Fraud analysts use the term ‘entity’ for any data point. In the example used in Blog #2, we used personal identifiable data as entities.  The process of linking entities and connecting the dots between entities is called entity resolution. Entity resolution is used for the purpose of fraud detection (healthcare, internal fraud etc.), risk scoring (employee, applicant, and supplier background checks) and intelligence (watch lists and investigation findings). Resolving the link between entities provides better results with actionable insights.

Connecting the dots between entities using channel separation as a technique to confuse and misguide the fraud analyst, is best resolved with a multidimensional visual analysis tool.  The tool should be able to connect quickly and easily to multiple data sources, reducing the time aggregating the data, and helping detect multiple vulnerabilities and threats normally not identified through a manual process. The modern tool should be able to connect directly and securely to any data source, API (Application Programming Interface), or authorised open source. Having access to more data layers improves the fraud analyst’s ability to detect any channel separation technique applied by a fraudster.

The next step in the process depends on the tool’s ability to avoid duplication of data, match (fuse) the data records on a chart, and visualise the insights. The tool should finally be able to perform advanced link analytics, meaning it will supply multidimensional (two or more) visual analysis,  which  identifies non-obvious connections. For example, a timeline analysis on the entity’s behaviour and a  social network analysis of the entity, finds the pathway and lists the most connected entities.

XTND’s ExposeIT fraud analysis and crime profiling is the preferred tool that:

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    Written by Leon Towsen, COO, XTND

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