How to start using AI to combat money laundering – Part 1
According to a recent Europol report, combatting global money laundering continues to be a challenge. “The banks are spending $20bn a year to run the compliance regime … and we are seizing one percent of criminal assets every year in Europe”, Rob Wainwright, director of Europol told Politico recently.
At an institutional level, the volume, variety, availability and location of data, along with changing regulations and fraud schemes keeps many anti-money laundering (AML) compliance officers up at night.
With this in mind it is not surprising that many in the field are looking to new solutions that include artificial intelligence (AI) and, more specifically, machine learning (ML) to help propel the effectiveness and performance of their AML programs.
However, while there is much enthusiasm for the promise of what new technology can offer, there is also much hesitation since many don’t understand how AI and ML work and what it can and will do for them.
Before getting into what the technology can do for AML programs it is worth noting that the one thing AI and ML will not do is completely replace the need for people. Human or natural intelligence has to be coupled with machine intelligence in an effective AML program. However, what machine intelligence can do is look at both structured and unstructured data to detect patterns or clusters of activity that may be out of the ordinary and potentially fraudulent.
It should also be noted that solution providers do not “program” the AI system to detect threats – they help financial institutions to train it to do so. This requires people who are subject matter experts and leaders within the AML compliance and fraud space to work in conjunction with the AI system to determine whether abnormal behaviours or clusters of transactions are suspicious or fraudulent.
Starting with anomaly detection
The first model that should be implemented by every organization is anomaly detection. It is an advanced technique that uses an organizations’ data and data science to detect behaviour that doesn’t fit within the expected normal behaviour profile. There are different types of anomalies that can be detected, including:
- Point anomalies are single instances (or outliers) that fall outside of the normal behaviour. These include sudden large deposits or credits to an account.
- Contextual anomalies are data points or transactions that appear anomalous without the appropriate context. For example, increased spending during the month of December would appear anomalous in itself but in the context of the holiday season, would be considered normal. A similar situation may appear if a client is starting a new business
- A collective anomaly is when a number of data points are considered anomalous but the values of the individual data points are not themselves anomalous. An example of a collective anomaly includes a case where deposits of $4,500 are made into a chequing account. It may be unusual to have frequent deposits of such amount into a personal account but the value of the deposit is not flagged as suspicious.
It should be noted that to have an effective anomaly detection system, organizations need to implement a solution that detects all the different kinds of anomalies – not just point anomalies.
It should also be noted that not all anomalies are fraudulent – they are simply outliers that require further investigation. To help compliance staff manage the number of anomalies that need investigation, the technology should be configured to rank alerts by risk level.
In Part 2 of this blog, we will be discussing how transaction monitoring using machine learning and robotic process automation can be used to combat financial crime. You can also jump start the conversation by contacting us.
About Khaled Ghadban
Khaled Ghadban (LinkedIn) is Director of Analytics and Data Science at CaseWare RCM and has more than 20 years of experience in the data analytics space in various industries, including financial services. He is a regular contributor to conferences and a frequent speaker on the topics of analytics and cognitive (AI).