Cross-industry collaboration can end AML data 'paralysis'

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Issuers are collecting overwhelming amounts of data in an effort to improve anti-money- laundering and know-your-customer functions, an update advocated by The Clearing House.

However, in trying to complete this task, many experience analysis paralysis, an inability to escape the mountain of data and create actionable insights. To recover, issuers need to collaborate with industry peers and external third party data providers, using an enterprise-level mindset to move from analysis to data and information enrichment.

Financial institutions can no longer operate in a vacuum, and need to instead collaborate with peers facing similar regulations and challenges. An isolated approach for tackling such a complex issue contributes to increased operational costs, inefficiencies and wasted resources. These fundamental shifts will allow banks to identify and prioritize high-risk alerts and efficiently mitigate money laundering activities.
All dedicated resources, anti-money-laundering investigators and analysts, have the same goal of creating a secure network for transactions and detecting those that exhibit suspicious characteristics to determine if they violate AML regulations. Additionally, every agent brings a necessary specialty to AML/KYC (know-your-customer) functions, so partnering together will allow for quicker change and more success.

In addition to collaboration, companies can break through analysis paralysis by creating a consistent, enterprise-level onboarding process to develop a holistic view of customers. A strong foundation for creation of customer and client profiles is necessary when AML platforms are attempting to detect suspicious activity. If the initial data is inconsistent and detached from the enterprise, it is likely red flags, such as increases in account balance or large withdrawals, could go unnoticed by the system, as opposed to filtering data for more thorough suspicious activity reports.

Finally, data only provides its true value when we have confidence the data models have demonstrated predictive indicators, stability and accuracy to identify potential non-compliant activity. So, at some point, the action must transform from mining data to unveiling findings. Financial institutions must consolidate data collection into a consistent model approach and execution and evaluate the impact on increasing the effectiveness of AML monitoring via data cleaning and enrichment of legacy records.

It is easier to fall into analysis paralysis as pressures arise from the growing number of regulations and improvements. By working together to solve the AML/KYC issues facing the industry, we can create a system that more accurately protects our financial markets and customers from activity from nefarious parties.

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