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SURVEY DATA LAUNDERING (for Fraud Detection and Deviation Detection) Developer : Joesph Mullat

SURVEY DATA LAUNDERING (for Fraud Detection and Deviation Detection) Developer : Joesph Mullat. Presented By : Sriram Subramanian 11 - 16 - 1999 CSE 6331. PRESENTATION FLOW. Introduction Brief Overview Demo Tool in Detail Data Mining techniques

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SURVEY DATA LAUNDERING (for Fraud Detection and Deviation Detection) Developer : Joesph Mullat

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  1. SURVEY DATA LAUNDERING(for Fraud Detection and Deviation Detection)Developer : Joesph Mullat Presented By : Sriram Subramanian 11 - 16 - 1999 CSE 6331

  2. PRESENTATION FLOW • Introduction • Brief Overview • Demo • Tool in Detail • Data Mining techniques • Advantages & Disadvantages • Applications • Conclusions • References

  3. INTRODUCTION What is Data Laundering ? -process by which the survey data is split into different blocks and then reconstructed back but with doubtful answers or results removed. Why Data Laundering ? - To increase the reliability of results. - To deduce completeness of data - To detect fraudulent data / values.

  4. Tool - Overview & Demonstration System Requirements: OS -Windows NT / Windows 98 / Windows 95 Tools - MS - Excel Memory Requirements : 5.5 Mb Inputs : Excel table format Output: Excel table format and graphs

  5. Symbolic Representation of Tool ANALYSE E / I E / I SCHEMA

  6. Example Survey data collected from a travelling agency (180 travelers) answering about 25 questions.Viz... Description of travelling destination and hotel, Telephone service, Personal service, Sales staff knowledge for the destination, Check in at the airport - departure etc..., SDL applied on these data items and the accuracy of responses (seriousness ) ascertained.

  7. Tool in Detail - Examples

  8. AnotherExample: INTERNET SURFING - quest for knowledge or waist of time? Identification of “Spiders” and use of Internet for non-work related reasons by using the principle of duality and data laundering. Details :183 Employees,4368 sites ==> 8 Employees ,105 sites SDL E1 E2 E3 E4 E5 E6 E7 E8 S1 S2 S3

  9. Data Mining Techniques Applied Classification - to distinguish the survey data as proper / improper data Clustering - to group the survey data by the responses.

  10. Applications & Usefulness In Deviation Detection and Fraud Detection. In Market Research areas to overcome hesitation effect and to validate the survey data. To understand the customer response to survey and to improve the efficiency of surveys.

  11. Positive aspects and Drawbacks • Resources required for this tool are minimum. • Results derived are easy to comprehend. • Tool can be extended to suit individual user needs since it is • Simple and available in public domain. • Tool supports an extensive “HELP” • The current version of Tool is application specific. • User needs to familiarize with terms before using the tool.

  12. Conclusions An effective , simple tool for small businesses to determine the effectiveness of survey data and to interpret the results from the survey data.

  13. References http://users.cybercity.dk/~dko5867/

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