With Payment Platform Information and Data
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Dynamic Calibration of Decision Thresholds for Financial Anomaly Detection: Verification With Payment Platform Information and Data
2025
Overview
Digital payment channels have expanded quickly, reshaping transaction flows while opening new avenues for fraud. Isolation Forest (IF) remains attractive for unsupervised screening, yet deployments that rely on a fixed anomaly-score threshold deteriorate when traffic shifts or is actively manipulated. The authors present a Temporal-Attention Isolation Forest with Dynamic Calibration (TA-IFDC) that treats threshold selection as an adaptive component rather than a static post-processing step. The method monitors the evolving distribution of IF scores in streaming mode and updates the decision boundary online, while a lightweight temporal-attention module encodes short-range dependencies across consecutive transactions. Together, these pieces allow the detector to adjust to drift without sacrificing precision during stable periods.
