Volume 6 • Issue 1 • PP: 01–08 • 2026
Adaptive RegTech for Financial Complaint Operations: Temporal Institutional Risk Signals Outperform Text–Tabular Fusion in Predicting Untimely Responses
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© 2026 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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While the innovation in the financial sector can be measured by the products it offers customers, many valuable innovations are generated by operational technologies that facilitate the regulatory system to respond more quickly. This research designs and pilots an adaptive regulatory-technology approach to prioritize consumer complaints at high risk of an untimely institutional response. A leakage-safe chronological design is used to compare four approaches: static historical institutional-risk benchmark, short-horizon rolling-risk score, interpretable structured classifier, and text–tabular fusion classifier. Selection and calibration of models come before a non-biased one-month holdout period for evaluation. Untimely responses are very uncommon, and performance is evaluated based on precision–recall discrimination, calibration, and lift and recall under fixed review-capacity constraints, not just on accuracy. The seven-day rolling institutional-risk score proved to be the best performing operational group at a 2% review budget, identifying 75.7% of cases that arrive late with 9.9% accuracy, a 37.8-fold lift over random review. When the review budget was increased to 5%, 92.8% of cases that were untimely were captured. Unlike a common belief in financial NLP, incorporating any complaint-language indicator failed to improve financial ranking results: The fused model performed worse than any of the institutional-risk dynamic or static benchmarks. When service failures are concentrated in institutions, the findings indicate that the operational state near to the time of the failure could convey more information than richer complaint content. The suggested framework provides a clear low cost human-in-the-loop RegTech solution that allows to channel limited compliance focus and maintain auditability and chronological validity.
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