“We’re on a mission to transform our business into a world-class digital insurer, and to disrupt the insurance industry”
– David McMillan, CEO at esure.
esure is undergoing a transformation programme across the whole business that will result in a world-class digital ecosystem; leveraging cutting-edge processes and technology, insights, and data, alongside fantastic customer service, to deliver more personalised experiences that meet the evolving needs and expectations of their customers.
The programme includes the delivery of a new insurance technology platform that fits into its evolving architecture – cloud-enabled, open, and compatible with microservices and API stacks. Forming the backbone of esure’s technology platform are:
- Cloud-based Amazon Web Services (AWS);
- Omni-channel contact centre Amazon Connect;
- The US Digital Insurance Coretech platform from EIS; and
- The RightIndem Digital Claims Platform
At Insurtech Insights Europe, esure's Chief Strategy and Transformation Officer Roy Jubraj explained the massive amount of planning and hard work required to become the “Insurer of the Future”.
“Our technology partners have been carefully chosen and we have E&Y leading the programme to help ensure that these ambitious goals are achieved.”
Of course, it is far more than just claims but when Bill Pieroni, CEO at Accord, was asked on a recent Insurtch Insights webinar " In your view is there one area of insurance where digitisation can add most value for customers" his reply was "Well I like going where the money is .... 75% of premium dollars show up in LAE and loss.... claims is where the money is. "
Simple goals like making sure claims are triaged to the right team can ensure customer satisfaction is high. Keeping motor claims repairs in the insurer's repair network can help reduce indemnity costs. Leveraging damage and repair estimation software can help speed up claims settlement and for cosmetic damage reduce indemnity spend.
You would think that with automation insurers can mitigate the increased frequency of claims reported above. AI-powered decisioning software is evolving fast and being deployed more.
Yet adopting one point solution that is AI-powered may just push the decision further down the claims journey forcing someone else to make a decision. Too many claims management systems make it a hard job to leverage these automation tools.
Some claims platforms are designed from the start to be the integration platform to feed the FNOL data in real-time to these solutions and pass the predicted outcomes to the next stage whether simply offering a cash settlement or, for more complex issues passing to designated repair shops, loss adjusters, complex claims teams or whatever the appropriate next step is.
But, just a reminder that innovation takes longer than hoped for.
Two decades after Edison switched on the light bulb, only 3% of U.S. businesses used electricity.
AI has also taken longer to adopt and deploy across claims operations and needs time for insurers to experiment and leverage it effectively. The frequency with which AI-powered applications appear on LinkedIn, insurer annual reports and websites, and trade publications you would think it is a mature, deployed technology. Read the case studies for automated damage and repair cost evaluation from technology partners such as CCC, Mitchell, LexisNexis, Verisk, Tractable, Solera, SLVRCLD, and Sprout.ai. and you might think straight-through-processing was “de rigueur”.
AI delivers predictions from large and multiple data sets; data is the raw material for AI to access, surface, normalize, analyse, and predict outcomes. Digital maturity is nothing without the power of AI. Yet there is often a disappointment when trying to measure success and justify the expenditure. Why is that?
AI-powered applications are like unconnected digital islands in a systems ocean of data tides, waves, and shoals all flowing around the land masses of systems, unconnected technology stacks, and networks. Then, technology vendors add flexible, moving clouds as an alternative to fixed islands. A beautiful scenario when the sun is out, the seas calm, and the wind acceptable but dangerous when gales run amok, and storms batter the mainland and islands where much legacy technology is still located.
You need to be engaged and evolving with the market leaders to fully leverage these applications but you must avoid the problems of point solutions that do not connect and contribute with the wider network that is an insurer and its partners.
The November-December edition of HBR analyses the problem in "From Prediction to Transformation" - why it happens, and the solutions.
- “AI predictions often improve the quality of specific decisions but can have a disruptive effect on overall systems of decision-making. “
- Decisions are usually a combination of prediction and judgment and when extremely accurate predictions are made by AI it speeds up the actual process to impact other people- ripples spread out from island to island, cloud to cloud.
- The predictions hit reefs- This leads to a requirement for systems that balance modularity in design with co-coordination.
- AI-powered applications are evolving but are like the island and floating clouds above. They require an environment that hosts them and delivers an optimal balance of modularity and coordination.
Systems platforms that connect the many islands, enable modularity in design with coordination and communication that enables decisions to be made over a wider network.
is one of the new breeds of cloud-native, API rich and micro-services architected platforms Its strategy was, and is, to be the claim technology partner to enable modularity in design with coordination to enable decisions to be made over a wider network. Automated and human decisions in the optimal combinations required.
Take a simple example: a customer registers FNOL via her phone and answers questions posed in a comforting bot framework thus automatically explaining her TV is broken; caused by her tripping over the dog, falling into the TV with iron in hand, smashing the screen before over-turning the whole TV causing further damage.
RightIndem automatically interrogates the policy admin and/or claims center core system in the background to validate coverage.
She added photos as automatically requested, five of them including the damage, serial number, make and model, and, very helpful, a video with an audio explanation showing the context of the whole event.
The automated RightIndem claims process confirms the claim is covered, analyses the metadata in photos and video, and “green” flags that the photos and video were taken in that location, at the time described and the images were not doctored. The claims software decides that the customer can be offered the replacement value all in an automated process (could all be rules-based or AI-powered).
But- here’s the thing. Automation has speeded up judgment and action, but will it hit an analog reef? Without the planning and strategy to avoid it, the decision is moved to someone who must now make a judgment. What is the current like-for-like replacement, where can it be procured, what is the price, and what are the deductibles? And say the customer says “cash will do fine thank you” we must now pass an instruction to the carrier’s payments system. If unlucky, the customer must wait for a cheque; lucky might be waiting days for an electronic transfer to her account.
OR- RightIndem enables the carrier to extend straight-through processing (STP) with point solutions like SLVRCLD, Be Valued, or Value-Checker to find the like-for-like replacement, cost, and availability. Then RightIndem applies any deductibles and offers in the same chat-bot style the choice of cash, a virtual credit card, or an Amazon voucher to redeem online. She presses the button of her choice, and the claim is settled.
RightIndem- a platform that connects all the islands, for which read modules or applications, passing data and decisions from one to the next and can even then instruct an automated payments system like Imburse or Stripe to pay by cash transfer, virtual credit card, or Amazon voucher then and there. The RightIndem strategy
That was a very simple example to be fair, but many claims are simple. Take a recent deployment of RightIndem for home and contents claims: -
Following a 10-week analysis of 760 home claims processed on the platform 87% of claims submitted were for single items only and therefore could have been processed via STP
- 33% of customers submitted claims outside of office hours and this is increasing.
- 80% of claims received had photos or videos attached.
- 35% of photos/videos received had referral indicators (metadata flags).
- 83% of customers who started the journey completed the digital journey.
- 66% faster settlement by handlers as they had more valid information allowing validation.
- 4 minutes was the fastest submission time – the average was 14 minutes.
- 91% of customers completed the CSAT.
- 82% of customers recommend the RightIndem platform.
- 86% said it was easy to use.
Hold on though- many property claims are more difficult than that. Internal damage from escape-of-water (EOW), multiple rooms damaged by flood and fire requiring emergency accommodation for clients and disaster/restoration attention. Some have vehicles involved and personal injury.
An eFNOL platform like RightIndem can apply triage logic leveraging custom question sets to drive the loss into the right parts of the organisation and its supply chain: e.g.
- Desktop Survey
- Liability@ EOW a water company responsibility or the insurers?
- Loss Adjuster review
- Builder/engineers, surveyor’s inspections
- Disaster Restoration/CAT team
Looking at motor insurance, take the case of a road traffic accident involving the claimant’s vehicle and third parties.
But this involves not just the carrier and the customer, but also the repair network, engineers, investigators interviewing third parties, and judging liability, possibly total loss. The number of hand-offs between systems and people increases geometrically and even a small 2% error rate at each hand-off would lead to significant data corruption and customer dissatisfaction. .
The introduction of AI into your company’s decision-making doesn’t affect just you. It also affects your partners in the value chain and the ecosystem you operate in.
Or as HBR describes: -
What this shows is that while AI can be used to resolve one person’s uncertainty, that effect doesn’t spread to decisions throughout a system. The fundamental problem—that demand needs to be aligned with supply—hasn’t really been solved. Like a stone thrown into a pond, your own AI solution has ripple effects on other decisions in the system.
That leaves us with something of a paradox. The value of AI comes from improving decisions by predicting what will happen with factors that might otherwise be uncertain. But a consequence is that your own decisions become less reliable for others. Introducing AI into the value chain means that your partners in it will have to coordinate a lot more to absorb that uncertainty.
To adopt AI “point solutions” means you must plan and coordinate systems to align effort and resources.
Furthermore, you need a platform that empowers you to combine coordination with modularity. Passes the decision from one AI application like Tractable to another like BAIL. That connects an investigations partner combining technology and human intuition into the whole subrogation process.
That is at the heart if the RightIndem strategy, technology and people that make up the company. We have found that working closely and in collaboration with new clients we can deliver a clams technology “stairway to heaven” as Geoffrey Moore of “Crossing the Chasm” fame describes.
This is a staircase of steps from the initial adoption of an AI package to help one part of the claims operation to the next step that, say, connects the automated estimation of damage and repairs/replacement/restoration to the skilled tradespeople, contractors, specialists, and engineers that constantly amend and update costs and times as supply chain disruption impacts the whole process.
The claims platform must enable coordination and communication and combine automation, human intuition, and knowledge.
It will not be simple; it will not be easy. But it will be feasible when a true partnership of joint commitment, preparation, and planning leads to the viable “stairway to heaven”.
To begin that journey, we would need to build clear representations of your current state and your desired future state. Capturing your current state involves an act of description.
Capturing your desired future state requires an act of design. If you do not have a clear design for your future state, you have no north star by which to navigate your digital transformation, and it cannot possibly succeed.
So, let us assume we have a clear design for our desired future state. It won’t take you long to realize there is little chance that a single intervention can get you from here to there. So, the next major deliverable must be a roadmap organized around a maturity model. Each step up the stairway should be designed to deliver value upon completion, thereby allowing the organization to pace its change management activity. Funding things as it goes, building its confidence and reassuring its various stakeholders.
With such a roadmap in place, now you have a current-state/future-state accountability mechanism that can govern each stage of the transformation—the software and systems, the systems integrators, the process owners inside your enterprise, and the people responsible for executing the processes.
A path from prediction to transformation.
Want to discuss more? Drop me a line at firstname.lastname@example.org ( yes I declare my role at RightIndem)
Esure has an extremely ambitious plan to fix insurance for good. To do so, we must tackle the challenges that consumers face head on and ensure that we build the best environment for both our customers and people to thrive. Now is not the time to play around the edges. We had to engage in a dramatic transformation that would replace the beating heart of our business.