Hi Seshika, Are you planning to test this based on some real world scenarios and data? ( ex: stock prices) Therefore to get and idea how accurate we can be.
Thanks, On Tue, Nov 18, 2014 at 10:59 AM, Seshika Fernando <[email protected]> wrote: > Hi all, > > Following the implementation of Fraud Rules, and Markov Chain capability > in order to do outlier detection in CEP, we are hoping to implement Fraud > Scoring capability. > > Fraud Scoring is a mechanism to evaluate multiple features of a > transaction (eg:- geolocation, ip address, billing/shipping address, > transaction velocity etc;) and based on historical trends and blacklists, > compute a score for each transaction (eg:- between 0 and 100). Higher the > score, higher the risk that the transaction will be a fraudulent > transaction. [1] is a good introduction to Fraud Scoring. > > Now that we have already implemented several fraud detection rules, the > plan is to augment this using a scoring system, so that siddhi calculates a > score for each transaction, based on how it performed in the rules. [2] > gives a very simple example of how this might be done. > > When we complete this, we are able to show that CEP can perform fraud > detection in the following ways > a. Rule based > b. Using Markov Chains > c. Using Fraud Scores > > 1. http://www.fraudpractice.com/fl-fraudscore.html > 2. https://www.maxmind.com/en/ccfd_formula > > Cheers, > Seshika > > > > _______________________________________________ > Architecture mailing list > [email protected] > https://mail.wso2.org/cgi-bin/mailman/listinfo/architecture > > -- Waruna Perera Senior Software Engineer - Test Automation Mobile: +94 77 3867037 WSO2, Inc.; http://wso2.com/ lean . enterprise . middlewear.
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