Data Mining for Compliance

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Overall, compliance management is a broad application area that applies to variety of situations.  For example, in the insurance world, the objective is to identify potentially inflated or fraudulent claims.  Here the goal for insurance adjustors is to identify the right set of claims to investigate.  In the tax compliance domain, the aim is to select the right set of tax filings for audit.  

In these situations, the auditing resources are limited, and would be better utilized if they were verifying the targets (claims, taxpayer filings, etc.) that are more likely to be non-compliant.  In these scenarios, the main challenge for combating non-compliance is the high cost attached to the activity of auditing and monitoring, and the fact that only limited resources can be devoted to deter non-compliance.

On a broader level, a compliance management organization performs a variety of functions.  In fact, one could view compliance management as a business process, starting from target selection, assessment and investigation, collections and enforcement.  Each of these functions has complicated sub-processes in themselves, and any effective compliance management organization faces decision making challenges at each of these steps. 

Our expertise is in leveraging the power of “data driven” analytical methods to help organizations achieve greater efficiencies in their compliance management functions.  Some organizations are already leveraging data mining technology to achieve greater financial and operational efficiencies by better utilizing auditing resources in day-to-day operations.  In all these applications areas, data is abundant, but making sense of it for decision making is a major challenge.  Our capabilities are focused upon helping you leverage this data-rich environment to understand, analyze and predict non-compliance behavior.