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Artificial intelligence (AI) is a buzzword across nearly all industries. Robots taking over has long been stuff of science fiction nightmares and utopian fantasies. So naturally enough, there has been lots of talk about how it could disrupt businesses and transform the world as we know it.

Ironically, there is actually a very human side to the potential of AI, especially in financial services - the role it can play in radically improving customer interactions and experiences. But what does this actually look like when it’s put into action?

Over the last couple of years, building societies and Festina Finance have been working closely together and using AI to create an end-to-end digital customer journey throughout the mortgage application process.

Our approach to AI has been to see it as a complementary tool when designing a business process and customer journey.

Building societies are spending a great deal of time and effort giving sound and proper advice to their customers. The advisor will collect information about the customer's financial situation and ask relevant questions on their views and understanding of mortgages and the overall economic outlook.

It is a time-consuming process. But the prize is that all this information makes a strong foundation for a specific assessment - the proper mortgage solution for the customer.

Our starting question was to see whether it was possible to use AI to transform some of this time into improving the customer experience and still remain compliant.

In the field of AI, the common challenge is that you need large amounts of data to train the AI models. That's why the big technology companies such as Google and Amazon have such a massive advantage in the field of Big Data. For example, Google alone is processing 3.5 billion searches every day – that works out at about 40,000 searches per second.

Festina Finance is not (yet!) the size of Google, so our approach has been to adopt the existing specialist knowledge from building society advisors, instead of learning customer behaviour from scratch.

For the technically inclined, we do this mainly using a Bayesian network. This means we work with specialist advisors to train the AI engine, but we do not need a large set of data for the training. Instead, the AI engine applies its “intelligence” to each answer to build a model of the customer's knowledge and preferences via adaptive questioning. This also means that our AI engine can explain the advice it has provided.

As recent news articles have, Hinckley & Rugby Building Society has implemented our system with AI supporting mortgage advisors in the suitability assessment. Hinckley & Rugby is also looking at using the AI model for self-service suitability as well, which would support customers to choose the best-fit mortgage for themselves.

With our system, both the customer and the building society advisor gets a specialist AI-helper with integrated process support. This allows the customer to do more themselves, sitting at leisure with their computer while getting personalised financial advice. It also helps human advisors to be efficient and compliant.

In the near future we see a big potential in digitalisation and using intelligent systems to evolve the mortgage market. Our clients and we believe that the mortgage market will benefit significantly from using AI and robo-advice