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The future of Anti-Money Laundering Compliance

It is common practice for AML compliance departments to be traditionally people-heavy and technology inefficient. This is set to change in the next five years or so, with AML compliance departments planning on making large investments into new technology that significantly reduces staff count.

Some of the areas that are due for the most change include the following: Investigations, Know your customer and Machine Learning.

Financial Institutions are taking additional steps to identify all suspicious activity as a result of being exposed to increased fines and regulatory pressures. As a result, AML departments have expanded their operational centres in low-cost cities, to handle the growing number of alerts. While new centres are adding capacity, the work in pulling data for a single case can often require over 50 per cent of an investigators time. The issues associated with data access and storage were largely created when financial institutions merged and did not make the proper IT investments, as a result, companies are now investing in new technology to make data pulling a more seamless process. Financial institutions are relying on in-house experts and outside vendors to streamline how their data is stored and accessed. Each financial institution is different and has unique challenges that require unique solutions.


The creation of standard templates for common investigations is becoming more standard in the AML compliance industry. With an increased template utilisation, investigators will be able to complete the most common cases with minimal modifications. By around 2023, compliance departments will automate the most typical cases by incorporating the latest technology and humans will primarily be required for complex investigations and to review machine produced cases.


Know Your Customer (KYC): KYC reviews are generally a time consuming and manual process. Over the next decade or so, financial institutions will implement automatic tools which will aim to streamline the KYC process. Financial institutions will aim to utilise the technology outlined below to enhance their KYC process:

1) Client is prompted to take a picture of a government identification on their mobile device

  a) Financial institutions can then automatically authenticate that identification

2) Client is prompted to take a selfie on their mobile device

b) Financial institutions can automatically validate the selfie with the picture of the government authenticated identity.

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

Incorporating machine learning into transaction monitoring systems enable financial institutions to identify suspicious activity more efficiently. 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 very helpful in detecting anomalies that a traditional monitoring system would have difficulty in identifying. In addition, machine learning allows computers to be trained to risk rank alerts, allowing financial institutions to more effectively manage their compliance program.


In the near future, cutting-edge AML compliance departments will dramatically change how they investigate suspicious activities, conduct their 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.





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Shaun Thomas

Shaun Thomas

Product Manager


Member since

29 Oct 2018



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