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Can the Solution to Money Laundering be Found in AI & Machine Learning?

Money laundering is a big problem for many organizations. A huge amount of funds are spent each year trying to fight and manage the issue in order to comply with regulations.

Firms are now turning to artificial intelligence (AI) and machine learning (ML) technologies as a better solution to this ever-increasing problem. But so far this has been quite a slow process, partly because firms are unsure how to integrate AI and ML into their businesses and partly because AI and ML are still so new and not completely understood yet.

Machine learning, in particular, has proved to be very useful when it comes to transaction and activity monitoring. Not only can it teach computers to detect suspicious activity, but it can also teach it to classify the activity as being high, low, or medium risk.

By adopting ML technologies, firms can lower their dependency on human operators, which will save a great deal of time and money. Humans will always be needed when it comes to Anti-Money Laundering (AML) processes, but where they’re needed may be about to change.

MORE – 10 Applications of Machine Learning in Finance

MORE – RPA – 10 Powerful Examples in Enterprise

MORE – Top 50 RPA Tools – A Comprehensive Guide

Before firms can fully implement ML into their businesses a few things need to be in place first including:

  • Clear-cut systems and processes need to be developed and implemented
  • A complete view of the customer to include data sharing among banks should be available
  • High-quality data is needed meaning customer contact details and other relevant information needs to be kept up to date
  • Companies need to have trained staff in both financial services and ML.

Financial firms are aware of these challenges and are working to fix them quickly. Many banks are already using Robotic Process Automation (RPA) within their organizations. Both AI/ML and robotics can either work independently of one another or together depending on the company’s needs. For example, robotics could be used to train AI/ML models, with the AI/ML models then being used to make the decisions.

In regards to AML, as well as other areas of the financial services sector, AI/ML is proving to be of great importance when it comes to improving efficiency and effectiveness. If these changes are to take place in the area of compliance, it’s the customers of the business that will benefit too. Regulator’s jobs will become much easier and the global financial system will become stronger in general.

READ MORE – Top 25 AI Software for the Banking Industry

Source Forbes

 

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KC Cheung
KC Cheung
KC Cheung has over 18 years experience in the technology industry including media, payments, and software and has a keen interest in artificial intelligence, machine learning, deep learning, neural networks and its applications in business. Over the years he has worked with some of the leading technology companies, building and growing dynamic teams in a fast moving international environment.
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