Look Beyond Compliance When Choosing An Anti-Money Laundering Solution

We are delighted to publish “The Forrester Wave™: Anti-Money Laundering Solutions, Q3 2019” this week.

While we have evaluated fraud management offerings in the past, this report marks the first time we have looked directly at anti-money laundering services. In our 32-criterion evaluation of anti-money laundering (AML) solution providers, we identified the eight most significant ones — ACI, BAE Systems, Featurespace, Feedzai, FICO TONBELLER, IdentityMind, NICE, and SAS — and researched, analyzed, and scored them.

When looking for an AML services provider, there are three primary factors organizations need to keep in mind: maintain compliance with multiple regulations across an array of jurisdictions, reduce costs associated with investigations and suspicious activity report (SAR) filing costs, and minimize impact to customer experience (CX). With the growing number of requirements for a successful AML solution, this space has seen many innovations over the past few years. To meet the ever changing regulatory and operational requirement landscape, today’s AML solutions are much more focused on data integration, usability, ready-made, and productized integration with Office of Foreign Assets Control (OFAC) and Politically Exposed Person (PEP) screening lists, and easy model building, scoring, and investigation.

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In response to these trends, fraud management professionals should consider the following when choosing an AML solution:

· Data integration capabilities: To ingest and process large amounts of transaction data, invest in a solution that can be implemented quickly, offer API- and SDK- based data integration, and provide enough customization options to adapt to an organization’s specific requirements.

· Built-in reporting: Not requiring third-party reporting tools and instead providing an integral workflow for report writing and use improves operational efficiency and lowers the cost of compliance.

· AI Powered Optimization: Augmenting rules-based systems with risk scoring using supervised and unsupervised machine learning methods is becoming the norm in today’s highly competitive market.

The solutions which ultimately rose to the top of this analysis were the ones which provided powerful, comprehensive functionality capable of handling a wide array of use cases and transaction data flows. For a more in depth look at the Anti-Money Laundering Services space, you can find the full Wave report here.