Credit Card Fraud Detection using Hidden Markov Model and Stochastic Tools and technology

Abstract

The advent of credit card increases the people comfort but also attracts fraudsters. Credit cards are good targets for fraud, because in a short time large amount of money can be earned without taking risks. The crime will be discovered after few weeks so it is easy for malicious agents to commit this crime. For the past 20 years financial organizations have seen increase in the amount and types of fraud. The best method is to testify the reasons of fraud from the available data. From several researches the solutions for this credit card fraud are determined by genetic algorithms, artificial intelligence, artificial immune systems, visualization, database, behavioral, distributed and parallel computing, fuzzy logic, neural networks and pattern recognition. There are many specialized fraud detection solutions which protects credit card, insurance, retail, telecommunications industries. The main objective of these detection systems is to identify the trends of fraudulent transactions. Out of these techniques, we chose HMM and Stochastic (behavioral), and compared the two in terms of the detection of frauds. Stochastic proves to be better in terms of accuracy