O slideshow foi denunciado.
Utilizamos seu perfil e dados de atividades no LinkedIn para personalizar e exibir anúncios mais relevantes. Altere suas preferências de anúncios quando desejar.

Credit card fraud detection methods using Data-mining.pptx (2)

5.800 visualizações

Publicada em

Here we proposed LR for Best detection result.

Publicada em: Tecnologia

Credit card fraud detection methods using Data-mining.pptx (2)

  1. 1. ADVANCED CREDIT CARD FRAUD DETECTION Presented By K Ganesh K Suryakumar B Shanmuga anand Department of MCA Valliammai Engineering College
  2. 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. 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. 4. • Hidden Morcov Model. • Decision Trees. • K-Nearest Neighbor Algorithm. • Logistic Regression. Datamining Techniques
  5. 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. 6. Hidden Morcov Model
  7. 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. 8. K-Nearest Neighbor Algorithm • Locate the nearest neighbors. • Neighbors used to classify the new sample. • Easy detect. • It is unsupervised learning.
  9. 9. Support vector machine • Kernel representation. • Margin optimization.
  10. 10. • Detect the problem. • Best supportive technique • Trees constructed. • Favour for SVM. Random forest
  11. 11. Logistic Regression ❖Support vector machine. ❖Random forest. ❖This two are the important techniques in data mining which is together called logistic regression
  12. 12. Conclusion • Logistic Regression can minimize the fraud rate. • It is easy to implement.
  13. 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.

×