Why AML model validation matters more than ever
Regulators expect AML systems, whether or not they are formally defined as models, to meet clear design, documentation and performance standards.
 | Appropriately designed and thoroughly documented |
| Independently reviewed |
| Calibrated to the institution’s risk profile |
| Regularly monitored for performance |
| Adjusted as risks, customer behavior and typologies change |
Y&A’s validation services help your institution avoid compliance pitfalls, strengthen internal controls and show examiners that your AML model risk management framework is robust, independent and fully aligned with regulatory expectations.
Our AML model validation approach
Our approach covers the entire AML model lifecycle to support accurate, reliable outcomes. Every engagement follows the three core components outlined in regulatory guidance:
Is the model designed properly, aligned with your risk profile, and supported by defensible logic?
We review whether your AML system’s structure, methodology and configuration meet regulatory and institutional requirements. This process includes:
- Analyzing model documentation, core assumptions, methodology and risk factors
- Assessing the quality, completeness, mapping and transformation of data
- Reviewing system configuration and its alignment with your BSA/AML risk assessment
- Confirming adherence to regulatory expectations for model governance
Outcome: Confidence that your model is built on a sound, transparent and defensible framework aligned with your institution’s risk profile.
Your AML model must keep pace with evolving risks. We make sure it does.
Regulators expect institutions to periodically review system performance and ensure proper governance.
Our review may include:
- Assessing tuning, thresholds and calibration for effectiveness
- Reviewing key performance indicators and alert-to-case ratios
- Benchmarking model performance against historical activity
- Evaluating change management and version control practices
Outcome: Assurance that your model remains effective, efficient and responsive to changing risks.
We validate whether your model’s alerts are accurate, complete and representative of actual risk.
Our independent outcomes testing includes:
- Above-the-line and below-the-line testing to assess detection accuracy and identify missed suspicious activity
- Review of scenarios, rules, typologies and parameters
- Sampling alerts to validate quality and disposition
- Evaluating data inputs, logic and exception handling
- Incorporating stress testing and statistical back-testing when applicable
Outcome: Independent evidence that your model detects suspicious activity accurately and consistently.
Every engagement is tailored to your institution’s size, complexity, risk profile, products and services, customer base and transaction patterns.
