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

Objective: The primary objective of this research is to conduct a comprehensive analysis of the evolution, effectiveness, and future potential of intelligent surveillance systems in fraud detection over the past two decades. Fraudulent activities have become increasingly complex, requiring equally sophisticated countermeasures. At the forefront of this battle against financial malfeasance are intelligent fraud detection systems. This research embarks on a profound exploration of studies undertaken in this domain. Beyond merely identifying fraudulent transactions, these systems play a crucial role in upholding the financial integrity of organizations and fostering confidence among investors and other stakeholders. The aftermath of fraud can be devastating, potentially destabilizing financial institutions, intensifying investment volatility, and negatively influencing economic health. Through an innovative approach, this study delves into the types of frauds detected, the data sources utilized, which encompass both financial and non-financial metrics, and examines the advanced machine learning and deep learning algorithms utilized in these systems. A significant facet of this research is understanding the metrics that define the effectiveness of these intelligent systems. Lastly, while shedding light on the challenges encountered in crafting these sophisticated surveillance systems, the research also aspires to outline possible trajectories for future investigations in this realm.



Method: The vastness and complexity of fraud detection research necessitate a methodical approach to ensure a comprehensive understanding. Adopting a descriptive research methodology, this study emphasizes a quantitative content analysis. This technique is complemented by documentary methods, ensuring a meticulous and well-rounded data collection process. To obtain a rich tapestry of insights, an extensive search was conducted encompassing international articles, conference proceedings, and research papers. The Scopus database, renowned for its vast repository of scholarly articles, served as a primary source of data. In addition, reputable domestic journals specializing in the design and nuances of fraud detection systems were consulted to ensure a holistic perspective. The time frame for this research was expansive, covering publications from a period spanning two decades, from 1381 to 1401. Ensuring the accuracy and consistency of data interpretation is pivotal; thus, coding and analysis sheets within the MAXQDA software were utilized. To provide a more intuitive grasp of the data, the VOS Viewer software was employed, crafting a detailed research map emphasizing the nuances of fraud detection and prevention systems.



Results: The fruits of this intensive research are diverse and enlightening. By analyzing various studies, it was feasible to classify the nature of fraud as investigated in predictive research. Such categorization offers clarity on the multitude of fraud types and the respective systems devised to detect them. Furthermore, the study discerned vital elements underpinning both traditional and intelligent fraud detection mechanisms. Recognizing this distinction is instrumental in tracing the evolutionary trajectory of fraud detection methodologies over the years. More than just retrospective insights, the study also charted a prospective path, providing a roadmap to steer future endeavors in this domain.



Conclusion: This study, with its exhaustive review and deep insights, lays a robust foundation for future research, highlighting the importance of intelligent surveillance systems in countering fraud within public companies. Public companies are the lifeblood of many economies, and any fraudulent activities within them have repercussions that ripple through the financial ecosystem. The escalating urgency for countermeasures against such malicious actions underscores the indispensable role of research in intelligent surveillance systems. The vision is clear: with persistent and rigorous research, the design and implementation of even more effective and intelligent systems will become a reality, necessitating proposals and policy recommendations for stakeholders. These systems will not only detect and prevent fraud but also play a pivotal role in safeguarding the interests of shareholders and fortifying the financial resilience of public companies. The content analysis findings illuminate several promising avenues for future exploration. There's a pressing need to expand the scope of research, especially considering the potential of ESG and sustainable development criteria in fraud detection. The limited exploration of large linguistic models (LLM) in fraud detection, especially newer paradigms like transfer learning, presents a ripe opportunity for future research.

The results of this research by reviewing fraud detection systems in the last two decades have led to the innovation of a conceptual model for future fraud detection research, which by integrating financial and non-financial data, including environmental, social and governance (ESG) criteria, will result in a serious paradigm shift in research in this field. This approach will increase the accuracy and transparency of fraud detection systems by removing existing limitations in fraud detection and considering a wider range of variables.

Delving into cutting-edge AI technologies like Bard and ChatGPT could revolutionize the efficacy of fraud detection systems. With fraudsters continually evolving their tactics, the need for adaptive and context-aware fraud detection systems is more pronounced than ever. By leveraging intelligent data processing and modeling, future systems could shift from reactive detection to proactive prevention, identifying fraud risks in their nascent stages. The integration of diverse data sources, including social media and transaction records, can significantly bolster the potency of fraud detection. Given the high stakes, it's paramount to rigorously evaluate the performance of these surveillance systems in real-world scenarios. The reliance on vast data sets brings forth ethical and privacy concerns. Balancing effective fraud detection with ethical considerations will be a cornerstone for future research. By navigating these research avenues, the domain of intelligent monitoring for fraud detection stands poised for transformative advancements, thereby reinforcing the bedrock of global financial systems.