Distributed Data Mining in Credit Card Fraud Detection

1
Distributed Data Mining in Credit Card Fraud
Detection

• Prosper , Jean Ouafo
• Superviser Herr. Master Owotoki

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DDMCCFD

• Definition Data Mining
• Defintion of Credit Card
• Introduction Goals of fraud detection
• Credit Card Data and Cost Models
• Mining the analog Data
• Combining symbolic , analog information
• AdaCost Algorithm
• Experiment and Results
• Conclusion

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Defintion of Data Mining

• Method of searching Data with mathematical
  Algorithms
• Typical applications
• New Applications where we can observed Data
  Mining
• - Business and E-Commerce Data
• - Scientific , Engeneering
• - Web Data

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Defintion of Credit Card

• Visa
• MasterCard
• American express
• Discover

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Definition of Credit Card

• - Any Card , plate , or coupon book that may
  be used repeatedly to borrow money or buy
  products and ser vice on credit.
  
  
• - Many forms of credit cardletter of
  credit,earnings credit rate, etc
  
• - Informations about Credit Card
  

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Introduction

• Credit Card transactions continue to grow in
  number, taking a large share of the US payment
  system and follow thus to a higher rate of stolen
  account numbers and the Banks losse much
  money.

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Goals of fraud detection

• The 3 Steps
• High efficient technique,
• Data are highly skewed
• choose Cost-based techniques
• Black box fraud detection
• JAM(Java agents for Meta Learning)

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Meta-Learning

• Apply to the area of Data miming
• Combine the prediction from multiple models
• Reduce the cost of fraud through timely
• Minimize the losses by catching fraud more
  rapidly
• Minimize Costs assoziated with false alarms

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JAM

• 2 techniques with JAM
• Local fraud Detection agents to learn how to
  detect fraud
• Secure, integrated meta-detection system ot view
  the network transaction

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Credit Card Data and Cost Models

• The transaction data are characterized by some
  very special proportions
  
  
• The probability of a fraud transaction is very
  low(0.2)
• Most of the 38 data fields (about 26 fields)
  per transaction contain symbolic data as merchant
  code, account number,client,name, etc.....

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Cost Models

• . A symbolic field can contain as low as two
  values(e.g. the kind of credit card) up to
  several hundred thousand values (as the merchant
  code).
• .Transactions with a confidence for fraud of
  higher than 10 are accepted to be revised or
  aborted.

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Experiment and Results

• 4 learning algorithms
• C4,5
• CART
• RIPPER
• BAYES

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Results
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Mining the analog Data

Analog Data
2 layer time net
Decision
Misuse YES/NO
2 layer credit net
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Results

• The neuronal network experts for analog Data.
• Diagnosis sequence data
• 2 Ideas
• first, there can be the typical fraud
  sequences, for instance the behavior of a thief
  after copying or picking the credit card.
  
• Second, there can be a typical behavior of the
  user which it does not correspond to the
  actual.transaction sequence may indicate a credit
  card misuse.
  

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Combining analog and symbolic Information

• time
• 
  Fraud y/n
• 
  

Rule based classification
S
Analog value classification
User Profile classification
A parallel Diagnostic
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Combining with Sequential
Rule based classification
Analog value classification
S
User profile classification
P
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AdaCost Algo

• Used internal heuristics based upon training
  acuracy
• Learning Algorithm to predict fraud.
• Employs internal metrics of misclassification
  cost.

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Conclusion

• In summary, We can observe that the combined
  power of rule and analog expert does not
  increase the amount of detected fraud, but detect
  it more securely with 100 confidence just as
  we expected. Nevertheless, the probability of
  fraud detection is too low compared with the rule
  based system only. Therefore, we tested the
  strategy of adding additional rules even with
  lower confidence.
• Links www.twocrows.com/glossary.html
• Distributed data mining in credit card
  fraud detection P. Chan, W. Fan, A.
  Prodromidis, and S. Stolfo IEEE
  Intelligent Systems, 14(6)67-74, 1999.
• JAM Java Agents for Meta-learning over
  Distributed Databases S.J. Stolfo, D.
  Fan, W. Lee, A. Prodromidis, P. Chan