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Credit Card Fraud Detection Using Data Mining and Fuzzy Logic

Exploratory Analysis

The study analyzed a dataset of fraudulent transactions from 2005 to 2007, yielding some key findings:

• Demographics Affected by Credit Card Fraud: Women are most likely to be affected (55%), while young people (18-30 age group) are also highly susceptible (34%).
• Products Involved in Fraud: Products involved in fraud can easily be converted into cash, including:
  • Gas stations
  • Drug stores
  • Fast food joints

Development of the Process

The study employed data mining and fuzzy logic to identify association rules indicative of fraudulent behavior. The process consists of three stages:

Stage 1: Establishment of Linguistic Labels

• The researchers used Serrano’s software tools to define trapezoidal labels for attributes such as:
  • Age
  • Number of years account held
  • Purchase period
  • Purchase amount

Stage 2: Incorporation of Linguistic Labels into Client and Transaction Tables

The linguistic labels were incorporated into the client and transaction tables.

Stage 3: Application of the FuzzyQuery 2+ Software Tool

The researchers applied the FuzzyQuery 2+ software tool to identify association rules indicative of fraudulent behavior.

Computational Process

The study utilized:

• An HP Compaq nx9020 notebook with a 1.4 GHz Intel Celeron processor and 256 MB of RAM.
• Microsoft Windows XP Professional 2001 operating system with Service Pack 1.
• Oracle 9i database engine.

Conclusion

This study aimed to develop a process for detecting credit card fraud using data mining and fuzzy logic, which can help identify association rules that may indicate fraudulent behavior.