In this article by Richard Jones, VP of Sales, Northern Europe, Confluent, we explore the attitudes and challenges of data in finance and the next stages in the technology and personalisation evolution.
Building up a total understanding of someone’s financial situation is no mean feat. Underwriters need to access a huge amount of mission-critical data in near real-time to deliver the products and services that customers need, while accurately assessing levels of risk.
But with an applicant’s patience understandably limited, insurers are increasingly caught between consumer expectations and the realities of the system they are working with. With the data submitted often incomplete, and a human link in the chain at almost every significant step, the journey from application to resolution is dependent on manual intervention — and inevitably slow.
As a result, consumer satisfaction is falling. US analytics company, JD Power, suggests that insurers are only meeting customer expectations 34% of the time. The average consumer has learned that other industries can automate and accelerate processes using the right technologies — even if they don’t necessarily understand what AI is – and now expect a modern, forward-thinking underwriter to do the same.
Retail is a good example of an industry that has shown consumers what AI is capable of. Research from omnichannel marketers Emarsys found that more than two-thirds (70%) of UK shoppers want personalised recommendations – even though less than half (41%) specifically believe that AI was positively impacting their experiences. In other words, they might not understand what AI can deliver, but they definitely want those associated benefits!
This puts pressure on insurers to explore a ‘data transformation’, not just a digital one. If their current systems are holding back customer sentiment, it’ll have to be addressed sooner or later.
But that reality comes with a caveat; you can’t automate everything across the board.
Customers might not want humans at the fore of every decision, but they do expect to be able to rely on a human counterpart when required — from customer service to consultancy. McKinsey cites ‘employee knowledge and professionalism’, and ‘employee courtesy’ amongst the key qualities driving customer satisfaction.
In essence, underwriters need to harness automation and artificial intelligence where possible, without compromising that human touch. So how do they find that balance?
Differentiation through data
Well, underwriting is about quantifying risk first and foremost. The more relevant and high-value the data available, the better understood the risks for a specific customer can be.
This makes data capture and accessibility fundamental. Research from PwC suggests that being able to ‘better leverage business data’ is a driver for technological transformation in 97% of London Market insurers.
The gap between data entering the system and it being made available for analysis as part of an application should be as small as possible – and ideally in real-time. That process must also be able to accommodate not just one underwriter, but the plethora of teams and systems across an insurer’s infrastructure.
Ultimately, insurers are only as accurate, fast, and informed as the data they have available.
Risk and reward
On the other hand, many insurers are – quite rightly – risk averse. Data transformation demands that they move away from systems that aren’t fit for purpose in the modern day, but extracting those systems from your business is tricky.
Many, as a result, hold on to legacy tech in lieu of an upgrade. They see comprehensive transformation as a long, expensive, complicated process. And what happens if their long-term objectives change?
For starters, new systems are a huge logistical challenge. They need to integrate with other systems across the business _ often requiring a long and thorough audit process. And employees in multiple departments trained on the new technology.
Part of the problem in the data transformation of underwriting is reaching the tipping point at which you commit to a realistic level of evolution. Stasis will ultimately kill a business – so at some point, the plaster must be ripped off.
Going hyper with the personal touch
The transformation into a data-driven business doesn’t just make the process faster. It also allows us to return to that balance of personability and automation, as the data being captured can act as the basis of automated personalisation.
Data capture isn’t just financial. Every data point, from a preferred name to a customer service history, can potentially inform us how a customer would like to be treated. That information should be fed into a customer profile that acts as a 360-degree view of that individual; it’s a single source of truth for the entire business and the convergence point of all that real-time data that’s so valuable to understanding what a customer really wants from you.
That profile can then shape a customer’s entire journey. Whether it’s retaining key data from a previous application, contextualising a new complaint, or engaging on a customer’s favourite channel, this sort of ‘hyperpersonalisation’ can make a customer feel recognised and valued by an insurance provider.
Compared to the approach of previous decades, in which customers could be bumped from person to person without any context, this is transformative.
Looking forward
When we look at transformation, insurers are walking a tricky tightrope. The financial services industry at large, particularly traditional banks, is well-known to have a reliance upon legacy technology. As new technologies emerge, they’ve held on tight to systems — customising, integrating, and patching to keep things moving.
But it’s prohibited financial services firms from moving at the speed of modern challengers — and providing that personal touch that delivers an exceptional experience without slowing things down for the customer. Competitors aren’t only beating them to the punch — they’re more personable at each stage of the process, too.
Despite the pains of digital transformation, this is a reality that underwriters must contend with sooner or later. The gradual development of a technology stack that can automate the menial, reflect the individuality of customers and improve the delivery of the product should be an absolute priority for underwriters across the globe.