The Convergence of AML and Fraud Analytics has been extensively discussed in the past few years. While many financial institutions still separate their teams from handling fraud and AML analytics, here are five solid reasons which warrant the acceleration of applying fraud analytics to AML and vice versa:
Same techniques
The same set of techniques is being utilized at the core of financial crime detection and deterrence. Throughout the past few decades, we have seen simple algorithms as scorecards evolve into more advanced Machine Learning techniques. However, they always apply in the same fashion to both fraud detection and AML.
Same model validation framework
Same model validation framework: both fraud detection and AML analytics are models which must be managed within a Model Validation Framework. The components of the framework are the same: they must encompass data quality, governance, methodology and documentation to ensure model risks are properly managed.
Same data
Not only do fraud detection and AML share common datasets, but it is more important to recognize that some datasets currently deemed as “specific” to one can actually provide more insights to the other through convergence analysis.
Enhanced view
When data is shared and evidence is viewed holistically, an enhanced view of entities can be achieved. This will enable new algorithms and techniques to be applied.
Increasing staff productivity
Not only data scientists and analysts can benefit from the convergence, but the institution can make more efficient usage of the resources when the above overlap and synergies are recognized.
Conclusion
As “dirty money” needs to be laundered (including fraud-related monies), there is no reason why we shouldn’t look at fraud analytics and AML analytics together.
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