Financial Crime Buster Analysis


The approach used in this template for fighting financial crime places machine learning at the center of the crime detection system. Machine learning models use historic data to learn how to spot risky or abnormal behavior exhibited by transactions, clients, suppliers, or other players. It uses two types of models: Supervised learning algorithms, that tell us how similar to past fraud a new transaction is and Unsupervised learning algorithms, that tell us how odd a new transaction seems when compared to past transactions.  The first model guarantees accuracy, the second the ability to adapt to changing realities.


Read the whitepaper on Busting Financial Crime with TIBCO


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Financial Crime Buster Analysis

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Product Details
Release date: November 30, 2016
Last updated: November 1, 2016
Current version: 1.2
Software application type: Spotfire Template
File format: .zip
File size: 109.7
Requirements: Tested on Spotfire 7.6, TERR 4.2 and CRAN packages randomForest 4.6-12, ROCR 1.0-7, rjson 0.2.15, RCurl 1.95-4.8
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