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  2. Data analysis for fraud detection - Wikipedia

    en.wikipedia.org/wiki/Data_analysis_for_fraud...

    Fraud detection is a knowledge-intensive activity. The main AI techniques used for fraud detection include: . Data mining to classify, cluster, and segment the data and automatically find associations and rules in the data that may signify interesting patterns, including those related to fraud.

  3. Intellectual property valuation - Wikipedia

    en.wikipedia.org/wiki/Intellectual_property...

    IP valuation is also beneficial in the enforcement of IP rights, for internal management of IP assets, and for various financial processes. The valuation process necessitates gathering much more information as well as in-depth understanding of economy , industry, and specific business that directly affect the value of the intellectual property .

  4. Beneish M-score - Wikipedia

    en.wikipedia.org/wiki/Beneish_M-Score

    If M-score is less than -1.78, the company is unlikely to be a manipulator. For example, an M-score value of -2.50 suggests a low likelihood of manipulation. If M-score is greater than −1.78, the company is likely to be a manipulator. For example, an M-score value of -1.50 suggests a high likelihood of manipulation.

  5. MaxMind - Wikipedia

    en.wikipedia.org/wiki/MaxMind

    MaxMind, Inc. is a Massachusetts-based data company that provides location data for IP addresses and other data for IP addresses, and fraud detection data. [1] History

  6. Precision and recall - Wikipedia

    en.wikipedia.org/wiki/Precision_and_recall

    In a classification task, the precision for a class is the number of true positives (i.e. the number of items correctly labelled as belonging to the positive class) divided by the total number of elements labelled as belonging to the positive class (i.e. the sum of true positives and false positives, which are items incorrectly labelled as belonging to the class).

  7. Corruption Perceptions Index - Wikipedia

    en.wikipedia.org/wiki/Corruption_Perceptions_Index

    Subsequently, a standardized z score is calculated with an average centered around 0 and a standard deviation of 1 for each source from each country. Finally, these scores are converted back to a 0-100 scale with a mean of approximately 45 and a standard deviation of 20. Scores below 0 are set to 0, and scores exceeding 100 are capped at 100.

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  9. Scams in intellectual property - Wikipedia

    en.wikipedia.org/wiki/Scams_in_intellectual_property

    American Intellectual Property Law Association (AIPLA) - Patent Registry Scams; Australian Patent Office - Warning!Unsolicited IP Services; Belgian Patent Office - Warning to inventors about fraudulent registration services, in (in Dutch) or (in French) (with link to a Decision of January 14, 2005 of a Belgian Appeal Court (Brussels, R.G. 2003/AR/2192 and 2003/AR/2356) (pdf) - in French)