Here we proposed LR for Best detection result.

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.