The second largest bank in Europe, HSBC is one of the industry’s most recognized brands not just in Europe, but all over the world. However, in recent years, HSBC has been embroiled in a number of high profile scandals ranging from predatory lending to tax evasion to money laundering on behalf of criminal organizations. 

As part of their ongoing strategy to bounce back from these scandals and regain public trust as well as optimize organizational performance in general (which we’ll talk about next week), HSBC is embracing the next generation of data and analytics. 

AI for Anti-Money Laundering and Fraud Detection

Developed in collaboration with AI platform provider, Ayasdi, HSBC’s AI-enabled anti-money laundering uses anomaly detection technology to identify suspicious and potentially fraudulent payments. Using this new solution, HSBC has purportedly reduced false positive investigations by 20%. 

In addition to Ayasdi, HSBC also launched its Global Social Network Analytics (GSNA) platform - a centralized anti-money laundering platform that pulls together data from numerous internal and external sources, providing investigators with a contextual, comprehensive view of customer activities and relationships. 

As HSBC’s Group Head of Compliance Product Management and Compliance Chief Data Officer, Michael Shearer, explained to Diginomica, “This is all about joining the dots- about making sure that we can get the full picture of our customers, both who they are, their profile and what they are doing, their behaviour. Only when you bring those two things together can you really take the right actions to address financial crime risk.”

He also added, “The system does two things for us. One, it detects any behaviour we think might be of concern to us and then allows us to investigate that concern. How do we know that detection is working? Because in its first year we found significantly more financial crime risk than we had examined and acted upon than via manual methods. In the first six months, it also generated more alerts that we thought were of quality than our previous process did in 12. So we have very good reasons to think that it's detecting both quicker and better than our previous methods.”

AI Ethics

When it comes to business ethics, technology is merely a tool and cannot make up for a lack of internal controls or accountability. After all, more than anything else, the outcomes of AI reflect the value of its creators rather than a single, objective truth.

Like many other large entities experimenting with AI, HSBC has built out its own ethical AI and big data framework to help ensure these technologies and the data used to fuel them are not misused. The framework is built around 7 pillars:

• Alignment with HSBC’s overall core values
• Protect privacy
• Start with a clearly defined purpose
• Address unfair bias and decision-making
• Be responsible for AI
• Adapt existing governance to meet emerging needs
• Contribute to the development of best practices

In an interview with Techerati, Dr. Juergen Rahmel, Chief Digital Officer at HSBC Germany and an AI researcher explained the company’s approach to building the framework, “I strongly believe that the full potential of AI can only be realised within a framework that supports trust and operates in a scope that benefits each stakeholder. [Our] key pillars are firstly to develop proper AI systems that can be tested and verified to do what we expect them to do. Secondly, to develop this framework of ethics and moral acceptance of the functions such an AI system could execute. Thirdly, to define the influence that regional differences of mentality and jurisdiction will have on the judgement of acceptability.”

In addition to ensuring that AI and big data are developed/used in ethical ways, data scientists at HSBC are experimenting with AI to make more ethical decisions. 

For example, without proper guardrails, many customers will likely borrow beyond their means. With AI-powered credit and lending tools, HSBC could potentially predict the likelihood of default with more accuracy. On the flipside, these same tools could also help them rethink how they’re currently approaching traditionally underserved markets - groups that have been denied loans for unfair or unsound reasons. 

However, this is only the tip of the iceberg. Stay tuned for next week when we look at how HSBC is using data to transform the customer experience………..

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