Abstract

Focusing on Data-mining role in fraud detection for discovering the threats on credit card transactions by the increase of financial deals and huge information used by it, while the threats change continuously are evaluated which determined by a group of odd and anomalous behaviors, an example of detecting fraud in financial credit card transactions which represent one of the basic things in our life and significant in business and financial deals that is an interested subject for study by its important in life. An important requirement of fraud detection is finding system able to detect a various types of credit card attacks and an effective procedure for detect it, depends on various credit cards type and different fraud in financial deals used in trade, banks and industry,  there is many measures can reduce fraud through using data mining techniques, we take two of them and comparing a results to for best reduce and detect fraud trying in financial domain for credit card which are Adaboost classifier and Hidden markov model.
Using these techniques objective is to minimize frauding on credit card. By finding fraudulent clients and merging classification methods focusing on two different algorithms, discovering the degree of fraudulent activities in the financial domain the the used techniques are Adaboost and Hidden Markov model.