How the Pandemic Impacted Money Laundering and Changed the AML/KYC Paradigm

How the Pandemic Impacted Money Laundering and Changed the AML/KYC Paradigm

In the aftermath of the pandemic, it has become increasingly apparent that the traditional methods of combating money laundering (ML) and terrorist financing (TF) are no longer adequate. The global pandemic has exposed serious deficiencies in both public and private sector AML/KYC procedures and it has highlighted the need for a more holistic and risk-based approach to tackling financial crime.

One of the main problems with the traditional AML/KYC approach is that it is overly reliant on compliance procedures and risk assessments that are based on historical data. This has proved to be a major weakness in the current system, as it is no longer possible to rely on past trends and patterns to accurately predict future risks.

Financial Criminals Are Becoming More Innovative 

The pandemic has also shown that criminals are becoming increasingly adept at exploiting new technologies and financial channels to move money undetected. As a result, traditional methods of detecting money laundering and terrorist financing are no longer effective, and new approaches are needed to keep up with the changing landscape.

To effectively fill this gap, several organisations have begun to implement new techniques and technologies in their AML/KYC procedures. One approach involves the use of big data analytics, which is helping financial institutions increase efficiency while leveraging advanced technology that can revolutionise the way money laundering and terrorist financing investigations are conducted.

Big Data Analytics Have Helped Identify Suspicious Transactions 

According to a recent report by the International Monetary Fund (IMF), the use of big data analytics has already helped to identify several suspicious transactions that may have otherwise gone unnoticed. In one case, the IMF report highlighted how big data analytics was used to identify a series of suspicious transactions linked to North Korea.

This is just one example of how big data analytics can be used to combat money laundering and terrorist financing. Other applications include the use of artificial intelligence (AI) and machine learning, which can help identify patterns and trends that would otherwise be difficult to detect.

Overall, the pandemic has highlighted the need for a more holistic and risk-based approach to AML/KYC procedures. To effectively tackle financial crime in the current environment, organisations must embrace new technologies and techniques that can help them stay ahead of the curve.

Transaction Monitoring and the Pandemic

Transaction monitoring is just one of the most common approaches to AML/KYC compliance. According to the IMF, transaction monitoring can be used to filter large amounts of data to identify suspicious transactions with greater accuracy and speed than ever before.

However, this approach relies heavily on historical data to assess risks and identify potential money laundering or terrorist financing activity. As a result, it has fallen short when dealing with the pandemic, which has exposed serious deficiencies in AML/KYC compliance procedures.

According to Clayton Arundel, Managing Director at Black Lantern Capital Partners, transaction monitoring is based on statistical patterns that are not always reliable during periods of high volatility and change. “Transaction monitoring is a critical piece of any AML/KYC program, but it is also important to remember that it is based on historical data,” said Arundel. “During times of high volatility and change, such as we are experiencing now with the pandemic, traditional statistical patterns may not be reliable.”

This highlights the need to adapt AML/KYC procedures to deal with the changing landscape. One way of achieving this is by leveraging advanced technologies, such as big data analytics and machine learning.

Big Data Analytics for AML/KYC Compliance

According to a recent report by Transparency Market Research, the global big data analytics market is expected to grow from $8.5 billion in 2018 to $47.9 billion by 2025. This growth is being driven by the need for organisations to adopt advanced technologies that can help them stay ahead of the curve and combat financial crime.

Big data analytics is a perfect example of such a technology. It enables organisations to process vast amounts of data to identify suspicious activity, determine risk levels with greater accuracy, and create more efficient AML/KYC programs. The use of big data analytics in AML/KYC is also gaining momentum in the financial sector. According to a recent report by Cognizant entitled “Big Data Analytics for Anti-Money Laundering and Countering the Financing of Terrorism Compliance,” financial organisations are already using big data analytics to identify suspicious activity across a wide range of business lines, including payments, collections, trade finance, compliance investigations, insurance claims processing and more.

When implemented correctly, big data analytics can help financial institutions stay ahead of the curve. According to the report, big data analytics can help organisations:

  • Reduce financial crime risk with enhanced accuracy of detection
  • Improve customer experience by identifying high-risk customers and transactions quickly
  • Enhance regulatory compliance by identifying non-compliance risks in near real-time
  • Detect money laundering and terrorist financing networks through sophisticated pattern recognition

Machine Learning for AML/KYC Compliance

Machine learning, a subset of artificial intelligence (AI), is also playing an important role in AML/KYC compliance. According to the IMF, machine learning can provide significant value to financial institutions by helping them identify suspicious transactions faster and with greater accuracy. This approach involves teaching computers how to learn from data, without being explicitly programmed. By doing so, they can identify patterns and relationships that would be difficult for humans to detect.

One of the benefits of using machine learning for AML/KYC compliance is that it can help organisations combat financial crime in real-time. This is because machine learning algorithms can adapt as new data comes in, making them more effective at identifying suspicious activity.

Another benefit of machine learning is that it can help organisations improve their understanding of customer behaviour. By doing so, they can better assess the risk associated with individual customers and transactions.

Areas of Improvement in the Fight Against Financial Crime

In light of the current pandemic, it is clear that financial institutions need to take a more proactive approach to combat financial crime. This includes leveraging advanced technologies, such as big data analytics and machine learning, to stay ahead of the curve.

While traditional methods, such as AML/KYC procedures and compliance audits, are still important, they are no longer enough. Financial institutions need to embrace new technologies to stay ahead of the curve and combat financial crime.

The global pandemic is posing some challenges for financial institutions when it comes to AML/KYC compliance. However, by leveraging advanced technologies such as big data analytics and machine learning, they can overcome these challenges and stay ahead of the curve. 

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