ML for AML – 8 reasons to use it for transaction monitoring

The digital revolution has brought us closer. It has creatively disrupted our lives and transformed it by bridging the gap between the customer and the seller. For customers, the benefits often outnumber the risks. One can buy a movie ticket, order things online, apply for a loan or a credit card in a matter of seconds. From the ubiquitous ATM to online payment gateways, the rapid transformation in the financial sector has redefined banking. One of the most accelerating trends noticed in the banking sector is the electronic transfer of funds. In consequence of increased digitalization, institutions have been forced to continuously monitor and detect any suspicious activity. Banks are required to have procedures in place to prevent fraudulent activities from materializing and there is an increased need for caution. Transactions now happen round the clock, across time zones and banks must always be alert.

How can Machine Learning help in Anti Money Laundering:

1. ML can prove effective in reducing false positives and use advanced techniques such as financial networks visualization, transaction flow analytics to assist data scientists in curbing financial crimes.

2. Robotics, AI and ML can exist independently and support each other’s capabilities. Robotics can prove to be helpful to train AI and ML models which can aid decision-making to a great extent.

3. To efficiently curb financial crimes, an analytical data lake that brings together details like transactions, accounts, alerts and other financial crimes related data will significantly reduce the time and effort of data scientists.

4. Efficient AI and ML solutions check significant spikes in transaction volumes by closely monitoring high-risk jurisdictions and keep a watch on the unusual movement of funds. Predictive analytics platforms, powered by machine learning overcome the shortcoming of traditional process-based systems. They deploy continuously evolving new data points from the user analysis and bring in enhanced security features to prevent frauds.

5. ML could be implemented to improve event-to-SAR conversion rates, but the need for human intervention will be a must. What could be a possibility soon is a hybrid human / AI model to represent the next generation of AML TM alert operations teams. The adoption of ML and AI for AML is just the beginning of financial services compliance and resilience automation.

6. AI and ML have an advantage of a high accuracy rate and superior data quality which aids decision making. Financial institutions and banks leverage data for better efficiency in operations through improved predictability.

7. Another advantage of AI-based machine learning processes is the dynamic workflow it helps create due to the built-in self-learning capabilities which identify transactions with genuine risks.

8. Machine learning models have also been developed to detect changes in customer behaviour by analysing transactions. The AI Rule engine-based monitoring gives enriched data to flag any suspicious activity for investigation at a later stage. This prevents possible fraudulent activities at the initial stage thereby avoiding potential frauds.

Keeping the future secure

It is time financial institutions moved away from traditional rule-based models. AI-based dynamic predictive models allow banks to perform better in real-time and help in large volume transaction-based detection techniques. These platforms can carry out accurate confidence scores and this helps in fast deployment time and enhances business functions considerably.

Stay ahead of Money Launderers using ML

The banking and financial services sector has witnessed tectonic shifts and adapted to the changes as per times. Adopting cutting-edge new AI-based platforms will help bankers to process large amounts of data seamlessly. Banks will also be able to deliver accurate results with improved alert predictions while maintaining compliance with regulatory laws. Though new technologies will play a potentially huge role in Anti Money Laundering, they cannot fully replace human judgment which remains an important element in these processes. Our aim should be to enrich the existing AML solutions with right technologies in artificial intelligence while balancing human intervention in the process.