Dark Web Financial Fraud Identification Using Mathematical Models in Healthcare Domain

Anand Singh Rajawat - School of Computer Sciences and Engineering, Sandip University, Nashik, 422213, India
S.B. Goyal - Faculty of Information Technology, City University, Petaling Jaya, 46100, Malaysia
Ram Kumar Solanki - School of Computer Sciences and Engineering, Sandip University, Nashik, 422213, India
Amit Gadekar - Sandip Institute of Technology and Research Centre, Sandip University Nashik, 422213, India
Dipak Patil - Sandip Institute of Engineering and Management, Sandip University Nashik, 422213, India

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DOI: http://dx.doi.org/10.62527/joiv.8.1.2600


The so-called "dark web" has emerged as the most trustworthy platform for thieves to launch their enterprises. The healthcare industry has become a haven for illegal activities such as the sale of medical gadgets, trafficking in human beings, and the purchase of organs. This is because the sector provides a high level of privacy, which makes it an ideal location for engaging in unlawful operations. In this field of research, linear regression is utilized to uncover previously unknown patterns in customer demand. A vector will be created using a time series of medical equipment purchases to do this. When we look at the data the case firm gave us, we notice that people tend to desire to purchase products in one of three ways. After that, we sort the hospitals into groups according to the course of the trend vector by employing a technique known as "hierarchical clustering," which we apply to the data. According to the research findings, the trend-based clustering method is an excellent way to partition hospitals into subgroups that share similar tendencies. According to our model evaluations, no one model can reliably produce the most accurate forecasts for each cluster when used by itself. Some models can be utilized to make accurate predictions, and these models apply to a wide variety of time series that exhibit various patterns.


Dark web; fraud identification; mathematical models; healthcare

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