South Africa has graced the Financial Action Task Force (FATF) grey list for a little while now; some analysts forecast it won't be removed from the list until 2026. According to Smart Search, South Africa ranks seventh among the worst 20 offending countries for AML events in the last decade, with 122 AML (anti-money laundering) events. According to Banker's Academy, the South African government estimated in 2023 that $2 and $8 billion is laundered every year through South African financial institutions.

Recently, in April 2024, South African parliament speaker Nosiviwe Mapisa-Nqakula was charged with corruption and money laundering. South Africa has been considered a potential and fruitful route for terrorism financing, a status that also led to FATF's greylisting.

This volatile status or situation has everyone on their toes to ensure that the financial web sector becomes a safer place for citizens, business owners, and foreign investors. Advanced technology such as machine learning is frequently explored to provide new solutions for urgent crucial problems such as terrorism financing and money laundering. 


What are AML and CFT?

AML and CFT are not just buzzwords but important measures that South African banks need to implement to ensure financial security and regulatory compliance.

AML, or anti-money laundering, refers to regulations, laws, and procedures made to prevent criminals from disguising illegally profited funds as legitimate income. AML regulations are essential for the financial industry and other sectors to detect and prevent financial crimes, such as money laundering and terrorist financing.

CTF, on the other hand, stands for Counter-Terrorism Financing. Similar to AML (Anti-Money Laundering), CTF includes all the regulations, laws, and procedures made to prevent and detect the funding of terrorist activities. It aims to identify, prevent, and disrupt the flow of funds that terrorist organizations use to carry out their operations.


Why Machine Learning Innovation for AML/CFT Compliance?

Manual measures can no longer keep up with the tasks, and neither can humans stay up around the clock trying to monitor for suspicious patterns and compliance processes, especially with the surge in bank users and customers. Although actors in the industry might have been somewhat apprehensive about the effectiveness of machine learning in compliance, its return on investment proves to be high, as machine learning helps compliance become less redundant, time-consuming, and even more accurate. Machine learning is the bedrock for automated solutions that aid compliance measures and tasks, especially for AML and CFT.

There are several reasons machine learning is suitable for AML/CFT compliance for South African banks; these include: 

1. Scalability and Efficiency

There has been a recent surge of bank users in South Africa; according to Statista, more than 85% of the South African population have a bank account.  The surge in bank users also leads to an increase in transactions. Bank users in South Africa have been predicted to rise even more between 2024 and 2029. With this surge or increase, manual methods can't keep up.  

Machine learning algorithms can process and analyze massive amounts of data quickly and efficiently and are easily scalable. 

Machine Learning can be used to automate repetitive and time-consuming tasks, freeing up staff for more strategic activities. This reduces the workload on compliance officers and allows them to focus on higher-value tasks.


2. Enhanced Accuracy and Detection

Machine learning models can identify complex and subtle patterns in data that might be missed or overlooked by humans. This enhances the detection of suspicious activities and unusual transaction patterns. 

False positives might be a concern, but by learning from historical data, ML algorithms can improve their accuracy over time, reducing the number of false positives and allowing compliance teams to focus on genuine threats.


3. Real-Time Monitoring and Response

Unlike manual systems that work in batches, ML systems can operate continuously, providing real-time monitoring and faster detection of suspicious activities.

Also, machine learning models can adapt to new and evolving threats. As criminals change their tactics, ML algorithms can learn and adjust, ensuring that AML and CFT measures remain effective.


4. Cost-Effectiveness

By automating many aspects of compliance, ML reduces the need for extensive manual labour, leading to significant cost savings for financial institutions. Although machine learning may be considered somewhat costly, the initial investment in ML technology is often outweighed by the long-term savings and efficiencies gained, providing a high ROI. South African banks also get to avoid hefty sanctions and fines that may impact their business. 


5. Regulatory Compliance and Reporting

Machine learning systems can help ensure that financial institutions comply with regulatory requirements by automatically monitoring and reporting suspicious activities. Machine learning systems can keep detailed logs and audit trails of their processes, demonstrating compliance during regulatory audits, which is easier.


6. Machine Learning Enhances Fraud Prevention and Risk Management

Machine learning algorithms can detect potential fraud before it occurs by identifying high-risk behaviours and patterns.

Machine learning also helps in assessing the risk profiles of customers and transactions, enabling better risk management and mitigation strategies.


AML/CFT Machine Learning Solutions By Youverify for South African Banks

Youverify offers a whole suite of compliance software that can help South African banks streamline their compliance processes or measures. These compliance solutions are risk-based and are valuable assets to every compliant team.


1. Transaction Monitoring

Youverify's Transaction Monitoring system utilizes machine learning algorithms to analyze transaction patterns and identify suspicious activity in real-time. This includes transactions with unusually high volumes, transfers to high-risk countries, or transactions that are different from a customer's typical behavior. 

It is also able to accurately classify customers based on automated risk assessment results into high, medium, and risk for equivalent monitoring for compliance.


2. Transaction Screening

Youverify's transaction screening system enables automated, informed decision-making by screening the receiver and sender against third-party government-backed data sources, high-risk jurisdiction checks, and other conditions set by the business for AML. 

Post-screening, the transaction may be frozen if it aligns with some adverse conditions, the sender is alerted and briefed on the process, and the host financial institution can now make an informed decision based on an automated, robust report on the transaction and manual review.


3. Adverse Media Screening 

The Adverse media screening system provides an additional system to vet customers and clients or partners before onboarding or official transactions. It crawls individual and business digital footprints for direct and affiliated negative news before onboarding. 

The automated system can help manage high-risk customers or clients on a case-by-case basis through an intuitive case management system that presents adverse media screening results as actionable and informed decision-making. 


Bottom line 

Machine learning is highly needed for AML/CFT compliance for South African banks. It bridges the limitations of manual methods by providing scalable, efficient, and accurate solutions that enhance fraud detection, regulatory compliance, and overall risk management. With South Africa's financial crime challenges, investing in ML technologies not only ensures better compliance but also allows for a safer financial environment for citizens, businesses, and investors.

Youverify provides simple, efficient, and scalable compliance solutions for financial institutions. Banks are empowered to comply with aml/cft regulatory requirements and itigate risks, while achieving sustainable growth with our AI-powered compliance solution for money laundering. Want to stay aml/cft compliance as a South African bank? Request a demo. It will take less than a second.