Data Mining for Financial Reconciliation and Exception Monitoring: An Anomaly-Detection Framework for Enterprise Financial

Volume 8 of 10 in the Engineering-to-Research Monograph Series. Every financial institution must continuously prove that its data agrees with itself: that transactions in one system match another, that the books reconcile, and that anomalies are caught before they become losses, restatements, or regulatory findings. This work is still largely manual or rule-bound, which does not scale, adapt, or explain itself. This report reframes enterprise reconciliation and exception monitoring as a data-mining and anomaly-detection problem: compare data across systems, learn the shape of normal, flag and rank deviations, and surface explainable, actionable exceptions. It contributes a four-stage architecture, a design principle (reconciliation as explainable anomaly detection), an evaluation of detection approaches, and a resilience mapping drawn from a disaster-recovery plan for a hybrid financial IT environment. Its distinctive claim is that one explainable anomaly detector answers operations, security, and compliance questions at once.
The paper and figures are licensed CC BY 4.0; companion code is released under the MIT License. This work contains no confidential or proprietary employer information.