Effective model risk management is more critical than ever. Increasing, evolving AML regulations are the norm and upcoming changes such as Rule 504 regulations and other updates will only add to the challenge. Validation can be cost-prohibitive, time consuming, difficult to benchmark—and after all that, still fail to address the most critical part of the process.

Many model validation and certification processes don’t account for underlying weaknesses caused by bad data. Starting with poor quality data simply validates that “bad data” is being accurately transferred and used within your program. You can’t effectively meet compliance mandates without validating the integrity, accuracy and completeness of data—pre and post-implementation. You may think you’re connecting all the dots. But they’re not the right dots.

Don’t settle for just any model validation tool 
Model risk management is as much about data quality as it is processes and transaction validation. LexisNexis data quality and data enrichment processes offer a proactive, holistic approach to data hygiene that, when paired with AML Analytics’ Global & US Benchmark services, can provide scalable, cost-effective screening validation.