The need to understand and make transparent the decisions

The gap

Evidence profile

Sourced from the stated research gap and stated challenges of the source papers, classified as general, spanning 3 journals.

Supporting evidence — 4 representative gaps

• 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
• 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 gapevidence 5/5
  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

  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 challengesevidence 5/5
  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 gapevidence 5/5
  Keywords: need understand make transparent decisions machine learning models

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