Traditional detection methods often fail to address

The gap

Evidence profile

Sourced from the stated research gap and future-work section and stated challenges of the source papers, classified as general, spanning 4 journals.

Supporting evidence — 6 representative gaps

• A Machine Learning Approach to Audit Modification Risk Prediction in Financial Reporting: Methods, Data, and Human-Centered Challenges (2026) · Journal of risk and financial management · doi

  Prior fraud-detection studies rely on raw numeric ratios, which limit interpretability. Most approaches frame the problem as a purely algorithmic classification task and offer limited interpretability for auditors, regulators, and decision-makers. The study identifies the need for a human-interpretable approach to audit modification risk prediction.

  generalstated research gap
  Keywords: prior fraud-detection studies rely raw numeric ratios limit
• A Machine Learning Approach to Audit Modification Risk Prediction in Financial Reporting: Methods, Data, and Human-Centered Challenges (2026) · Journal of risk and financial management · doi

  Exploring unstructured data-such as textual disclosures or transaction descriptions-using natural language processing techniques, - Integrating extensive language models with sophisticated classification algorithms, - Investigating the use of machine learning and artificial intelligence-based systems in fraud detection

  generalfuture-work section
  Keywords: exploring unstructured data-such textual disclosures transaction descriptions-using natural
• A Machine Learning Approach to Audit Modification Risk Prediction in Financial Reporting: Methods, Data, and Human-Centered Challenges (2026) · Journal of risk and financial management · doi

  The study identifies the challenge of limited interpretability in prior fraud-detection studies. It notes the difficulty of integrating machine-learning systems into real-world financial reporting and audit-risk assessment workflows. The study highlights the need for human-centered challenges related to model interpretability, decision support, and the integration of machine-learning systems into real-world financial reporting and audit-risk assessment workflows.

  generalstated challenges
  Keywords: study identifies challenge limited interpretability prior fraud-detection studies
• Explainable artificial intelligence in accounting and financial auditing: a systematic review (2026) · Frontiers in Artificial Intelligence · doi

  The need to understand and make transparent the decisions of machine learning models in accounting and financial auditing. The lack of a systematic analysis of the literature on XAI in accounting and financial auditing. The limitations related to computational cost, data quality, explanation stability, and regulatory adaptation.

  generalstated research gap
  Keywords: need understand make transparent decisions machine learning models
• NUMERICAL OPTIMIZATION METHODS FOR DETECTING ANOMALIES IN FINANCIAL TRANSACTIONS: AN INTERPRETABLE HYBRID FRAMEWORK (2026) · Journal of Mathematics Mechanics and Computer Science · doi

  Traditional detection methods often fail to address the dynamic nature of fraud or provide the interpretability required by regulated financial sectors. Key challenges in this context include model convergence, addressing extreme class imbalance, and managing concept drift in nonstationary financial environments.

  generalstated research gapevidence 5/5
  Keywords: traditional detection methods often fail address dynamic nature
• Bibliometric Analysis of Fraud Detection in the Fintech Sector (2026) · West Science Interdisciplinary Studies · doi

  The study identifies a research gap in the field of fraud detection in the fintech sector, particularly in the areas of artificial intelligence and machine learning. The study highlights the need for more research on the development of innovative AI-based fraud detection methods, security based on blockchain technology, and intelligent risk management strategies.

  generalstated research gapevidence 5/5
  Keywords: study identifies research gap field fraud detection fintech

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