An Intelligent Financial Fraud Indication System Using Fuzzy Logic
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Date
2014-06-22
Journal Title
Journal ISSN
Volume Title
Publisher
African Journal of Computing & ICT
Abstract
The issue of financial fraud is taking different dimensions in various countries due to rise in fraud enabling factors emanating from internal and external challenges. The internal challenges from unemployment rate depend on overpopulation, poor standard of living and nature of existing leadership of government. Recent advancement in modern technology and increase in tourist activities have contributed to the existing external challenges. In this paper, an intelligent financial fraud indication system using fuzzy logic is proposed, which involves the use of fuzzy arithmetic, fuzzy degree of association, fuzzy inference, fuzzy rules and different surface plot in determination of different relationship as it relate to certain choice of inputs. The selected input indices were meant to determine how certain degree of changes in inputs can affect the nature of fraud indication. The proposed system also depends on fraud enabling factors in the determination of appropriate fraud indication level. From the results obtained, it is shown that people from areas with high population, high political activities and high education are likely to be engaged in more financial fraud compare to an area in which population, political activities and education are low
Description
This research paper proposes an intelligent financial fraud indication system based on fuzzy logic to address the growing and complex nature of financial fraud driven by both internal and external factors such as unemployment, overpopulation, poor governance, technological advancement, and increased tourism. The system utilizes fuzzy arithmetic, inference, degree of association, and rules to analyze how variations in selected input indices—like population, political activity, and education—impact the likelihood of fraud. By modeling these relationships through surface plots and fuzzy reasoning, the system effectively evaluates fraud-enabling conditions to determine fraud indication levels. Results indicate a higher propensity for financial fraud in regions with dense populations, active political engagement, and higher education levels.
Keywords
Financial fraud, Financial institution, Fuzzy logic, Intelligent system, Membership function