aspg-37-4449-1792223273 20261017074753 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Financial Technology and Innovation FinTechI 2836-5372 2025 5 2 N. Metawa College of Business Administration, University of Sharjah, United Arab Emirates Iman Akour Department of Information Systems, University of Sharjah, United Arab Emirates Rania Itani Murdoch University Dubai, United Arab Emirates A key measure of financial digital transformation is the adoption, cost efficiency, channel migration, and transaction growth. They are all indicators of platform reach, but they provide little insight into what happens when a mishandled platform fails to provide a customer with their service, causes a customer to question their transactions, fails to inform a customer how much they are being charged, or has vulnerabilities that cause a security incident or disables the service for a customer due to restrictions. In this paper, a framework for platform recovery is designed that connects the type of digital-service failure to the depth of the institutional response to the failure, taking friction into account. Complaint narratives are symbolized by interpretable latent themes and intertwined with structured intake information in a chronologically validated response model. The predicted response depth provides a case-mix benchmark and recovery is compared with this benchmark at the company-product level, where results from the small samples are limited by empirical-Bayes shrinkage. The out-of-time evidence demonstrates that adding narrative themes modestly yet consistently to structured platform information boosts monetary-relief precision–recall area from 0.373 to 0.381 and any-relief precision–recall area from 0.519 to 0.525. The depth of recovery is different from material to material within the friction layers. The monetary-relief complaints rate is the highest, while the complaints rate for trust and security is the highest for the explanation closure. In units that are adequately observed, risk-adjusted alignment distinguishes recovery leaders from constrained, trust-intensive, and transaction-intensive types of units. The framework will go beyond just counting the number of complaints and relieve, and ask the more defensible question: Was the response from a financial platform deeper or shallower than what the service friction and case mix would reasonably suggest? It is not designed for automated adjudication or public ranking that would be part of a government system. 2025 10 19 4449 https://creativecommons.org/licenses/by/4.0/ 10.54216/FinTech-I.050202 https://americaspg.com/journal/37/article/4449 https://americaspg.com/storage/articles/manuscripts/01M2RQ6FSS652W5ZDSWXT1RVZ7.pdf