Abstract

Credit card fraud detection raises unique challenges due to the streaming, imbalanced, and non-stationary nature of transaction data. It additionally includes an active learning step, since the labeling (fraud or genuine) of a subset of transactions is obtained in near-real time by human investigators contacting the cardholders. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of fraud associated with it are also rising. In this paper, we model the sequence of operations in credit card transaction processing using a Hidden Markov Model (HMM) algorithm and Fraud detection model show how it can be used for the detection of fraud in card processing. Financial fraud is an ever growing menace with far consequences in the financial industry. HMM, Fraud detection model and image process had played an imperative role in the detection of credit card fraud in online transactions. Credit card fraud detection, which is a data problem, becomes challenging due to two major reasons – first, the profiles of normal and fraudulent behaviors change constantly and secondly, credit card fraud data sets are highly skewed. The using fraud detection algorithm performance of fraud detection in credit card transactions is greatly affected by the sampling approach on dataset, selection of HMM, Fraud detection model. Using fraud detection algorithm and image and image technique(s) used. At the same time, we try to ensure that genuine transactions are not rejected. A reliable augmentation of the target scarce population of frauds is important considering issues such as labeling cost; algorithm HMM, fraud detection; and constantly changing of patterns in the data streaming source. We have approached several scenarios with different legitimate and non-legitimate transaction ratios showing the feasibility of improving detection capabilities evaluated by means of receiver operating characteristic (ROC) curves and several key performance indicators (KPI) commonly used in financial business.

Paper Details