AI for automating anti-money laundering investigations

Summary:

By implementing an AI-powered anti-money laundering solution, the bank was able to reduce its investigative workload by more than 20% and minimize regulatory risks by identifying previously overlooked risk segments.

Client: 

HSBC is a renowned global banking and financial services corporation headquartered in London, UK. With a history dating back to 1865, HSBC is one of the largest and most established institutions in the global banking and financial services sector.

Problem Statement: 

Recognizing the importance of improving efficiency and enhancing the targeted detection of potential criminal activities, HSBC identified the need to automate its anti-money laundering investigations. Traditionally, these investigations were conducted manually by thousands of employees responsible for analyzing data and creating new rules—a process that was not sustainable. Although HSBC had implemented numerous rules to filter transactions, these rules lacked the flexibility and adaptability required to effectively combat money laundering threats. Standard industry practices for transaction monitoring and other forms of financial crime risk detection were largely ineffective, even when meeting regulatory requirements. HSBC aimed to improve operational efficiency in the anti-money laundering field by 3%, with an ambitious target of 5%.

Results: 

• A 20% reduction in false positives, indicating instances where the bank’s existing rules would have flagged potential money laundering risks, despite no actual risk being present.
• Reduced regulatory exposure by identifying previously overlooked risk segments.

AI Solution Overview: 

HSBC collaborated with Ayasdi, a machine learning software company, to develop an AI-driven solution for combating money laundering. This software, created jointly by HSBC’s internal IT team and Ayasdi’s data scientists, is designed to identify suspicious patterns in historical money laundering-related data. When presented with current payment data, it rapidly detects fraudulent patterns and alerts staff to block such payments. The software analyzes various factors, including the source and destination of payments, to detect any unusual behavior.

HSBC’s IT experts assisted Ayasdi in understanding the bank’s internal anti-money laundering data, while HSBC’s modeling team helped Ayasdi develop precise customer behavior models. This collaboration facilitated the seamless utilization and integration of Ayasdi’s models into HSBC’s operational practices, overcoming the typical challenge of incorporating a vendor product.

Ayasdi improved its ability to incorporate and generate features such as transactional data (type, direction, value), customer data (geographical, chronological), and risk data. Using a subset of this data, Ayasdi developed a series of potential segments. Importantly, Ayasdi did not increase the number of groups; instead, it created more intelligent, defensible, and consistent groups using entirely different features compared to those used by the bank. Remarkably, this process was conducted without supervision; Ayasdi’s software autonomously selected suitable algorithms, generated candidate groups, and fine-tuned scenario thresholds until optimal results were achieved.

The presentation and organization of these results are handled within the Ayasdi platform. Ayasdi generates a decision tree to illustrate how decisions are made within the system, aiming to address the “black box” issues commonly associated with machine learning, which are unacceptable in the tightly regulated finance sector. The distribution of customers across these groups was subsequently assessed, independently validated, and integrated into the bank’s existing infrastructure.

References: 

1. Jon Asprey (2023) AI-powered compliance: Transforming banking processes for efficiency and accuracy. https://www.coreconsultants.io/resources/blog/ai-powered-compliance–transforming-banking-processes-for-efficiency-and-accuracy/
2. Artificial Intelligence for Anti-Money Laundering – An Analysis of Solution. https://emerj.com/ai-sector-overviews/ai-anti-money-laundering/
3. Artificial Intelligence for Risk Reduction in Banking: Current Uses. https://www.linkedin.com/pulse/artificial-intelligence-risk-reduction-banking-current-shroff/
4. Bank Reduces Money-Laundering Investigation Effort with AI. https://emerj.com/ai-case-studies/bank-reduces-money-laundering-investigation-effort-with-ai/ 
5. Anti-Money Laundering Solution Deep Dive. WHITE PAPER. https://s3.amazonaws.com/cdn.ayasdi.com/wp-content/uploads/2018/04/22170635/AML_Solutions_Deep_Dive_WP_051617v01.pdf 

Industry: Financial Services

Vendor:  Symphony AyasdiAI

Client: HSBC Bank

Publication Date: 2018