The QuantaVerse Platform, which reduces financial crime risk by identifying patterns and discerning anomalies that current approaches and systems regularly miss, can be tuned to account for risk expectations from both regulators and financial institutions to establish specifications for risk analysis and scoring.

Risk appetite varies among financial institutions by risk intensity, nature of risk, and institutional tolerance. For example, consider a bank with many customers transacting business in the Philippines. As evidenced by its recent addition to FATF’s gray list, the Philippines is understood to be a riskier BSA/AML jurisdiction.

In a recent Business World article, it was noted that: “Disproportionate application of measures against gray-listed countries included requiring financial institutions to apply specific elements of enhanced due diligence; limiting business relationships or financial transactions with the identified jurisdictions or persons in that country; and requiring financial institutions to review or even terminate correspondent relationship in the country concerned.”

Banks must determine how to differentiate innocuous transactions more accurately from risky transactions to serve their customers who do business in risky jurisdictions. To solve this problem, the QuantaVerse Platform uses data sets that match each bank’s unique risk tolerance, including risky lines of businesses, risky jurisdictions, and so forth.

One example of risk appetite variation involves banks who primarily serve the Asia Pacific region and elect to rate transactions with Venezuela as riskier than banks in Central and South America who must be more judicious when risk scoring transactions in their own region of the world. The QuantaVerse Platform from AML RightSource can be optimized to deliver accurate risk analysis and scoring for each scenario based on a FIs business profile and risk appetite.

Risk Tuning Avoids the Downside of De-risking

To simplify matters, some financial institutions may consider protecting themselves by de-risking — terminating customers in risky industries or restricting business relationships in higher risk jurisdictions. However, terminating or restricting business relationships with clients or categories of clients to avoid risk rather than managing risk is never a good option.

Although de-risking errs on the side of caution, it can result in additional regulatory scrutiny based upon the potential for abandonment of jurisdictions and business lines. The impact of financial institutions’ abilities to leave market segments and, even, entire countries without access, due to de-risking strategies does not go unnoticed by regulators.

In addition to regulatory scrutiny, a bank may be losing out by walking away from perfectly legitimate customers. De-risking can also create a perception among potentially profitable customers that it is difficult to do business with the bank in question. In addition to aggravating new customers with onboarding delays, customers quickly get the sense that the bank can’t effectively distinguish between honest business owners and criminals. So, while banks may naturally want to minimize their risks, de-risking may push their institutions into another type of risk – namely being uncompetitive.

Applying Risk Appetite to AML Risk Scoring

These considerations highlight the importance of the risk tuning capabilities of the QuantaVerse Platform. Financial institutions have different nuances in their data – additional facts, not otherwise available to QuantaVerse, that can be invaluable in more finely determining risk. The ability of the QuantaVerse Platform to incorporate these nuances provides financial institutions with significantly improved risk management capabilities.

What’s more, there will come times when an investigator has reason to disagree with the data that has been used by the QuantaVerse Platform to determine risk. To accommodate discrepancies, QuantaVerse enables its customers to override its risk segmentation when indicated. QuantaVerse’s ability to accept feedback to its risk scores allows customers to influence risk segmentation. Feedback from human investigators improves the QuantaVerse risk analysis by honoring that risk segmentation going forward for subsequent risk processing at that financial institution. This enables consistent analysis across cases and investigators.