Why Data Analytics Should Start with Self Service Data Prep Background

Another Anti-Money Laundering Fine: Time to Rethink the Process

April 12, 2018
· 2 min read

We continue to see vivid examples that demonstrate the importance of complying with anti-money laundering (AML) regulations – and the severe consequences that can transpire for failing to do so.

In unfortunate news, the seventh-largest bank in the United States was recently charged by federal authorities for neglecting AML rules. Federal prosecutors reached an agreement with the bank to defer prosecution, provided the bank can demonstrate improvement of its monitoring of customer transactions.

Ultimately, the bank agreed to pay various fines and penalties totaling $613 million to settle the charges from the Justice Department and cases brought by other regulators.

This hefty fine paid by the bank pales in comparison to earlier punishments imposed by the government. For instance, in 2012, federal prosecutors settled a money-laundering case against banking giant HSBC for a whopping $1.9 billion.

In order to accelerate investigations of merchants whose sales patterns indicate potential money laundering, banks and financial institutions (FIs) need to possess complete knowledge of their clients and continually monitor and measure their activities and transactions.

To ensure compliance, FIs must employ Know Your Customer (KYC) and Customer Due Diligence (CDD) solutions.

The challenge for FIs is that KYC and CDD processes require unifying high volumes of client data from multiple sources and linking millions of deposits and transfers every few days. In many cases, individual transactions may be missing, dirty, or have unexpected values. This is where quality is king!

To complete and validate the data, FIs typically assign large technical staff – but this approach drives up costs and leaves a wide margin for error, and ultimately, IT possesses neither the knowledge nor the context around the data. This introduces delays, as IT then must exchange findings with their business team counterparts.

Self-service data preparation tools like Data Prep enable line of business staff at FIs to get directly involved with cleaning the data, rapidly improving the quality of transactions, and producing data sets that support timely and accurate AML investigations.

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Value-Driven AI

DataRobot is the leader in Value-Driven AI – a unique and collaborative approach to AI that combines our open AI platform, deep AI expertise and broad use-case implementation to improve how customers run, grow and optimize their business. The DataRobot AI Platform is the only complete AI lifecycle platform that interoperates with your existing investments in data, applications and business processes, and can be deployed on-prem or in any cloud environment. DataRobot and our partners have a decade of world-class AI expertise collaborating with AI teams (data scientists, business and IT), removing common blockers and developing best practices to successfully navigate projects that result in faster time to value, increased revenue and reduced costs. DataRobot customers include 40% of the Fortune 50, 8 of top 10 US banks, 7 of the top 10 pharmaceutical companies, 7 of the top 10 telcos, 5 of top 10 global manufacturers.

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