Reattributing Recovery Costs Arising from Digital Process Failures in the Costing of Banking Service Channels

Document Type : Research

Author
Department of Accounting, Energy Institute of Higher Education (Non-Profit, Non-Governmental), Saveh, Iran.
10.22034/smajournal.2026.603316.1314
Abstract
This analytical-applied study develops a decision-oriented management-accounting framework for recovery costs generated by traceable digital-process failures in multichannel banking. The framework distinguishes where resources are consumed from the event that generated demand for those resources while preserving total organizational cost. A downstream activity is reattributed only when the originating failure is identifiable, the recovery activity is traceably linked to that failure, and the activity would have been avoidable had the digital process performed as designed. The model therefore retains a location-of-consumption view for capacity and operational accountability, adds a source-of-demand view for process economics, and separates capacity consumption from the portion of cost that is actually avoidable within the relevant decision horizon. The proposed framework is neither a new costing system nor an accounting transfer; rather, it provides a secondary managerial classification for decision-making purposes. Scenario analysis shows that economic significance depends jointly on recovery frequency and recovery intensity: even a low frequency of recovery-generating failures may be decision-relevant when transaction volume is high or recovery is costly. Reattribution can also reverse the ranking of alternative service designs, change process-improvement priorities, and prevent released capacity from being mistaken for immediate cash savings. The contribution lies in providing a bounded, reproducible, and empirically testable rule that links resource consumption, the event generating recovery demand, and decision-relevant cost avoidability. The framework is intended to support digital-investment appraisal, channel performance evaluation, customer profitability analysis, and process-improvement prioritization and can be tested empirically using event-log and cost data.
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Articles in Press, Corrected Proof
Available Online from 27 October 2026