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Practical Considerations When Investing in anti-money laundering Technology

Fighting Financial Crime in the 21st Century

Although 49% of global organizations are victims of fraud and economic crime, many are fully aware of the full extent of their risk exposure.

Traditional rules-based processes to fight financial crime may flag cash transactions over a certain amount or block transactions to certain countries but require a significant amount of bank resources to review flagged or blocked transactions and weed out the false positives.

In addition, a rules-based approach to anti-money laundering is unable to quickly adapt to the latest changes in criminal behavior to avoid detection. This model also focuses on reducing the number of false positives from an investigation but does not factor in missing links between a series of events or customer activities.

Artificial Intelligence (AI) Catches Up with Financial Crime

An AI approach to anti-money laundering does not require rules to identify potentially criminal transactions. Instead, algorithms power the engines that runs AI and machine learning technology. These models operate on large data sets to process patterns, anomalies faster and more efficiently than a human.

Machine learning creates and relates risk profiles across a vast data network to determine whether a bank should continue doing business with a potential client (or not). The system would be trained to identify suspicious transactions over time and identify patterns and connections such as where a customer opens an account relative to their home address or even changes in the customer’s social media presence.

However, considering the breadth of the latest in artificial intelligence advancements such as robotic process automation (RPA), natural processing languages, and graph analytics, may be a mind-boggling affair for any business looking to augment their anti-money laundering technology. There needs to be a balance of enthusiasm in applying these technologies and limiting exposure to unnecessary risks.

To put it simply, it should not automation for the sake of it. The focus of any anti-money laundering team should not be on investing in a machine learning model that cuts down false positives by 40% but to understand the inner workings of the algorithms powering the intelligence.

Practical Considerations When Investing in AML Technology

1) Good quality data is important. Algorithms rely on existing data to make predictions on what may happen based on distances or similarities between data samples. Organizations will need to maintain a high level of accuracy in their internal and external data. Simple data cleansing is not enough. More extensive profiling and auditing of data ahead of screenings ensure that anomalies and inconsistencies are removed ensures that data is optimized. The correct definition and application of rules to customer and list data sources, plus the use of secondary identifiers ensure that false positives are reduced without increasing risk.

2) Focusing on high-impact use cases enables anti-money laundering teams and senior management to see how the technology investment has made a difference. The real value of machine learning models is best unlocked with specific use cases or applications. Banks will need to identify the right use cases where automation can deliver the greatest impact and highest return on investment before embarking on the technology investment. This exercise also ensures that the team is able to articulate what the technology investment will do to both an internal and external audience.

3) Consider the external audience – the regulators. Regulators are gaining a better understanding of how the latest technology can bolster their fight against financial crime and are organizing their fair share of hackathons. The Bank of England recently published a report on how machine learning technology is widely used in anti-money laundering among financial institutions as the need to “connect large data sets and undertake pattern detection is a set-up that lends itself well to machine learning”. Anti-money laundering technology teams need to have explainable AI but also maintain a transparent audit trail to ensure that the bank is not exposed to unnecessary compliance risks or exposures.

Finding the Sweet Spot for AML Technology Investment

Deploying artificial intelligence and machine learning to fight financial crime is not groundbreaking anymore. But it can be a challenge to justify the expense involved in augmenting your current AML tech stack, especially if there haven’t been any major known risk exposures.

However, the greater risk always lies in not knowing what you don’t know. Regulators have taken a very hard stance on financial crime, handing out huge fines to those financial institutions found lacking in rigorous anti-money laundering processes and systems.

Although historically, anti-money laundering compliance has been slow to adapt new technology, an increase in geopolitical sanctions, real-time transactions and cross-border activity has also made it critical for banks to augment their existing AML tech stack and keep up with the current speed of global financial transactions.

Taking into account the above considerations can help ensure that your next investment in anti-money laundering technology will deliver the required return on investment.

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