Build Fraud Detection Model Without Historically Known Fraud Data

When there are no historically known fraud data, we have to build unsupervised models that do not require a target variable. There are a number of such models that we can try: one-class support vector machine, principal component based compression net, etc. They are detecting data points that are abnormal, not fraudulent per se. We only hope that  being abnormal is highly related to being fraudulent.

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132 Responses to “Build Fraud Detection Model Without Historically Known Fraud Data”

  1. kirk says:

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    thank you!!…

  2. Alberto says:

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    áëàãîäàðþ!!…

  3. clayton says:

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    thank you!!…

  4. shane says:

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    good info….

  5. micheal says:

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    ñïñ çà èíôó….

  6. Chester says:

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    good info!!…

  7. homer says:

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    tnx for info!!…

  8. pedro says:

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  9. corey says:

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  10. ken says:

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    áëàãîäàðåí!!…

  11. Ken says:

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    thanks!!…

  12. Don says:

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    áëàãîäàðþ….

  13. nick says:

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    ñïàñèáî çà èíôó….

  14. Timothy says:

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    thanks for information!!…

  15. oliver says:

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  16. calvin says:

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  17. michael says:

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    ñïàñèáî!!…

  18. Daryl says:

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  19. otis says:

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    thanks….

  20. freddie says:

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  21. vernon says:

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  22. Tim says:

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    thanks for information!!…

  23. Melvin says:

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  24. edward says:

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    ñýíêñ çà èíôó!…

  25. Clyde says:

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    good info….

  26. richard says:

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    ñýíêñ çà èíôó….

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