A Comprehensive Analysis of Two Decades in Intelligent Surveillance Systems for Fraud Detection Research

Document Type : Research Paper

Authors

1 Department of Information Technology Management, Allameh Tabataba'i University, Tehran, Iran.

2 Department of Industrial Management, Faculty of Management and Accounting, Allameh Tabataba'i University, Tehran, Iran.

3 Department of Accounting, Gävle University, Gävle, Sweden.

Abstract

Objective: The main objective of this research is to conduct a comprehensive study aimed at identifying all the factors affecting the design and optimal performance of intelligent monitoring systems for fraud detection. This includes a detailed analysis of the types of financial fraud, the types of data used for fraud detection systems (both financial and non-financial), and identifying the most effective machine learning and deep learning algorithms. In addition, the aim of this research is to establish appropriate criteria to measure their effectiveness and identify challenges in the design of intelligent monitoring systems. Finally, this research seeks to predict the future research process in order to respond to these challenges.
Method: This research, using a descriptive research method, carefully reviewed sources including articles published in international journals, conference papers, and especially articles indexed in the Scopus database and reputable domestic journals. The time range covers a 20-year period ending in 2022. To ensure the accuracy and precision of the analysis, MAXQDA software was used for coding and analysis sheets, while VOS Viewer software was used for keyword analysis and comprehensive research mapping.
Results: This study, by reviewing fraud detection systems research over the past two decades, has led to the presentation of a conceptual model for future fraud detection research that, by integrating financial and non-financial data, including environmental, social, and governance (ESG) criteria, causes a serious paradigm shift in research in this field. This approach increases the accuracy and transparency of fraud detection systems by removing existing limitations in fraud detection and by considering a wider range of variables.
Conclusion: This study contributes to the expansion of the field of knowledge by analyzing previous research conducted in this field and identifying future challenges and trends. The output of this study includes a detailed analysis of the current challenges in designing intelligent surveillance systems and extracting future research trends. By addressing these challenges and trends, future research can significantly improve the design and implementation of intelligent surveillance systems and ensure their effectiveness in detecting and preventing financial fraud.

Keywords

Main Subjects

References

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  • Receive Date: 28 September 2023
  • Revise Date: 18 November 2023
  • Accept Date: 20 November 2023

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