About the Journal

 

Bulletin of Data-Driven Accounting and State Finance (BODDASF) is a peer-reviewed, open-access international journal dedicated to publishing pioneering research at the nexus of advanced data analytics, public sector accounting, and national fiscal management. Positioned as a premier scholarly forum for the digital transformation of state finance, the bulletin serves accounting scholars, data scientists, public finance economists, government auditors, and fiscal policymakers worldwide. BODDASF prioritizes high-impact empirical research, predictive financial modeling, econometric data analyses, algorithmic audit evaluations, and case studies on digital taxation and state budgeting. By synthesizing core public accounting principles with big data, artificial intelligence, and modernized financial systems, the bulletin fosters a comprehensive understanding of how data-driven methodologies enhance fiscal transparency, prevent fraud, and optimize the management of state wealth and public resources.

Reflecting its mandate to champion analytical rigor and technological innovation in national financial management, the bulletin welcomes high-quality original research articles, systematic literature reviews, computational econometric studies, and applied data evaluations within the following thematic areas:

Data Analytics in Public Accounting & Auditing: Big data applications in government financial reporting, artificial intelligence in continuous auditing, and automated anomaly detection in state expenditures.

State Finance & Predictive Fiscal Policy: Data-driven revenue forecasting, algorithmic tax administration and compliance monitoring, digital state budgeting, and predictive modeling for public debt management.

Financial GovTech & Information Systems: Cloud-based government accounting systems, blockchain for transparent state fund tracking, interoperable financial databases, and cybersecurity in state financial infrastructure.

Forensic Accounting & Fraud Analytics: Digital forensics in state-owned enterprises (SOEs), machine learning algorithms for detecting financial irregularities, and data-backed anti-corruption frameworks.

Performance Measurement & State Wealth Management: Data-driven value-for-money (VfM) evaluations, algorithmic performance-based budgeting, and real-time financial analytics for state asset optimization.

To ensure rigorous editorial oversight while facilitating the timely dissemination of highly relevant financial and computational research, BODDASF is published semi-annually (2 issues per year, typically scheduled for release in June and December). Special thematic issues focusing on machine learning in tax fraud detection, the integration of big data in national budgeting, or algorithmic accountability in the public sector may be commissioned by the Editorial Board.

BODDASF enforces a strict double-blind peer review policy to maintain the highest standards of academic integrity, analytical objectivity, and empirical validity across accounting, economics, and data science disciplines.

Initial Desk Evaluation: Every submission undergoes a preliminary screening by the Editor-in-Chief or a relevant Subject Editor to assess thematic alignment, methodological soundness (algorithmic validity, econometric rigor, dataset accuracy, or conceptual logic), and structural compliance. Plagiarism screening via Turnitin/iThenticate is strictly executed at this stage, requiring a similarity index below the bulletin's maximum threshold.

Expert Peer Review: Manuscripts passing the desk evaluation are anonymized and assigned to at least two independent international reviewers who are established academic researchers, senior government auditors, or financial data scientists.

Editorial Decision: Based on the reviewers' recommendations, the Editorial Board renders one of four decisions: Acceptance, Revisions Required (Minor/Major), Resubmit for Review, or Rejection. The final decision rests entirely on intellectual merit, methodological clarity, and the potential for tangible impact on data-driven state financial practices.