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Prevent Money Laundering with Machine Learning

With rapid digitalisation of financial services, the conduct of financial fraud is also getting more sophisticated every year. In order to stay ahead of the game, financial institutions must employ emerging technology to prevent fraud including detecting and preventing money laundering.

 

Traditional Anti-Money-Laundering (AML) systems is not enough to combat financial crime, given the high false positives it is generating. Prevent money laundering with machine learning. 

 

Machine Learning is an effective tool financial institutions can equip themselves to have a technologically powerful and intelligent analytical tools to combat money laundering.

Prevent Money Laundering with Machine Learning

How Machine Learning prevents money laundering by supporting Anti-Money Laundering (AML)

Machine Learning for effective transaction monitoring and investigation of alerts to reduces false positives.

A major challenge for AML and transaction monitoring is the high number of false positive that it generates. It is estimated that 98% of these alerts are false positive and only 1-2% are real threats.

 

Machine Learning can automatically investigate these alerts then deactivate false alerts. By doing so, institutions can focus resources in investigating actual suspicious activities.

More so, Machine Learning can classify the type of alerts into different levels – high, medium, low for financial institutions to prioritise high-risk alerts.

 

Two techniques that can reduce rate of false positive include:

Semantic Analysis – identification of correspondences due to redundant data

Statistical Analysis – use of customer information to identify high-risk entities that may likely turnout to be a positive result.

Increase efficiency in Know Your Customer (KYC) and Customer Due Diligence (CDD) procedures.

The use of machine learning to enhance AML activities through improving customer verification process. ML models can be used to detect change in customer behavior through transaction analysis. Suspicious activities can be detected and may trigger investigation as machine learning are designed to spot and identify abnormal behavior.

 

AI and ML algorithms can analyse customers’ transaction behaviour to make predictions about that user in the future. This then spots any behavioral changes no matter how subtle and automatically flag that change.

Robotic Process Automation (RPA) in AML and KYC.

RPA and ML can be combined to create an intelligent automation system for KYC tasks.

Applications are possible in:

Analysis of unstructured and external data

Financial organisations has a large amount of data and information to analyse to ensure KYC and CDD procedures are followed for AML purposes. In order to implement a risk-based system these institutions are always in search of ways to understand each customer through their personal, professional and social background. In order to achieve this, financial institutions must look into customer’s public profiles including social networks, media, open-source data sources and more. It is at this point that machine learning (ML), natural language processing (NLP) and artificial intelligence (AI) technologies can greatly contribute in analyzing unstructured data and pointing out connections and risks.

If you want to adopt powerful technology to improve your behavioral monitoring and prevent money laundering with machine learning, get in touch with AI Consulting Group.

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