Accounting Information Reliability in Credit Evaluation by Financial Institutions (gjstudies, 2022)
This research investigates the critical yet underexplored nexus between the perceived reliability of accounting information and its subsequent utilization in creditevaluation models employed by financial institutions. Departing from traditional studies that treat accounting data as a homogeneous, objective input, this paper positsthat reliability is a multidimensional, institutionally-constructed perception that significantly alters risk assessment outcomes. We introduce a novel methodological framework, the Reliability-Weighted Credit Evaluation (RWCE) model, which dynamicallyadjusts financial ratios and metrics based on a composite reliability score. This scoreis derived from a proprietary algorithm analyzing audit quality signals, reporting lag,industry volatility benchmarks, and textual sentiment in management discussion andanalysis (MDA) sections. Our empirical analysis, conducted via a simulation enginebuilt on historical datasets from 1998-2004, demonstrates that integrating explicit reliability metrics reduces Type I (false positive) lending errors by an estimated 18.7%and Type II (false negative) errors by 12.3% compared to conventional models, without a statistically significant increase in model complexity. The findings challenge theimplicit assumption of uniform reliability in financial statement analysis and propose aparadigm shift towards adaptive, reliability-sensitive credit algorithms. This researchcontributes to information economics, behavioral finance, and accounting theory by formalizing the processing of reliability as a distinct, quantifiable variable in automatedfinancial decision-making systems.