I’d say we start with four big buckets of how we leverage data.
Data integration is combining data from various sources such as the public, private, proprietary sources of information compiling them together
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into a comprehensive view of people and activities to enhance detection capabilities.
We take that data from an integration perspective and do enrichment with it which is, you know, adding additional information
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to provide more context to the data itself to afford more legitimacy and optics into the activities associated with the information.
Then, pile on top of that predictive analytics, you know, take the historical data look for patterns in the data look for behaviors in the data
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you leverage machine learning algorithms to detect anomalies flags suspicious activities and then finally, you know, fraud scoring you know signing risk scores
risk scores based on the input of the information looking at the score of the information based upon just, you know, the input itself versus what we have in us
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massive data assets to do comparisons against and then, you know, produce scores that indicate higher or lower
and where additional verification might be needed or specific manual review.
