2019 payments forecast
AI in banking:
navigating the road ahead By Elina Mattila
anada’s financial services industry has long been a leader in applying artificial intelligence (AI) technology. The Royal Bank of Canada, for example, is investing tens of millions of dollars over the coming years to investigate how AI can be successfully transferred to the banking industry1. Despite the considerable advances already made, the fact remains that there are still considerable challenges facing the delivery of AIdriven financial products and services. This is why Mobey Forum brought representatives from the international banking community together in Toronto recently to discuss the operational, technical and ethical challenges that banks and other financial institutions should consider when building AI products and services.
however, that only 24 per cent of banks validate the data they are using2. Fortunately, there is a growing consensus that there is much more work to be done in this area. Beyond technical and operational issues, there are the privacy and data security considerations. With the General Data Protection Regulation (GDPR) legislation now enforced across Europe it has never been more important to implement best practices for data protection. Banks and other financial services providers must understand their obligations: both domestic and international. Adopting the “privacyby-design” approach championed by Ann Cavoukian (Ontario’s former Information and Privacy Commissioner) will ensure that data protection considerations are proactively built into AI systems and programmes, rather than having to be reverse-engineered at a later date.
Only as good as the data you use
From “chat” to conversation
When it comes to AI, better data means better services. Obtaining, organizing, validating and protecting the massive datasets required to underpin AI products and services is hugely complex, however. Whether you are a start-up or global financial institution, obtaining or gaining access to data sets can be challenging. For start-ups, the sheer volume of data required can be prohibitive. For banks, securing the relevant permissions can be arduous. Once the information is obtained, the next step is data labelling and categorizing: a very labour-intensive procedure. Ironically AI has not yet found a way to streamline these mundane operational processes and relieve the need for extensive human input. Perhaps most importantly, banks must also be able to validate the data they are using. With AI increasingly used for decisioning and commercial modelling, the impact of using incorrect or manipulated data could be considerable. A recent poll from Accenture suggests,
The data itself is just the starting point. If we think about the increasingly popular chatbots, customer questions don’t align neatly with pre-developed FAQs that have been used in these applications. Instead they are often much more specific, contextual and complicated. This creates the potential for incalculable variabilities. Banks cannot build products and services with an infinite amount of data points, pre-programmed scenarios and responses. Therefore, AI programmes must be able to “learn” from previous experiences and interactions. Early AI deployments however could not effectively carry these learned experiences from one set of circumstances to another, meaning the smallest circumstantial variance derailed the interactions. For this reason alone, high proportions of queries still need to be passed on to human support. One bank, for example, has revealed that 55 per cent of enquiries submitted can be answered by
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PAYMENTSBUSINESS
november/December 2018