AI-AUGMENTED RECONCILIATION OF ACCOUNTING STANDARDS AND TAXATION PRACTICES: AN EMPIRICAL AND PREDICTIVE ANALYSIS IN EMERGING ECONOMIES

Abstract

In emerging economies, interplay between accounting standards and taxation rules significantly impacts the preparation and presentation of financial statements. This research examines whether financial reporting practices are driven by taxation rules or accounting rules and how the professional qualities of accounting staff with education, experience and training affect this orientation. Structural questionnaires were employed to gather data from 280 accounting and auditing practitioners in five emerging economies: India, Nigeria, Vietnam, Indonesia and Kenya. Descriptive analysis and cross-tabulation were used to evaluate prevalent financial reporting method and professional characteristics correlations with reporting orientation. Results show that most of the professionals adopt taxation-first method, illustrating the powerful role played by fiscal structures in directing financial disclosures. Significant number of respondents with a higher level of education, higher levels of experience, and regular professional training demonstrated increased dependence on accounting standards. These findings affirm necessity of an adaptive, context-sensitive model of reporting that acknowledges the joint impact of regulatory frameworks and human capital on financial reporting conduct. This study incorporates AI driven Decision Tree classification to examine patterns in professional direction (accounting-first vs. tax-first) based on gathered demographic and behavioral information. AI model resulted in 98.7 percent classification accuracy, justifying its deployment in forecasting compliance frameworks for emerging economies.

Keywords: Accounting standards, taxation policy, financial reporting, emerging economies, AI and fiscal compliance.