A difference in outcomes that looks unexplained may actually reflect a legitimate regulatory requirement, a prohibited basis, or a military-related protection. When those variables aren't available in the analysis, every such difference collapses into unexplained variance, which is the hardest kind to evaluate and to defend in a review.
Financial institutions can now capture and analyze two additional Core Fields across RiskExec's Fair Lending and Fair Servicing modules: Military Lending Act (MLA) Status and Servicemembers Civil Relief Act (SCRA) Status. These new fields expand the variables available for disparity testing, regression analysis, matched pair testing, and servicing reviews, so institutions can incorporate regulatory considerations that previously required manual workarounds or external datasets.
For compliance teams, adding these fields directly into the platform helps create analyses that better reflect the regulatory frameworks governing lending and servicing decisions. Institutions can evaluate whether a variance is associated with legitimate regulatory requirements, prohibited bases, or military-related protections, rather than leaving it unexplained.
Every fair lending or fair servicing analysis depends on the quality and completeness of the underlying data. When a prohibited basis or regulatory status is unavailable as an analysis variable, it cannot be evaluated consistently across statistical models or comparative reviews.
The addition of MLA Status and SCRA Status allows institutions to incorporate these attributes directly into their existing analytical workflows across both Fair Lending and Fair Servicing.
These enhancements were introduced in response to client feedback and support more comprehensive analyses.
MLA provides protections for covered borrowers by limiting the Military Annual Percentage Rate (MAPR) and restricting certain loan terms for covered credit transactions.
MLA Status is now available as a Core Field and analysis variable, with values of:
Capturing MLA coverage directly within the platform helps distinguish pricing differences resulting from statutory requirements from differences that may warrant additional review.
Examples include:
SCRA provides legal protections for eligible servicemembers on obligations incurred before military service, including a 6% cap on interest and fees for the period of active duty (50 U.S.C. ยง 3937) and restrictions on certain foreclosure, repossession, and collection activities.
RiskExec now supports SCRA Status as a Core Field and analysis variable with values of:
This field enables institutions to identify covered accounts during servicing analyses and comparative reviews.
Common use cases include:
These fields are available across both the Fair Lending and Fair Servicing modules.
They can be incorporated into:
Adding MLA Status and SCRA Status as native Core Fields gives compliance teams greater flexibility to incorporate prohibited bases and military-related protections into routine analytical workflows without relying on custom processes or supplemental files.
If you would like to discuss how these new fields fit into your institution's existing fair lending or fair servicing analyses, contact your RiskExec Account Manager.
To learn more about how RiskExec supports Fair Lending and Fair Servicing analytics without exporting loan data to layer these variables in by hand, request a demonstration.
RiskExec now supports Military Lending Act (MLA) Status and Servicemembers Civil Relief Act (SCRA) Status as Core Fields across the Fair Lending and Fair Servicing modules.
MLA Status identifies loans covered by the Military Lending Act, allowing institutions to evaluate pricing and servicing outcomes while accounting for statutory Military Annual Percentage Rate (MAPR) limits and other MLA requirements.
SCRA Status allows institutions to identify protected accounts during servicing reviews, comparative analyses, and compliance testing involving eligible servicemembers.
Yes. Both fields are available as Core Fields across these modules and can be incorporated into supported analytical workflows.
