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AI Case Study

HSBC reduces false positives for money laundering detection by 20% using AI to automate the system rules

HSBC automated its anti-money laundering (AML) investigations to increase efficiency and effectiveness in its regulatory compliance. Historically AML was carried out by humans but the bank has turned to Ayasdi and its machine learning software to monitor transactions and automate identification of potential criminal activity.


Financial Services


Project Overview

Ayasdi data scientists did feature engineering, and an HSBC internal modeling team were involved "to replicate the efforts of authorities in attempting to validate what the algorithms are predicting, so that it can be understood and explained as easily as possible. Ayasdi creates a decision tree to show how the decisions are made within the system, aiming to avoid the “black box” problems of machine learning, which cannot be allowed in the tightly regulated finance sector. The analysis that follows involves the AML-specific skills and experience of the HSBC teams, who are able to determine the usefulness and meaning behind Ayasdi’s “clusters”, allowing them to strategically decide which of these unique patterns should be permitted to update their AML rules and systems."

Reported Results

"Ayasdi claims to have unearthed many new cases and patterns directly correlated to fraud – as well as reducing HSBC’s false positives (cases when HSBC’s existing rules would have flagged for laundering risk when no such risk actually existed) by 20%."


The Ayasdi "platform enables an unsupervised way of running the data through a variety of algorithms – all with the goal of finding unique groupings and relationships in the transactional data."





"Industry standard approaches to transaction monitoring and other kinds of financial crime risk detection are largely inefficient, even when they meet regulatory expectations. ...the onus is on the bank to show that they did all that they could to prevent such transactions. Most banks apply myriad rules as filters on transactions (i.e. if you normally do small $200 jewelry transactions, and all of a sudden you send $21,000,000 to Romania, a system might flag the amount, the destination, or both). These rules are not able to be updated enough (and are not built to be flexible enough) to handle laundering threats adequately. Most banks have been employing manual workers to analyze the data and construct new rules, an approach that simply doesn’t scale."



HSBC internal anti-money laundering data

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