A Critical Examination of the Strategies for preventing Synthetic Identity Fraud: The Roles of Machine Learning and Behavioural Analysis in Improving Financial System Security and Cybersecurity

Authors

  • Ayodeji B. Owoeye (PhD)

Keywords:

Synthetic, Money Laundering, Behavioural Analysis3, Risk Assessment, Blockchain, Fraud, Identity Fraud and Cybersecurity

Abstract

Current research in security and identity management has mostly focused on identifying fake qualities in disputed ecosystems. A novel approach is provided that uses proximity-based methods and order-of-consensus computation to identify several forms of bogus characteristics, some of which are known and others unknown. Its strength is in the examination of the fundamental differences between natural and human activities, as well as the development of precision through the use of forgery detection. However, data quality and computational complexity remain obstacles that must be tested and enhanced. Synthetic identity fraud was addressed using a GCN-based strategy that included cluster and classification algorithms. This method can identify scammers or deviant groups. This paper argued that Data quality and scalability have limitations. Similarly, when it comes to social network identity fraud, the proposed technique combines verified social profiles and decentralised trust computation, both of which are quite effective in presenting real examples. The hurdles include acceptance rates and integration into current systems, thus the study should continue. Moving on to human perception, research on object stiffness perception demonstrates that visual feedback plays the primary function. The findings show that modifying visual signals to make a stiff surface appear soft has a greater influence on the perception of stiffness than vice versa. Behavioural adaption approaches and outcome metrics, such as ARD, help to explore multisensory integrations in stiffness perception.

On the other side, the research findings show that experimental parameters impact generalizability, requiring other research fields to incorporate more variables. Finally, these research result in the refinement and improvement of safety measures, as well as the analysis of many elements of human perception across domains.

 

Published

2024-06-26

How to Cite

Ayodeji B. Owoeye (PhD). (2024). A Critical Examination of the Strategies for preventing Synthetic Identity Fraud: The Roles of Machine Learning and Behavioural Analysis in Improving Financial System Security and Cybersecurity. SJIS-P, 36(2), 1–17. Retrieved from http://sjisscandinavian-iris.com/index.php/sjis/article/view/795

Issue

Section

Articles