The Future of Anti-Money Laundering Compliance

Anti-money laundering (AML) compliance departments are traditionally people-heavy and technology-light. Within the next five years, this is set to reverse. AML compliance departments are set to make large investments into technology that will significantly reduce staff count.

Below are three areas that will see some of the most impactful changes:

1. Investigations

With increased fines and regulatory pressures, financial institutions are taking additional precautionary steps to identify all suspicious activity. As a result, AML investigation departments have greatly expanded and/or opened new operational centers in low-cost cities to handle a growing number of alerts. While the new centers are adding capacity, the work in pulling data for a single case can often require over 50 percent of an investigator’s time. The issues of data access and storage were largely created when financial institutions merged and did not make the proper IT investments. Consequently, companies are now investing in technology to make data pulling a more seamless process. Financial institutions are relying on in-house experts, outside consultants and/or external vendors to streamline how data is stored and accessed. Each financial institution has unique challenges that require unique solutions.

In addition, the creation of standard templates for common and simple investigations is becoming more standard in the AML compliance industry. With increased template utilization, investigators will be able to complete the most common cases with minimal modifications. By 2023, compliance departments will automate the most typical cases (i.e., structuring and interstate cash) by incorporating the latest technology. In the future, humans will primarily be required for complex investigations and to review machine-produced cases.

2. Know Your Customer

Today, know your customer (KYC) reviews are a time-consuming and a manual process. Over the next few years, financial institutions will implement automatic tools to streamline the KYC process and gain a better understanding of their clients. Financial institutions can utilize the technology outlined below to enhance their KYC process:

  1. Client is prompted to take a picture of a government-issued identification on their mobile device
    • Financial institutions can then automatically authenticate that identification
  2. Client is prompted to take a selfie on their mobile device
    • Financial institutions can then automatically validate that the selfie matches the picture on the government-issued identification initially provided
  3. Client connects social media account(s)
    • Financial institutions will be able to understand clients’ interests, sentiments, personalities, social connections and interactions, and life events

When a client takes a picture from their mobile device, the financial institution will also receive the client’s location, device identification number and type of device operating system. With this new data incorporated in a machine-learning model, fraud can be prevented and reduced.

3. Machine Learning

The majority of financial institutions utilize fixed rules in their transaction monitoring systems to identify suspicious activity. Only 2 percent of system-generated alerts result in a suspicious activity report filing with the applicable regulatory authority.1 Utilizing machine learning will enable financial institutions to more effectively and efficiently identify suspicious activity. With machine learning, the computer learns as it is exposed to new data. The computer can then identify suspicious activity that it has not been specifically programmed to identify. This is helpful in detecting anomalies that a traditional monitoring system would not be able to identify. In addition, machine learning allows computers to be trained to risk rank alerts, allowing financial institutions to more effectively manage their compliance program.

Conclusion

Within the next five years, cutting-edge AML compliance departments will dramatically change how they investigate suspicious activity, conduct KYC and operate their automatic monitoring programs. The majority of investigations will be automated, KYC will be conducted in a seamless manner and machine learning will more effectively identify suspicious activity. As a result, the need for humans will move from conducting simple investigations/KYC reviews to operating as anti-financial crime specialists and providing guidance to their technology colleagues.

David Zacks, head of compliance, Auka, Oslo, Norway  david@auka.io

  1. Joshua Fruth, “Anti-money laundering controls failing to detect terrorists, cartels, and sanctioned states,” Reuters, March 14, 2018, https://www.reuters.com/article/bc-finreg-laundering-detecting/anti-money-laundering-controls-failing-to-detect-terrorists-cartels-and-sanctioned-states-idUSKCN1GP2NV

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