Platform Recovery Alignment in Financial Digital Transformation:

A Friction-Aware Benchmarking Framework for Digital Finance

Services

N. Metawa1,* Iman Akour2 Rania Itani3

1 College of Business Administration, University of Sharjah, UAE

2 Department of Information Systems, University of Sharjah, UAE

3 Murdoch University Dubai, UAE

Received: March 26, 2025 Revised: May 20, 2025 A c⋆ c Cep otrerde:s poAnudginugs ta ut2h8o,r 2025

ABSTRACT

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.

Keywords: Financial digital transformation Digital finance platforms Service recovery Complaint analytics

Platform governance Text mining RegTech