HSBC Holdings is a multinational banking and financial services holding company and is ranked 99th on the Fortune 500 list. The bank has worked with multiple AI vendors and provided evidence of success that other top banks lack. According to our AI Opportunity Landscape research on how the top global banks are using AI, besides Deutsche Bank, HSBC is the European bank with the most AI initiatives. HSBC’s AI initiatives account for 12.5% of the AI initiatives at the European banks in our analysis.
In this article, we cover the bank’s work with two vendors in particular:
- Ayasdi: AI for anti-money laundering and reducing false positives in fraud detection processes.
- Element AI: AI for compliance and predicting services customers might need.
We begin our dive into HSBC’s AI initiatives with its anti-money laundering solution from Ayasdi.
HSBC’s AI-enabled Anti-Money Laundering Solution
In 2018, HSBC partnered with Ayasdi to develop an AI-enabled anti-money laundering solution. The software can purportedly identify patterns within historical data that may point toward money laundering, which helps the bank stop payments before they violate regulations. Ayasdi claims to have reduced HSBC’s false positives by 20% and found numerous behavioral patterns directly related to fraud.
The solution was developed in collaboration with HSBC’s IT team and Ayasdi’s data scientists and developers. The IT team purportedly helped Ayasdi access and parse the bank’s AML data. Additionally, the internal modeling review team helped Ayasdi create more accurate models for customer behavior. The bank could then understand these models because they were made using terms they were already familiar with.
Ayasdi’s solutions are primarily based on anomaly detection technology, which is helpful for recognizing deviations from a pre-established norm. They claim their software analyzes the sources and destinations of customer payments to make sure the funds are coming from legitimate sources.
Anomaly detection software seems to have worked well for HSBC and other banks looking to improve their defense against money laundering. This is because well-trained algorithms may recognize deviations much faster than human analysts at computers.
HSBC’s Interest in AI for Trade Flow and Document Search
In 2019, HSBC announced a partnership with Element.AI, a firm that primarily offers AI solutions for trade flow and document search. They also offer AI business management solutions such as an access governor that determines which employees can access which sets of data.
HSBC claims their goal for this partnership is to improve global regulatory compliance in areas such as anti-money laundering rules. Additionally, the solution would help them predict which services and product solutions their clients may need in the future.
HSBC may be able to achieve its goal of global compliance using AI resources from Element. An NLP solution would allow them to automatically tag new information with metadata for greater searchability and transparency.
This allows a client company to more accurately retrieve data requested by a customer or auditor. One example of this is a situation where a customer requests all of their personal data be deleted from the company’s database.
Element AI’s solutions most likely run on some combination of natural language processing (NLP) and predictive analytics technology. NLP could be used to create document search applications and automatically tag documents with metadata.
HSBC likely intends to use Element’s predictive analytics engine to study customer data for information that could indicate potential problems. For example, Element’s software may be able to detect metadata tags pertaining to the type of customer support issue for each email. It could then determine that the majority of their customer support tickets come from their mobile app not recognizing a verification code.
Most of these customers could report a problem where the app would not allow them to log in until they have requested a second code and used it for verification. HSBC may be able to recognize this problem as it arises and begin working on a fix earlier than if detected and reported manually by customer service agents.
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