Research Of Credit Card Fraud Detection Model Based On Data Mining

With the development of economy and society, and the continuous opening of financial markets around the world, government of each country is actively promoting kinds of measures relevant to financial liberalization and internationalization, transactions based on credit card increase unceasingly. However, with the large-scale rise of credit cards and volume of transaction , the enlargement of business scope, and the quick growing of market, the amount of credit card fraud is boosting on amazing speed, and fraud methods have been retrodden and commit skills have been shrewder day by day. It is difficult for banks to discover fraud transactions effectively, and the risk and loss is larger and larger, so a model or system which can quickly judge and accurately distinguish credit card transaction is in urgent need to assist bank work.This thesis is directed at the fraud matter existing in the bank credit card transactions of our country. It constructs the detection model of credit card fraud on the basis of data mining technology and provides technical support for risk management of our bank credit card.This thesis firstly introduces status of credit card risk management of our country and analyses the causes of fraud risk and tactics of fraud detection and prevention. Then it constructs credit card fraud detection model based on data mining technology, using Self-Organizing fenture Map(SOM) arithmetic and combined classifier theory: the large number of sample collection is tentatively classified with SOM to improve the accuracy, then, the trained collection and fraud sample is combined respectively to form new collections ,and then, classifying the preceding collections once more with SOM based on client classified quota, and finally melting together the classified results with ballot, thus the classified modular model of credit card clients is set up. Lastly, it analyses the constructed model on the basis of concrete data of credit card transactions provided by sub-branches of Bank of China, and confirms the effect of the model with living examples.