Volume 5 • Issue 2 • PP: 01–09 • 2025
Remedy-Aware Financial Innovation: A Hierarchical RegTech Framework for Consumer Complaint Resolution
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© 2025 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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Financial complaint systems keep track of instances of service failure as well as of the remedy chosen by the responding institution. The crucial question for operational supervision isn’t just one of categorization of complaints, however; compensation is scarce, expensive, and fits into a larger framework of explanation vs. relief. In this paper, an approach for the development of a regulatory technology framework for remedies is proposed, which conceptualises the resolution of complaints as a hierarchy. The first stage determines if a case will be decided on relief or explanation, and the second stage distinguishes monetary from non-monetary relief. This is done by using sparse and regularized models, which are calibrated on the following validation period, and tested on an untouched chronological holdout, combining structured intake attributes with complaint narratives. In the empirical application, 268,570 complaints were received by California in 2024. Monetary relief makes up 1.04% of independent test period, and accuracy is not the best criterion. The hierarchical model yields a monetary-relief precision-recall area of 0.335, whereas the flat text-tabular model and a flat model with the event prevalence yield 0.314 and 0.010, respectively. At the 2 percent review budget, it is able to identify 58.9 percent of monetary-relief cases and has a lift of 29.4 times over a random review. The flat fusion model is slightly better for overall three-class classification, demonstrating the benefit of a hierarchy of remedies when institutional capacity is focused on rare, consequential outcomes, rather than labelling. Results provide a practical design of human-supervised complaint triage while retaining calibration, interpretability, chronological validation, and clear usage limitations for automated complaint analysis.
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