In a recent AYASDI blog post, Jonathan Symonds reviews anti-money laundering and AI at HSBC.
Anti-Money Laundering is a particularly challenging area of regulation for banks and even more so for large, geographically diverse institutions. What makes AML such a difficult problem to solve is that it involves complex data, detailed workflows, and significant human involvement.
In short it is perfect for AI.
This is what HSBC determined in their work with Ayasdi on a notoriously difficult problem – that of bank’s customer’s customers, also known as KYCC (know your customer’s customer). The vast majority (~95%) of AML investigations do not result in a suspicious activity report (SAR). This is called a ‘false positive’ – transactions that are flagged for investigation, but are not suspicious based on a deeper review.
Working with Ayasdi, HSBC was able to achieve a significant reduction in false positives (more than 20%) while keeping their suspicious activity reports at the same number.
The Modern AML Challenge
AML is a massive problem and is estimated to be around $1 to $2 trillion per year and growing. Given that the vast majority of those transactions end up funding bad people doing bad things, governments have stepped up their regulatory requirements, oversight and fines.
Banks have scrambled to respond to the increasing regulatory burden. The result is that the cost of compliance is increasing 50% year-over-year and quickly becoming a drag on earnings at a critical time for financial institutions. The complexity and costs are amplified for large, geographically diverse, financial institutions.
At the heart of the problem is the balance between signal and noise. Too much noise in the form of false positives increases costs for the bank as they need to investigate the suspicious activity reports. Too little signal means that the bank might miss criminals triggering a number of negative regulatory outcomes ranging from fines to physical observation.
Compared to its peer banks, HSBC’s AML infrastructure is the world’s best – so much so that it is often treated as a reference workflow. Current AML processes typically have hand-coded or, in the case of more sophisticated players like HSBC, machine-coded money laundering rule scenarios to evaluate each transaction for each geography or type of business. Subject matter experts encode established patterns such as high repetitive number of small transactions (structuring), money flows in and out of high-risk countries, etc.
Sophisticated institutions like HSBC go one step further and create segments of customers. The segments that emerge, however, are typically static and coarse since they only take into account a limited set of factors about each customer or entity. Moreover, they are done manually and separately for customer data and transaction data and have trouble effectively capturing the complex feature interactions.
This has a massive effect downstream as the rules are applied on a set of coarsely defined segments. When AML rules are triggered, the offending transactions must be investigated and adjudicated – creating the Hobbesian choice of regulatory exposure or throwing bodies at the problem.
HSBC saw past this false choice and transformed its AML process using enterprise grade AI.
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