Matin N. Ashtiani
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Matin N. Ashtiani has a total of 2 co-authors. Here are the top co-authors by number of shared publications:
Bijan Raahemi
3 shared publications- Bijan Raahemi
Knowledge Discovery and Data Mining Lab, Telfer School of Management, University of Ottawa, Ottawa, ON K1N 6N5, Canada
1 shared publication
Latest Publications
- Open Access
- 27 July 2026
- Published by Elsevier in Machine Learning with Applications
- Vol. 25
Enhanced cost-sensitive ensemble models with performance-weighted ensemble feature selection for financial statement fraud detection
- 27 July 2026
- Published by Elsevier in Machine Learning with Applications
- Vol. 25
Open AccessEnhanced cost-sensitive ensemble models with performance-weighted ensemble feature selection for financial statement fraud detection
- Open Access
- 1 January 2026
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 14, 115211-115230
An Evidence-Driven Hybrid Architecture for Financial Statement Fraud Detection With Long-Context Transformers
- 1 January 2026
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 14, 115211-115230
Open AccessAn Evidence-Driven Hybrid Architecture for Financial Statement Fraud Detection With Long-Context Transformers
- Top Cited Papers
- 9 January 2023
- Published by Elsevier in Expert Systems with Applications
- Vol. 217
News-based intelligent prediction of financial markets using text mining and machine learning: A systematic literature review
- 9 January 2023
- Published by Elsevier in Expert Systems with Applications
- Vol. 217
Top Cited PapersNews-based intelligent prediction of financial markets using text mining and machine learning: A systematic literature review
- Open Access
- 13 July 2021
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 10, 72504-72525
Intelligent Fraud Detection in Financial Statements Using Machine Learning and Data Mining: A Systematic Literature Review
Fraudulent financial statements (FFS) are the results of manipulating financial elements by overvaluing incomes, assets, sales, and profits while underrating expenses, debts, or losses. To identify such fraudulent statements, traditional methods, including manual auditing and inspections, are costly, imprecise, and time-consuming. Intelligent methods can significantly help auditors in analyzing a large number of financial statements. In this study, we systematically review and synthesize the existing literature on intelligent fraud detection in corporate financial statements. In particular, the focus of this review is on exploring machine learning and data mining methods, as well as the various datasets that are studied for detecting financial fraud. We adopted the Kitchenham methodology as a well-defined protocol to extract, synthesize, and report the results. Accordingly, 47 articles were selected, synthesized, and analyzed. We present the key issues, gaps, and limitations in the area of fraud detection in financial statements and suggest areas for future research. Since supervised algorithms were employed more than unsupervised approaches like clustering, the future research should focus on unsupervised, semi-supervised, as well as bio-inspired and evolutionary heuristic methods for anomaly (fraud) detection. In terms of datasets, it is envisaged that future research making use of textual and audio data. While imposing new challenges, this unstructured data deserves further study as it can show interesting results for intelligent fraud detection.
- 13 July 2021
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 10, 72504-72525
Open AccessIntelligent Fraud Detection in Financial Statements Using Machine Learning and Data Mining: A Systematic Literature Review
Fraudulent financial statements (FFS) are the results of manipulating financial elements by overvaluing incomes, assets, sales, and profits while underrating expenses, debts, or losses. To identify such fraudulent statements, traditional methods, including manual auditing and inspections, are costly, imprecise, and time-consuming. Intelligent methods can significantly help auditors in analyzing a large number of financial statements. In this study, we systematically review and synthesize the existing literature on intelligent fraud detection in corporate financial statements. In particular, the focus of this review is on exploring machine learning and data mining methods, as well as the various datasets that are studied for detecting financial fraud. We adopted the Kitchenham methodology as a well-defined protocol to extract, synthesize, and report the results. Accordingly, 47 articles were selected, synthesized, and analyzed. We present the key issues, gaps, and limitations in the area of fraud detection in financial statements and suggest areas for future research. Since supervised algorithms were employed more than unsupervised approaches like clustering, the future research should focus on unsupervised, semi-supervised, as well as bio-inspired and evolutionary heuristic methods for anomaly (fraud) detection. In terms of datasets, it is envisaged that future research making use of textual and audio data. While imposing new challenges, this unstructured data deserves further study as it can show interesting results for intelligent fraud detection.