NEURAL DATA MINING FOR CREDIT CARD FRAUD DETECTION
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Download free Neural Data Mining for Credit Card Fraud Detection.pdf The prevention of credit card fraud is an important application for prediction techniques. One major obstacle for using neural network training techniques is the high necessary diagnostic quality: Since only one financial transaction of a thousand is invalid no prediction success less than 99.9% is acceptable.
Due to these credit card transaction proportions complete new concepts had to be developed and tested on real credit card data. This paper shows how advanced data mining techniques and neural network algorithm can be combined successfully to obtain a high fraud coverage combined with a low false alarm rate.
The prediction of user behavior in financial systems can be used in many situations. Predicting client migration, marketing or public relations can save a lot of money and other resources. One of the most interesting fields of prediction is the fraud of credit lines, especially credit card payments. For the high data traffic of 400,000 transactions per day, a reduction of 2.5% of fraud triggers a saving of one million dollars per year.
Certainly, all transactions which deal with accounts of known misuse are not authorized. Nevertheless, there are transactions which are formally valid, but experienced people can tell that these transactions are probably misused, caused by stolen cards or fake merchants. So, the task is to avoid a fraud by a credit card transaction before it is known as “illegal”.
With an increasing number of transactions people can no longer control all of them. As remedy, one may catch the experience of the experts and put it into an expert system. This traditional approach has the disadvantage that the expert’s knowledge, even when it can be extracted explicitly, changes rapidly with new kinds of organized attacks and patterns of credit card fraud. In order to keep track with this, no predefined fraud models as in but automatic learning algorithms are needed.
This paper deals with the problems specific to this special data mining application and tries to solve them by a combined probabilistic and neuro-adaptive approach for a given data base of credit card transactions of the GZS.
here
Due to these credit card transaction proportions complete new concepts had to be developed and tested on real credit card data. This paper shows how advanced data mining techniques and neural network algorithm can be combined successfully to obtain a high fraud coverage combined with a low false alarm rate.
The prediction of user behavior in financial systems can be used in many situations. Predicting client migration, marketing or public relations can save a lot of money and other resources. One of the most interesting fields of prediction is the fraud of credit lines, especially credit card payments. For the high data traffic of 400,000 transactions per day, a reduction of 2.5% of fraud triggers a saving of one million dollars per year.
Certainly, all transactions which deal with accounts of known misuse are not authorized. Nevertheless, there are transactions which are formally valid, but experienced people can tell that these transactions are probably misused, caused by stolen cards or fake merchants. So, the task is to avoid a fraud by a credit card transaction before it is known as “illegal”.
With an increasing number of transactions people can no longer control all of them. As remedy, one may catch the experience of the experts and put it into an expert system. This traditional approach has the disadvantage that the expert’s knowledge, even when it can be extracted explicitly, changes rapidly with new kinds of organized attacks and patterns of credit card fraud. In order to keep track with this, no predefined fraud models as in but automatic learning algorithms are needed.
This paper deals with the problems specific to this special data mining application and tries to solve them by a combined probabilistic and neuro-adaptive approach for a given data base of credit card transactions of the GZS.
here
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