Identify Previously Hidden Relationships

Similarity matching and scoring can uncover relationships that don't want to be found.

Simple data matching can't find and explain complex relationships, but Identity Resolution Engine™ can.

Identities are usually contained in multiple databases with varying schemas, and they have ambiguous and sometimes missing attributes. Our Identity Resolution Engine can resolve them to deliver a clear picture of a person, their relationships, and the activities in which they're involved. Let's consider a readily understood example, insider stock trading.

Insider trading means someone has used confidential knowledge about a financial transaction to buy or sell stock to their personal advantage. Many illegal insider stock trades can be readily identified. How? By "similarity searching" across records of stock trades, associated timelines (who knew what and when about the event) and public company financial institution data (e.g., CapitalIQ) then finding hidden relationships using biographical information (e.g., Who's Who), background screening and residential information (e.g., ChoicePoint), and other public and private sources.

Many more cases where identity resolution can exploit available data sources to address complex problems exist. Making sense from massive amounts of data by aggregating and sifting through them to find hidden relationships requires an ability to score the results accurately. Just as importantly, you need to be able to configure the scoring to fit the specific problem, i.e. the solution must be tuned to meet unique requirements.

Solving complex business problems often requires knowing more about who you're dealing with and their relationships. Vast amounts of data are accessible online via APIs and web services, and they can be easily incorporated into new types of real-time applications that were once impossible.

Click here to learn how identity resolution works.