Credit Card Fraud Detection Using Hidden Markov Model

2008 ◽  
Vol 5 (1) ◽  
pp. 37-48 ◽  
Author(s):  
A. Srivastava ◽  
A. Kundu ◽  
S. Sural ◽  
A.K. Majumdar
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Credit Card ◽  
Hidden Markov ◽  
Fraud Detection ◽  
Credit Card Fraud

scholarly journals Credit Card Fraud Detection Using Hidden Markov Model

2019 ◽  
Vol 8 (11) ◽  
pp. 24878-24882 ◽  
Author(s):  
Mandeep Singh ◽  
Sunny Kumar ◽  
Sunny Kumar ◽  
Tushant Garg
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Credit Card ◽  
High Probability ◽  
Hidden Markov ◽  
Credit Cards ◽  
Fraud Detection ◽  
Normal Behaviour ◽  
Credit Card Fraud

Now a day the usage of credit cards and net banking for online payments has dramatically increased. The most popular mode of online as well as regular purchase payments is through credit card and security of such transactions is also a major issue as frauds are increasing rapidly. In the existing scenario, fraud is detected after the transaction is done and it makes more difficult to find out fraudulent loses barred by issuing authority. In this paper, we observe the behaviour of credit card transactions using a Hidden Markov Model (HMM) and show how it detects frauds. An HMM is initially trained with the normal behaviour of transaction. If the present credit card transaction is not accepted by the trained HMM with enough high probability, then it declares as a fraudulent transaction. At the same time, we try to ensure that no genuine transactions are rejected.

scholarly journals Credit Card Fraud Detection using Hidden Markov Model

IJARCCE ◽  
2015 ◽  
pp. 169-170
Author(s):  
Aashlesha Bhingarde ◽  
Avnish Bangar ◽  
Krutika Gupta ◽  
Snigdha Karambe
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Credit Card ◽  
Hidden Markov ◽  
Fraud Detection ◽  
Credit Card Fraud

Credit card fraud detection using Hidden Markov Model

2011 ◽  
Author(s):  
Divya. Iyer ◽  
Arti Mohanpurkar ◽  
Sneha Janardhan ◽  
Dhanashree Rathod ◽  
Amruta Sardeshmukh
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Credit Card ◽  
Hidden Markov ◽  
Fraud Detection ◽  
Credit Card Fraud

scholarly journals Credit Card Fraud Detection Performance Improvement using Advanced Super Gradient Boosting Algorithm

2020 ◽  
Vol 9 (6) ◽  
pp. 179-184
Keyword(s):  
Text Mining ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Credit Card ◽  
Hidden Markov ◽  
Training Data ◽  
Gradient Boosting ◽  
Data Set ◽  
Credit Card Fraud ◽  
Mining Algorithm

Credit card fraud introduces to the physical loss of a credit card or the destruction of sensitive credit card data. Several text mining procedures can be used for disclosure. This investigation reveals several algorithms that can be used to analyze transactions as a fraud or as a real background. This paper represents the possibility of fraudulent transactions in the prevalence and meaning of credit card usage also, Credit card fraud data collection was used in the investigation. Since the dataset was largely unbalanced, SMOTE (Synthetic Minority oversampling Technique) is applying for an overdose. In addition, jobs selected, and the data set divided into two parts, training data and test data. In this paper, The Advanced Super Gradient Boostingbased Text mining Algorithm (ASGB) suggested to detect the fraud transaction in Credit card transactions. ASGB is a Decision-Tree-Based Ensemble Text mining algorithm that utilizes a gradient boosting framework. In forecast difficulties, including unstructured data (Images, Text, etc.), artificial neural networks tend to exceed all other algorithms or structures. The proposed algorithms used in the experiment were the Hidden Markov Model, Random Forest, Gradient Boosting, and Enhanced Hidden Markov Model. The Experimental Results show that proposed algorithms, a welltuned ASGB classifier outperforms all of them. And it presents better Precision is 99.1%, and Recall is 99.8%, F-measure is 99.5%.

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