Artificial Intelligence (AI) and Machine Learning (ML) are amongst several technologies which are enabling banks and financial institutions to deliver enhanced service experiences for their customers.

Following his feature in Information Age, we sat down with Jonathan Shawcross, Managing Director of Gobeyond Partners’ Banking practice, to explore further how bleeding-edge technologies are shifting the dial and realising major shifts in customer experience.

How are banks and financial organisations now using Artificial Intelligence and Machine Learning to deliver improved Customer Experience?

These new technologies are increasingly being deployed in direct, customer-facing environments, with automated response systems, such as chatbots, now reaching a maturity where they can be safely deployed in stringent financial services environments and more critically, able to handle increasingly complex enquiries typical to the sector.

Alongside this, algorithms are now able to process highly secure voice biometrics and process customer data in near-real-time, with highly personalised rates and pricing offered to customers in line with the organisations' risk appetite, ensuring customer retention and profitability work hand-in-hand

This has ultimately created an environment where customers are now able to interact with banks on their terms, via text or voice and using channels which fit in with their daily routines. Where other sectors have been able to rapidly embrace cutting edge technologies, AI and Machine Learning are rapidly enabling slick, simple service with the security that customers have come to expect as standard.

How is understanding and proactively addressing the needs of customers becoming the key competitive differentiator among financial institutions?

Targeted marketing directed at a customer’s key life event (such as buying a house, having a baby or retiring) is not new in financial services. What is new though, is more precisely, proactively addressing consumer needs, with solutions powered by Artificial Intelligence and Machine Learning, enabling rapid scaling while retaining a personal feel, from initial communication through to day-to-day delivery.

Currently, traditional, large banks are being attacked in very targeted areas by new challengers, who are actively seeking to differentiate themselves in a specific area, which is often harder for incumbents to respond to quickly.  This includes payment and Forex specialists competing on ease and speed, through to the complete removal of some traditional pricing and fees or through transforming the speed at which new services can be delivered, such as opening new accounts and accessing new services in minutes vs days.

As we are now all accustomed to next day delivery, instant music streaming, taxi-hailing and movies on demand, with slick interfaces and high-levels of services, customers are rightly asking  “why should my bank be any different?” With barriers for new market entrants lower than ever before, tolerance for sloppy service and confusing propositions has also drastically declined.

How can using deep data and behaviour analysis that AI & ML offers, result in higher levels of trust with customers looking for financial services?

Providing targeted offers to consumers at the right time is something which is not just becoming accepted but is becoming the norm, across the industry. 

Whether it’s a highly targeted prompt to invest money wisely or a personalised insurance renewal offer, many people now want and expect organisations to make their life easier by reminding them that they need to act. Each of these targeted approaches helps organisations gather richer data, learn from it, and consequently deepen customer relationships over time. As long as this is all conducted in an ethical manner, consumers and banks alike can realise long-term value.

Personalisation is often touted as a core brand differentiator. How is the financial sector using AI & ML to deliver high levels of personalised services?

Personalisation, as we see it, is currently limited across the sector in terms of practical, large scale application.  It is still largely used for marketing purposes, and thus few organisations are genuinely demonstrating a powerful use of AI and ML in this way.  Challengers are perhaps achieving the most in this space, often due to highly targeted propositions, unencumbered by outdated legacy technology platforms.

Analysing spending profiles is allowing some financial institutions to show customers their transaction history in different, analytical ways, which can then drive personalised offers for products. However, beyond this, much of the promised value of personalisation in the sector is yet to come.

How are banks using AI & ML to build long-term trust with customers who are perhaps still suspicious of their motives when selling their services?

The key here is the precise nature of the financial institution targeting the customer at the right time or within the scope of their purchase journey. A good example being the Halifax App which uses Augmented Reality to provide sold house prices and tailored mortgage quotes, achieved by simply pointing the phone’s camera at a property when standing in the street outside whilst house hunting. 

There are also other, less obvious ways in which financial institutions are using these technologies to build trust and generate income. A key example is fraud transaction monitoring. New technologies in this environment are transforming effectiveness and customer experience; allowing customers to continue spending uninterrupted, when more simple systems of the past may have intervened, stating ‘unusual behaviour’. 

Improved credit scoring and risk assessment systems, driven by Machine Learning, are also allowing customers a greater chance of approval for products such as loans, whereas in the past these applications may have been rejected.

In the future, how do you expect banks to use AI & ML to further enhance Customer Experience?

It feels like the truly transformative applications of these technolgies are still very much ahead of us.

We should expect to see large financial institutions, having resolved many of the obstacles with their existing systems; really beginning to deploy highly intelligent, fast learning systems to reduce friction in both sales and service experiences.  For example, Lloyds Banking Group has announced a partnership with Thought Machine, an innovative fintech, enabling greater product tailoring and mass customisation for individuals. This may signal a recognition that leaving legacy platforms behind might be the only way to truly deliver the experiences that customers desire.

While it’s easy to look towards the future and feel somewhat paralysed by these legacy platforms, we’ve found that with a focused lens, we’re still able to deploy powerful AI and ML augmented solutions in areas with identifiable customer impact while simultaneously   building a roadmap for future transformation and integration.

This approach has allowed us to deliver significant benefit though rapid deployment and iteration in parallel with wider initiatives, while enabling improved levels of self-service, increasing team capacity and  reducing overall cost-to-serve.