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<h1>Investigative Data Mining, Credit Card Fraud Investigation and Prevention Software, UK</h1>
Credit card fraud prevention has become a major factor in modern society and is acheived using fraud investigation software and the application of various other techniques.
The concept behind data mining, especially in the case of credit card fraud prevention and identifying potentially fraudulent transactions, is to identify transactions that exhibit certain characteristics. This requires the use of fraud investigation software to achieve credit card fraud prevention and the ability to drill down into those transactions that are high-risk areas.
Data mining is also not limited to the analysis of one single data file, but should draw information together from a number of sources such as fraud investigation software to successfully acheive credit card fraud prevention. For example, in an analysis or investigation of procurement, data from the accounts payable, invoice history file, cash payment systems, standing data (such as supplier master file) and purchase order system may all be analysed and fraud investigation software applied. (Often standing data, such as details of suppliers or customers, is referred to as static data or information and histories of transactions as dynamic data. These terms will be used throughout this chapter and how they are used in credit card fraud prevention.) Analysis will not only be conducted within these data files on a stand-alone basis, but there will also be extensive cross matching of details via fraud investigation software. We therefore include "data matching" techniques under the broader term data mining.
An effective automated detection routine and use of fraud investigation software is not restricted to simply obtaining a download of data and then conducting computerised analysis. If it is to be effective at credit card fraud prevention, the proportion of time, use of fraud investigation software (and cost) dedicated to the "number crunch", should, in the majority of cases, form but a part of the project. Credit card fraud prevention requires that equal resource should be devoted to understanding the business process or unit, profiling the control weaknesses and likely frauds, developing appropriate search criteria, selecting the appropriate data mining tool, assessing internal and externally held data sources, running the automated testing and the proper investigation of findings.
Ultimately, there are two types approaches in using data mining techniques to prevent, detect and investigate fraud: proactive and reactive.
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