(untitled) (gjstudies, 2026)
This research introduces a novel, hybrid methodological framework for integrating predictive analytics into Accounting-Based Decision Support Systems (ABDSS), moving beyond traditional descriptive and diagnostic capabilities. The studyaddresses a significant gap in the literature by proposing a cross-disciplinary approach that synthesizes principles from computational finance, behavioral accounting, and evolutionary algorithm design to create adaptive, self-optimizing predictive models. Unlike conventional applications that focus on historical financial dataextrapolation, our methodology, termed the Adaptive Predictive Synthesis (APS)framework, incorporates non-traditional data streams—including unstructured textual data from managerial communications, real-time market sentiment indicators,and intra-organizational process metadata—to forecast accounting-relevant outcomes such as earnings management risk, audit anomaly likelihood, and long-termsolvency trajectories. The core innovation lies in a two-tiered neural-symbolic architecture where a deep learning component handles pattern recognition in complex,high-dimensional data, and a symbolic reasoning layer, governed by a rule-set derived from Generally Accepted Accounting Principles (GAAP) and InternationalFinancial Reporting Standards (IFRS), ensures predictive outputs remain interpretable and grounded in accounting doctrine. We formulate and investigate threeoriginal research questions concerning the efficacy of hybrid models in detectinglatent financial distress signals, the impact of model interpretability on accountant trust and system adoption, and the framework’s ability to adapt to regulatorychanges. Our empirical validation, conducted via a simulated accounting ecosystem and a longitudinal case study, demonstrates that the APS framework achievesa 23.7% higher precision in predicting quarterly earnings deviations compared tostandard time-series models, while significantly enhancing user confidence throughits explainable AI components. The findings contribute a new, principled architecture for next-generation AB-DSS, challenge the prevailing black-box paradigm infinancial analytics, and provide a foundation for regulatory-compliant, transparent,and adaptive predictive tools in accounting practice