Detecting Financial Statement Irregularities: Hybrid Benford-Outlier-Process-Mining Anomaly Detection Architecture

• Author(s): Chizoba Michael Okafor; Blessing Olajumoke Farounbi; Ogochukwu Prisca Onyelucheya; Omoize Fatimetu Dako
• Paper ID: 1711007
• Page: 312-327
• Published Date: 30-11-2019
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 3 Issue 5 November-2019

Abstract

Financial statement irregularities continue to pose significant challenges for organizations, auditors, and regulators, often resulting in financial misreporting, regulatory sanctions, and erosion of investor confidence. Traditional detection methods, including manual audits and standard statistical tests, have proven insufficient to address complex and high-volume financial data. This paper proposes a hybrid anomaly detection architecture combining Benford?s Law, statistical outlier detection, and process mining techniques to identify irregularities in financial statements. By synthesizing insights from prior literature in accounting, auditing, data analytics, and process management, the study presents a conceptual framework for detecting anomalies across transactional, operational, and reporting layers. The proposed approach leverages the strengths of each detection method: Benford?s Law for assessing digit distribution conformity, outlier detection for identifying numerical deviations, and process mining for uncovering irregular process sequences. The integration of these techniques offers a multi-dimensional anomaly detection system, enhancing the ability to flag potential fraud, errors, or reporting inconsistencies. This paper further explores the implications of such hybrid frameworks for audit quality, financial governance, and regulatory oversight. Recommendations for future research include empirical validation, adaptation to cross-jurisdictional financial regulations, and integration with machine learning for predictive analytics.

Keywords

Financial Statement Irregularities, Hybrid Anomaly Detection, Benford?s Law, Outlier Detection, Process Mining, Audit Analytics

Citations

IRE Journals:
Chizoba Michael Okafor, Blessing Olajumoke Farounbi, Ogochukwu Prisca Onyelucheya, Omoize Fatimetu Dako "Detecting Financial Statement Irregularities: Hybrid Benford-Outlier-Process-Mining Anomaly Detection Architecture" Iconic Research And Engineering Journals Volume 3 Issue 5 2019 Page 312-327

IEEE:
Chizoba Michael Okafor, Blessing Olajumoke Farounbi, Ogochukwu Prisca Onyelucheya, Omoize Fatimetu Dako "Detecting Financial Statement Irregularities: Hybrid Benford-Outlier-Process-Mining Anomaly Detection Architecture" Iconic Research And Engineering Journals, 3(5)