Bsa Aml Risk Assessment Matrix
Bsa Aml Risk Assessment Matrix – Anti-money laundering (AML) models have historically relied on qualitative, expert judgment components. Today, these models still rely on expert judgment but are coupled with more sophisticated scoring algorithms and contain other quantitatively derived components such as segments and thresholds. The increased sophistication of modeling techniques and widespread use of models have undoubtedly played a major role in the rapid growth of the AML industry. As the quantitative rigor behind these AML models increases, regulatory bodies are incorporating analytical and statistical experts into their AML examinations and assessments. AML models must follow guidance from OCC Bulletin 2011-12, which describes the elements of a sound program to effectively manage model risk, namely the need for independent review and model validation. At the same time, the widespread adoption of these AML scenarios and typologies, which are considered models, has introduced new risk management challenges. Quite simply, how do we know that our AML models are working as intended?
Across the industry, we appreciate the potential improvements that can result from greater collaboration between industry leaders, academic researchers, and regulators. Given the reputational implications associated with the accuracy and effectiveness of AML models, issues related to model validation are an obvious concern for the industry. Errors or incorrectly specified models can lead to costly retroactive or other regulatory penalties. The points discussed here are intended to provide clarification and specific suggestions for improving the AML model validation process.
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When approaching AML model risk management, the first activity is to define the population of AML scenarios, typologies and methods used in the financial institutions (FIs) transaction monitoring environment. Once you have a complete inventory of all the scenarios, typologies and methods in the AML environment, the model risk practitioner will evaluate each scenario against the model definition. Modeling is defined by the OCC Bulletin 2011-12 as a quantitative method, system, or approach that applies statistical, economic, financial, or mathematical principles, techniques, and assumptions to process input data into a quantitative estimate. In the AML world, this definition applies to transaction monitoring models:
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AML models include transaction monitoring software vendor products, large in-house transaction monitoring systems, customer risk assessment models (if with a quantitative scoring component) and alert risk scoring models (again, if with a quantitative scoring component). A transaction monitoring product, in its basic form, is not a model. However, when combined with an optimization method and strict segmentation definitions in production, this additional quantitative rigor is what characterizes the product as a whole as a model.
In addition, individual methods are qualified due to their quantitative nature, and are usually considered. An example method includes methods used to define a process for optimizing thresholds, scoring events, generating segmentations, and performing up/down line analysis.
Once the models are determined, the risk practitioner should use a ranking/tiering process to categorize the models into risk levels to establish appropriate model control and correlation to the risk that each model represents. Most FIs have a model governance group that ranks the risk of their FI models across the board with a variable of low/medium/high risk. Since OCC 2011-12 was published, AML models have been classified inconsistently in our industry and validating AML models is a foreign concept.
As with so many unknowns, the choice quickly became to treat AML scenarios/typologies as models and treat them all with the highest risk. Where reputational risk is high, the quantitative rigor and financial impact associated with AML models is low. Now that many practitioners, statisticians, regulators and financial quants are looking “under the hood” of these AML models, we feel that AML models do not warrant a high-risk classification. Classification of AML models as low risk or moderate risk is more consistent with mathematical/statistical rigor in AML models.
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Once the models are written, appropriate model controls can be established based on the level of risk presented by each model. The higher the risk, the more stringent the model checking and the more rigorous and routine the optimization and validation. The lower the risk, the more basic model controls should be established and the optimization and validation will be less frequent than at higher levels. Therefore, the frequency of optimization and validation is discussed below in relation to established level/rank of the model.
There are several compelling reasons why AML programs should pay attention to model validation, which is basically the activity of monitoring model performance. Considerations of size and scale are factors that increase the importance of carefully monitoring model performance. Age is another factor. Many AML scenarios/typologies were created five to 10 years ago with no improvements. Models should be optimized/tuned every 12 to 18 months. An independent validation, by a team other than the analyst who optimized the model, should be done with the same frequency. Using the risk ranking process from above, higher ranked models will be optimized/validated closer to the 12-month cycle; While low risk models are optimized/validated closer to the 18-month cycle. Some major typologies cannot be classified as models due to the special model definition of FIs. However, these non-models should also go through the optimization and validation process, but with a slower frequency of every 24 to 36 months.
Now having defined the model environment, associated model risk level and frequency of optimization/validation activity, AML practitioners can begin the model validation process. As model risk becomes a larger factor in the overall risk assessment of FIs, model validation becomes important. Model risk management is a process in which AML practitioners must 1) be able to demonstrate to senior management and regulators how their models are performing against expectations and 2) know how risk exposures fit within defined bands of acceptance.
Unlike traditional market and credit risk modeling, AML models do not provide precise results that can be backtested against a previous dataset. The AML model aims to identify abnormal activity that may be suspicious upon investigation. The AML model is not intended to capture proven suspicious activity. The true productivity of AML models is unknown, as even the best data scientists cannot prove that 100 percent of all money laundering activity is detected. The definition of AML model “productivity” may vary from model-to-model; Since true productivity is unknown, it is important to relate productivity to the intended use of the model, as discussed below.
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Validation procedures, and related documentation and reporting, should be relevant and clearly linked to the purpose of the model. AML executives should consider:
Many of these questions have technical components that are usually addressed with detailed statistical considerations. Recognizing that there is no common yardstick against which AML modeling can be measured, AML practitioners can use the framework to think about the purpose of the model, the use of the model, and expectations for results for evaluating AML models.
It is important to clarify/document the purpose, use and expected results of the model. Documentation should include key input/output decisions made during the development and/or implementation of the model. Documenting the expected outcome and expected outcomes of the model is also an important component, but more challenging to define. Expected results can be obtained using standard statistical tests, such as the Gini coefficient and the KS statistic. FIs concern themselves with selecting the best sampling method and/or method of statistical performance analysis. Developing effective procedures and using sound judgment are just as important as accurate statistical measurement techniques.
Validation methods should be closely related to how the model is used. For example, in cases where FI uses a cluster model, validation criteria should include an assessment of the model’s ability to achieve high intra-cluster similarity and low intra-cluster similarity. However, if only the rank order properties of the FI score are used, validation should focus on the model’s ability to differentiate risk over time. These metrics and measurements do not apply to all scenarios on all FIs. A more general point is that when evaluating a model’s performance, one must depend on a clear understanding of the intended use of the model.
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In development, models should establish clear means for the purpose of the model. Models should be valid according to clearly understood expectations. Rather than establishing some arbitrary statistical criterion for model performance, the central question for validation is whether the model performs as intended, is robust and stable over time, and produces results that are at least as good as alternative approaches. A clear understanding and documentation of expected performance is an essential and fundamental foundation upon which all validation approaches must be built.
While there is general agreement that the accreditation process is part science and part art, there is a need to establish clear quantitative criteria as part of the accreditation process. Such criteria need not only be a measure of model performance, but they are essential for establishing scientific rigor and discipline in the validation process. Classification and clustering models are discussed below, along with associated, recommended validation tests.
Several transaction monitoring scenarios can be classified as classification models. Classification models should be evaluated based on how well they separate “good” and “bad” account behavior over time. A common approach is to consider the difference between “good” and “bad”.
An effective classification tool should lead to this
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