This document discusses advanced credit card fraud detection techniques. It outlines that millions of dollars are lost annually to credit card fraud. It then describes different types of fraud like counterfeit cards, lost/stolen cards, and identity theft. It presents several data mining techniques used for fraud detection, including hidden Markov models, decision trees, k-nearest neighbor algorithm, and logistic regression. Specifically, it notes that hidden Markov models use automatic techniques to take action at precise times, decision trees separate complex problems, and k-nearest neighbor and support vector machines are used for easy detection and kernel representation/margin optimization respectively. The document concludes that logistic regression can minimize fraud rates and is easy to implement.

1. ADVANCED CREDIT CARD FRAUD DETECTION
Presented By
K Ganesh
K Suryakumar
B Shanmuga anand
Department of MCA
Valliammai Engineering College

2. Introduction
• Millions of dollar get Losses in worldwide.
• $3.6 billion to $4 billion get Loss in 2008.
• $11.27 billion in 2013-2014.
• Frauders follow fraud practices to avoid
detection.

3. Different Types of Fraud
• Counterfeit Credit Cards.
• Lost or Stolen Cards.
• Card Not Present (CNP) Fraud.
• Phishing.
• Non-Receipt Fraud.
• Identity Theft Fraud.

4. • Hidden Morcov Model.
• Decision Trees.
• K-Nearest Neighbor Algorithm.
• Logistic Regression.
Datamining Techniques

5. Hidden Morcov Model
• Automatic techniques.
• A Hidden Markov Model is a
finite set of states.
• Initially trained with cardholder.
• Take action at exact time.

6. Hidden Morcov Model

7. Decision Trees
• Classification rules,
extracted from decision trees,
IF-THEN expressions and all the tests have
to succeed if each rule is to be generated.
• Separates the complex problem into many
simple ones.
• resolves the sub problems through
repeatedly using.

8. K-Nearest Neighbor Algorithm
• Locate the nearest neighbors.
• Neighbors used to classify the new sample.
• Easy detect.
• It is unsupervised learning.

9. Support vector machine
• Kernel representation.
• Margin optimization.

10. • Detect the problem.
• Best supportive technique
• Trees constructed.
• Favour for SVM.
Random forest

11. Logistic Regression
❖Support vector machine.
❖Random forest.
❖This two are the important techniques in
data mining which is together
called logistic regression

12. Conclusion
• Logistic Regression can minimize the fraud rate.
• It is easy to implement.

13. Reference
• C. Chen, A. Liaw, L. Breiman, Using Random Forest to Learn
Imbalanced Data,Technical Report 666, University of California at
Berkeley, Statistics Department.
• A. Srivastava, A. Kundu, S. Sural, A.Majumdar, Credit card fraud
detection using hidden Markov model, IEEE Transactions on
Dependable and Secure Computing.