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Cognitive Computing will Transform the KYC/AML Process in Banking

Banks may soon find needed relief from the burgeoning expense, risk and compliance challenges required by Bank Secrecy Act / Anti Money Laundering regulations. Recent advancements in advanced technologies such as Big Data, Cognitive Computing and Machine Learning are showing promise in cutting manual activities in the process while reducing risk. In fact, these capabilities may make it possible to cut the manual efforts of Know Your Customer (KYC) compliance by as much 50% while improving the bank’s overall ability to reduce the risk of Money Laundering on a near real time basis.

Banks are looking for cost relief, efficiency, and accuracy that will allow them to conduct compliant KYC at all levels from the more basic Customer Due Diligence (CDD), through the more intensive Enhanced Due Diligence (EDD) processes. According to Thomson Reuters, banks spend between $60 million and $500 million a year on KYC compliance alone. The key capabilities that the advanced technologies add to the KYC/AML process include:

  • Much improved and less manually time intensive efforts to risk rate a potential customer by bringing together and analyzing multiple sources of structured, unstructured, internal, external and social media data


  • Near real time updates to customer risk rating capabilities based on the ability to constantly monitor changing information from all relevant sources

  • Monitoring of transactions across historical data sets to better understand the risk of an individual transaction that has set an alert.

We don’t see these capabilities as a replacement to traditional alert management systems used in the current KYC/AML processes, however, these capabilities will significantly improve manual and data intensive processes and workflows while helping to mitigate ongoing KYC/AML risk.

The mitigation of KYC/AML risk starts with a clear and full understanding of money movement between related parties. Today, due to the siloed nature of systems across bank product lines and the sheer volume of external data sources, this is a difficult process. In addition, understanding the potential relationship between parties is a difficult task, due to the lack of true customer master data management and a governed enterprise data lake in most banks.

The volume of internal and potential variety of external data sources to review easily creates KYC analyst overload. The review and analysis of these sources is a largely inefficient, inaccurate, and time consuming process that often results in a great deal of backlog within a banks’ KYC department. Cognitive computing capabilities, such as IBM’s Watson, are continuing to excel at reviewing large data sources quickly; presenting the analyst with relevant customer facts.

For example, businesses tend to leave a data trail across the internet on social media sites, news sources, publicly available government information sources, etc. This “data exhaust” from the normal course of doing business is in identifying the risk- a potential customer represents to the bank. In fact, the lack of data is pertinent in and of itself. Therefore, analysts would expect to see data exhaust from legitimate businesses. The lack of this exhaust is of interest and would require more investigation, and a shift to the more intensive EDD process, on the part of a KYC analyst.

Cognitive computing capabilities are quickly becoming more adept at searching through data sources and pointing out the most relevant information to the KYC analyst as well as identify a lack of information, which may help identify a “shell” business set up to potentially launder money. In addition, Cognitive computing can identify business relationships (via link analysis) between related or associated parties displaying potentially suspicious combinations which would warrant further study by a more senior investigator. The KYC investigator would be able to set up ongoing monitoring of these varied data sources to continuously monitor a customer’s risk profile. As significant changes are noticed by the Cognitive system, a KYC investigator could be alerted.

A positive benefit of this capability to better and more quickly risk rate a potential customer is the reduction of False Positive AML alerts all of which must be reviewed or investigated. This would vastly reduce investigator workload reducing the need for lower level resource requirements.

Machine learning is also a promising capability in that it could provide continuous monitoring of transactions and be able to better identify if a particular transaction is worthy of follow up investigation given the systems analytics of historical transaction patterns and behaviors. A major advantage of machine learning is that previous transactions will establish a pattern of customer behavior. Thus, allowing the system to flag truly new and unusual events or provide the AML investigator with an analytical context to understand the nature of the transaction. The net result of this is to focus investigator time on those money movement transactions most interesting from an AML risk perspective.

Governed enterprise big data (lake) environments have the potential to bring together the complete picture of money movement between related or closely associated parties within the bank itself. The KYC/AML team is currently faced with a siloed data environment across bank Systems of Record (SOR’s). Fraudsters often attempt to take advantage of these data silos moving money between associated party accounts. Bringing the transactional data together from the various SOR’s and understanding the relationships between parties (Customer Master Data Management) is a key step to understanding and reducing the potential for Money Laundering within a bank. A governed big data environment would also be a very solid foundation for applying the cognitive and machine learning capabilities described above. Given the size of recent penalties levied by regulators, the business case for building a governed big data environment is strong.

Interestingly, the same big data required for KYC/AML is also the same data needed for marketing and revenue growth analytics, such as next best offer, increasing account relationships per customer and customized pricing. Unfortunately, in the absence of a strong enterprise data governance environment, compliance marketing teams, etc. are forced to operate in I separate data and analytics environments. This, in turn, further increases bank cost structure, without establishing a true risk or a 360 degree view of the customer relationship.

With increased development initiatives underway at several tech companies, over the next one to three years, we will see these capabilities mature and begin production operations. This has started in forward thinking banks that have the vision and technical acumen to understand and harness the power of these opportunities. This requires, for instance, Data Scientists to work more closely with Compliance teams to bring advanced capabilities to the KYC/AML process.

Given the potential upside of fine avoidance, MRA’s, Consent Orders and Legal Actions while strengthening the overall banking system and lowering bank expense ratios it appears the time is now to apply these advanced technologies to one of banking’s toughest and most important issues.


For More Information


For more information, please contact Michael Andrud, President, FinResults, Inc. (michael.andrud@finresults.com).



About the Author: Michael Andrud has significant experience as a senior level banking executive, having been an Executive Vice President at leading financial institutions, including roles as a transformative Enterprise Chief Information Officer and Chief Data Officer. More recently, he was the lead Banking Data & Analytics Partner for IBM’s Global Business Services organization. Michael founded FinResults, Inc., which provides financial institutions with advice and solutions to some of their most complex business problems by leveraging the capabilities of the Cloud, big data, Artificial Intelligence, advanced analytics, and process robotics.

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