Detecting Suspicious Card Transactions in unlabeled data of bank Using Outlier Detection Techniqes

With the advancement of technology, the use of ATM and credit cards are increased. Cyber fraud and theft are the kinds of threat which result in using these Technologies. It is therefore inevitable to use fraud detection algorithms to prevent fraudulent use of bank cards. Credit card fraud can be thought of as a form of identity theft that consists of an unauthorized access to another person's card information for the purpose of charging purchases to the account or removing funds from it. Credit card fraud schemes are divided into two categories: application fraud and account takeover. When a credit card account gets opened without someone’s permission is called application fraud. Account takeovers, on the other hand, is when an existing credit card account is hijacked, and the criminal obtains enough personal information to modify the account's information. The criminal then subsequently reports the card lost or stolen in order to obtain a new card and make unauthorized purchases with it. Data mining as a technique capable of identifying useful patterns among a great deal of data is an effective method in detecting fraud in this regard. The main purpose of this paper is to present a new method for detecting unattended outliers that require high accuracy and recall. The method presented in this study is based on a combination of NMF, hierarchical k-means, k-means and k-nearest neighbors’ techniques. To evaluate the proposed method of outlier detection, several experiments were performed using standard data, in terms of accuracy and recall with Isolation Forest, k-nearest neighbors, Median kNN, and Average kNN. The dataset used in this paper is one that was provided in a 2016 Kaggle competition and was provided by a European bank after anonymization. The results, corroborate that the proposed method has higher accuracy and recall than other algorithms.