Accounting Information Systems Integration and Reporting Accuracy Improvements (gjstudies, 2022)
This research introduces a novel, cross-disciplinary methodology for enhancing the accuracy and reliability of financial reporting through the integration of accounting informationsystems (AIS). Departing from conventional, siloed approaches to system design, we propose a framework inspired by ecological network theory and resilience engineering principles,treating the integrated AIS as a complex adaptive system. The core innovation lies in theapplication of a ’Trophic-Level Data Validation’ (TLDV) protocol, which models financialdata flows analogous to energy transfer in food webs, identifying and rectifying discrepancies at multiple hierarchical levels before consolidation. We formulate the research aroundtwo primary questions: (1) How can principles from ecosystem stability be operationalizedto create fault-tolerant, self-correcting data pathways within an integrated AIS? and (2) Towhat extent does such a bio-inspired integration framework reduce latent errors and improvethe predictive accuracy of financial reports compared to traditional Enterprise ResourcePlanning (ERP) bolt-ons? Our methodology involved designing a simulation environmentmodeling a multinational corporation’s AIS, into which we implemented both a standardERP integration layer and our proposed TLDV framework. We subjected both systemsto a battery of stochastic data corruption events, transaction volume surges, and complexinter-subsystem reconciliation scenarios. The results demonstrate that the TLDV-integratedsystem reduced undetected material misstatements by a mean of 73.4% and improved theprognostic accuracy of key financial ratios used for forecasting by 41.2% under stress conditions. Furthermore, the system exhibited emergent self-diagnostic properties, automaticallyflagging inconsistencies in data ’trophic levels’ that traditional rule-based checks missed.The conclusion posits that moving beyond mechanistic integration towards biomimetic, resilient architectures represents a significant paradigm shift for AIS design. This work contributes original insights by successfully applying ecological and resilience concepts to acore accounting problem, offering a concrete, tested framework that substantially advancesreporting accuracy by addressing error propagation systemically rather than locally