Designing an Intelligent Model for Detecting Financial Anomalies in Organizational Transactions Using Unsupervised Learning Algorithms
Abstract
This study aimed to design an intelligent model for detecting financial anomalies in organizational transactions by emphasizing unsupervised learning algorithms, data requirements, expert validation, and financial governance mechanisms. This study employed a qualitative, applied-developmental design. The participants included 18 experts in finance, internal auditing, risk management, data analytics, and financial information technology from large organizations located in Tehran. Participants were selected through purposive and snowball sampling. Data were collected only through semi-structured interviews, and the interview process continued until theoretical saturation was achieved. Thematic analysis was used to analyze the data through open, axial, and selective coding with the support of NVivo software. The credibility and dependability of the findings were strengthened through member checking, peer review, audit trail documentation, and constant comparison of codes and categories. The analysis of interviews resulted in five main categories: data architecture and transaction integration, algorithmic capabilities of unsupervised learning, interpretability and human validation, risk governance and internal control, and organizational implementation requirements. Participants emphasized that an intelligent anomaly detection model can be effective only when financial, operational, and behavioral data are integrated, cleaned, traceable, and analytically usable. They also stated that algorithms such as clustering, DBSCAN, Local Outlier Factor, Isolation Forest, and autoencoders can identify unusual transaction patterns without relying on labeled fraud data. However, algorithmic outputs should be interpreted, prioritized, and validated by financial experts, internal auditors, and risk management units. The proposed model indicates that intelligent detection of financial anomalies in organizational transactions is not merely a technical issue; rather, it requires alignment among data quality, algorithmic design, accounting knowledge, internal control, data governance, and organizational accountability. Unsupervised learning can enhance early detection of errors, misuse, control circumvention, and atypical transaction patterns, provided that it is implemented alongside human, ethical, and organizational oversight mechanisms.
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References
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