Getting beyond chat bots: Artificial Intelligence and Machine Learning in Financial Services

  • Sophie Winwood, Membership and Analytics Manager at Innovate Finance

  • 25.10.2017 10:30 am
  • undisclosed

Artificial Intelligence (“AI”) & Machine Learning has moved from a concept in science fiction movies to being used regularly in everyday life (e.g. Siri helping you find useful information, Netflix predicting what movies you want to watch etc.) - so why have we seen so little innovation within the Financial Services Industry (“FSI”)? 

AI is technology that allows computers to think like humans – sense, reason, act, adapt and eventually remember. Deep learning, which is based on the concept of artificial neural networks, is responsible for recent advances in computer vision, speech recognition, natural language processing, and audio recognition. While machine learning has been around since the 1940s, and neural networks since the 1980s, the catalyst for the recent explosion in the sector has been largely due to the large amount of data that is now being generated (from social media, smart devices etc.), together an increase in the breadth and sophistication of this data.

While sectors like healthcare and manufacturing have been early adopters of AI, Financial Services seem to have been late getting to the party. This is thought to be largely due to knowledge gaps between the technology start-ups selling the solutions and the financial services companies buying them:

-      The AI knowledge gap (FSI): If staff within financial services firms do not understand how an AI solution works they cannot justify it in order to convince key internal stakeholders to take on products. If advance AI machines can’t explain the decisions they make, how is someone at the innovation team at a bank supposed to?

-      The domain knowledge gap (AI start-ups): Being able to engineer data and fit algorithms is all well and good, but you need to understand the questions that need answering in the first place to produce a good product. This understanding currently lies with staff within the FSI, who have the domain expertise but lack awareness of what AI can be used for, and therefore do not consider it when looking to solve problems.

Then there is the age-old problem of FSIs procurement cycle not being set up to deal with start-ups, together with the fact that current risk appetite is low – meaning everyone is waiting for someone to try it out first, so they can then roll it out in a more secure way.

There have however been some use cases that have gained traction; roboadvisors, insurance underwriting AI and the one that seems to be in the spot light at the moment – the chat bot. While many technologists see chat bots as a “dumbing down of AI”, it is clear why they have become so popular within the banking community – they are easy to understand, quick to implement, and results can be seen immediately in the form of cost savings. They also make sense given the changing way consumers are communicating with each other and therefore want to communicate with their bank. And while some bots are “dumb” others involve the full stack of technology so they can be proactive and useful. Love them or hate them, at least they are providing publicity for AI in Financial Services and providing a success use case from which to build on.

So – what are the next steps? While using AI for interaction with customers is useful, the real power will come from using the technology behind the scenes in order to truly understand and segment the customer using the multiple data sources available. This will allow more personalised products, with a more informed and optimised pricing strategy. There will also be merit in the FSI looking to the industries that are already using AI successfully, to see if there can be any cross-pollination of ideas and innovation.

This article originally appeared at: LinkedIn

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