Quantifind for Customer Due Diligence

Automate your Enhanced Due Diligence to manage risks and prevent fraud loss.

Quantifind’s Customer Due Diligence solution uses a unique combination of external data sources and predictive risk typology models to inform profiling and segmentation at on-boarding. Broad data access expands the coverage of CDD reviews to better manage reputational risk and fraud loss, while machine learning models for accuracy and relevancy help ensure that on-going CDD alerts are on-target.

As investigative tools evolve to become more intelligent and more automated, it is sensible to consider redirecting them to the very top of the funnel – where potential bad actors are actually coming on board as customers. If we can restrict their affiliation in the first place, we can minimize compliance costs and potential fraud loss downstream. Regulators in AML see the same opportunities and are recommending more comprehensive, consistent, and risk-informed approaches to Customer Due Diligence. There is an opportunity now to transform how CDD is conducted in a way that benefits both the reputation and the bottom line of a financial institution.

Quantifind’s Customer Due Diligence solution uses a unique combination of external data sources and predictive risk typology models to inform risk segmentation of individuals and businesses at the time of on-boarding.

For the highest risk segments, Quantifind’s always-on surveillance identifies when there is new-net information which changes the customer’s risk profile.

The process is fully automated from end-to-end, enabling both speed and scale in the solution, but also delivering on the regulators’ desire for a rational, consistent approach across the board.

Quantifind for Customer Due Diligence scans for information in the public domain and summarizes results either over API or through a UI. End-to-end automation enables speed and scale in the implementation, making even continuous surveillance a possibility.

Broad data access expands the coverage of CDD reviews

Machine learning models for accuracy and relevancy help ensure that on-going CDD alerts are on-target.