At the Royal Exchange in the City of London merchants and businesses met to exchange news and information. On nearby Lombard Street Lloyds Coffee House was a centre for 17th Century global maritime trade and insurance.  Then as now, those ecosystem members with the best, relevant data sharing models were the leaders, and a simple tale of motor insurance is a perfect example of illustrating how shared data is an enabler and unshared data a barrier to digital transformation.

I met a digital claims transformation CEO for lunch at the Royal Exchange following my recent article “Auto insurance, digital maturity and how to transform it.”

 Source:  Altus DigitalBar 

There I analyse how motor insurers have transformed claims beyond the FNOL stage and gathering evidence utilising guided self-serve photos, video, and voice/text descriptions. But how often does real-life experience mean the digital claim process hits the analogue buffers early on in the journey?

My guest recently registered a claim with one of the digitally mature carriers in the Altus DigitalBar. His Tesla electric vehicle (AV) bodywork was damaged in an accident. He found it simple and convenient to action FNOL and provide text and photo images of the damage from his smartphone.  So far, so good.

Even so, “pre-fill” was lacking as he had to manually type in Policy Number, Registration, Name, address, phone number, email address etc. First question- wasn’t that data available as a prefill?

The claim process advanced and an SMS and email message direct to the phone told him he would be contacted by a credit-hire company and body shop. No opportunity to book appointments from his phone. Maybe this was a result of there being very few Tesla repair shops in the UK so booking is best achieved manually but still a disappointment.  

The credit-hire company rang saying they had reserved a vehicle near to him for collection. Except they were using a previous address they had for him on their database from years before. Too far away. The insurer had not provided the current address to the credit-hire partner.

The body shop sent someone to pick up the drivable EV who was, to be kind, scruffy. The only human contacts the policy holder experienced were in the hands of a credit-hire agent who had the wrong address and a body-shop driver who seemed to come from a back-street traditional internal combustion engine vehicle repairer.

Modern, leading brand insurer and EV manufacturer whose reputations are in the hands of supply chain partners who were not connected digitally.

This demonstrates the challenge for carriers and their auto OEM customers to achieve the fabled “end-to-end” claim management.

Another warning note for carriers is my feeling that Tesla is learning the insurance buy & quote and claims business from incumbent carriers in the US, Europe and APAC before taking a frontline role itself.  Carriers had better ensure they deliver the optimal digital experience for policy holders and auto OEMS.

The changing driving behaviour of policy holders during and after the pandemic with hybrid office and work-from home models will hasten the adoption of user-based insurance products (UBI). The success of Cazoo, Tesla and other online auto sellers is a stimulus to offer embedded insurance and this adds more competition to carriers versed in annual fixed price policies.

Great article here  "Why we’re excited about embedded insurance in 2021" by Jessica Bartos giving a VC angle on this high growth sector.  

Carriers can and will respond with new products and services developed inhouse or white labelled. Yet, in the back of my mind, the message from McKinsey that only 20% of insurers make an economic return on capital suggests to me that only 20% of carriers may succeed in this new world. There is a whole new raft of full-stack insurtechs that are now unicorns and immersed in effective data management, fully resourced with data scientists and the agile, no/low-code technology to deliver Amazonesque quality UX and then some.

Technology partners like Wakam focussed on helping embed insurance. 

It still comes back to data again and again. Data, data scientists and claims management platforms that can access and leverage all the data for an optimal customer experience and journey. Back in 2020 Martin Ellingsworth, senior analyst with Celent published a prescient article “Data science is like Gelato”. Then coincidentally I joined a Clubhouse session hosted last night by Thomas Verduzco-Weisel from Core Logic with Marty in full gelato mode. 

He examined the difference between actuarial data science and digital transformation data science.

“The actuarial profession was suffering from a skills gap. Predictive analytics skills were becoming necessary, and a new type of worker, a data scientist, was entering the gap to become valuable to the business of insurance. Many executives in those days proclaimed data scientists as the future of insurance, and these new outsiders were taking leadership roles, sometimes with actuaries reporting to them.

It was dually bewildering and depressing - and cause for career concern.

The subject of our conversations revolves around what is different about the practice of analytics between and among actuaries and data scientists as applied to insurance and risk.

They framed the discussion like this -

“If you cannot describe the difference between a taste of ice cream and a taste of gelato, it is obvious that you have never experienced tasting gelato.”

Ellingsworth continued: -

“Actuarial methods traditionally have had a narrow application to the confidence in a financial forecast that serves to maintain the solvency of the company in meeting its future obligations. This is served inside a restraint-laced regulatory framework with oftentimes slow and deliberate change cadence. Data science is not that.

Our conversations have evolved in recent years as the skills gap chasm grows even wider.

Data, AI, and cloud have grown more important with the emergence of new data, new analytics, and new computation capabilities vastly outside of the traditional actuarial remit and even beyond the IT department capabilities of many of the largest and most sophisticated companies.

The list of impactful technologies is extensive and growing. Here is an active list: computer vision, natural language processing, speech analytics, text analytics, geospatial frameworks, machine learning, telematics, end-to-end experiences, IoT, digital distribution, AI, household and living situation models, customer behavior scoring, knowledge graphs, social network information, etc.

Productizing and monetizing insights into actions across the enterprise are the dividing lines between data science and traditional IT and actuarial organizations.

Organizing for success is taking a wandering path. Companies are learning how to learn, how to ask better questions, and how to source the mix of build and buy levers to use in different AI and data science strategies and deployments. Everyone is finding new appetite for more data, more compute, more impact, and more recursive segmentation and feedback loops for more accuracy with faster cycle times and smaller expenses.

Inevitably we resolve that these career paths are separable with distinct changes in how and where knowledge, skills, and abilities are applied. Actuarial - slower, regulated, embedded. Data science - faster, nimble, scalable.”

Let’s get back to carriers and delivering end-to-end digital claims journeys. Even the most digitally mature carriers are struggling to deliver a complete journey with the optimal mix of digital and personal engagement. As experienced by my lunchtime guest. The proof is always in the pudding which is why I warmed to Marty Ellingsworth’s ice-cream and gelato narrative.

What are technology vendors doing to help carriers?

Frederik Bisbjerg had a rant the other day on LinkedIn that vendors are more interested in their solutions than the carrier’s issues. More interested in their numbers than the carrier’s KPIs. Just not putting the effort into understanding the carrier’s current core systems, recent point solution implementations in counter-fraud, predictive analytics, or touchless claims. Not immersed in the day-to-day operational issues that take up 80% to 90% of a VP of Claims time. Or the challenge faced by the Claims Leader of going back to the CEO and CFO for yet more funding when they spent £/$/€ tens of millions on insurance platforms just two/three years ago.

Mind you, carriers can be at fault as well. 

Having no clear vision, goals, or strategy to face the future but expecting the technology partner to deliver answers that anticipate disruptive competition.

To help a carrier a technology partner needs a focussed and well organised approach. With discovery sessions that absorb current research, initiate new research, and combine the outputs of a vast library of insights.

Avoiding the temptation to bring too many people, most of whom say little but put-up barriers to change. Better to bring a focussed, well briefed, and capable team able to see the big end-to-end picture as well as tackle the detail of priorities and see the wood for the trees.

All able to tell the difference between ice cream and gelato. All with a sound ability to manage and leverage data from a bewildering variety of current and evolving sources both internal and external. These are discussed in a Willis Towers Watson helpful article “Data sharing models in the insurance industry” from which I take this diagram.

LexisNexis, CoreLogic, Verisk, Mitchell International and many more enterprises are building out these data resources that are vital to achieve competitive advantage. The key, of course, is joining up these multiple, disparate and insight yielding data. Easy to say but challenging to achieve. None of the glamour of snazzy front-end apps and all the devil in the detail. But the absolute fighting ground for market leadership.

“This is where Tesla currently leads: more than a million of its vehicles are equipped with its AutoPilot system that is always on, in “shadow mode”, ready to upload snapshots to Tesla servers whenever the human driver makes decisions different from its own. Tesla is, in effect, outsourcing its real world testing to owners who pay for the privilege. It is a stunningly more efficient model than most Level 4 groups, which bankroll hundreds of engineers to sit behind the wheel of robotic test fleets.

Source : FT  “Robotaxis: have Google and Amazon backed the wrong technology”.  

Data available for underwriting, product development (insurance and vehicle) , claims management, repair network improvement.

The FT points out that auto OEMs and their ADAS suppliers like Bosch and Mobileye are adeptly implementing ADAS level two and towards three and four thus generating unheard of volumes of data. Meanwhile the autonomous vehicle (AV) driving technologies are racking up a miserably small number of miles and data in comparison.

By the time AVs eventually become viable the ADAS to level three and upwards might have delivered virtual EVs and data to have become the dominant vehicle technology stack leaders.

“If robotaxis fail to scale up in the coming years, analysts say the ADAS advantage will become clearer. Today, the biggest difference between the two approaches is the tech itself: ADAS is low cost and limited; Level 4 is high cost and sophisticated. But in a few years, the biggest difference is likely to be cash flow: ADAS players will be raking it in; Level 4 groups will be burning it at even greater rates.”  Source:  FT 

 That offers hope for auto insurers worried that AVs move insurance from driver risk management to product liability insurance. 

Mind you, that puts the auto OEMs in the ecosystem driving seat either way so carriers need to factor in how they can retain an ecosystem leadership position.

Choosing the right technology partners is an essential aspect in all this. Platform partners that empower the carrier to swap in and out point solutions as the first choice may not turn out to be the viable long-term partner. Technology partners that have the integration and connectivity capabilities and resources to enable that mix and match approach. That allow carriers to leverage data, data scientists and AI.  Brings the ice cream and gelato metaphor back in mind.

In my article Auto insurance, digital maturity and how to transform it ,  I pictured representative examples of the core technology partner choices ( I will have missed some out and everyday we will, see new additions). Your core platform partners must offer you the resilience, flexibility and agility plus the ability to contribute to the necessary vision, strategy and resourcing to innovate in a rapidly changing world.

Above all they must facilitate the sharing of data between all the parties you engage with.

Insurance Data Types

Insurance policies require a diversity of data types, which vary between policies dependent on the particular insurance line, e.g., retail automotive insurance or property and casualty, and the purpose of the communication such as to share customer data, fraud prevention, or broker a deal between a customer and an insurance provider. However, some broad categories can be defined for all insurance markets:

  • Customer information
  • Asset being insured and related exposure
  • Historical loss data
  • Behavioural data from telematics, IoT in buildings and fitness trackers
  • Business process definition and associated data, e.g., policy management and claims processing
  • Calculated risk of the insurance (i.e., estimated losses)
  • Related, non-insurance data, e.g., natural hazard, mapping, GPS data and weather information

Source: Data sharing models in the insurance industry Willis Towers Watson

Whichever partners you choose check out that that can support this data sharing model which means much more than just having an API. It needs integration resources and inevitably some dev work. 

Major core platforms Guidewire, Duck Creek, Majesco, ICE, Pega, Innovation Group and so on. Good on breadth of functionality but sometimes lacking in specifics e.g. claim management. And traditionally involving Capex, considerable SI costs and high annual licensing costs though that is changing slowly.

Quote & Buy, MTA & Renewal Platforms like Go-Insur, HUGHUB and iptiQ that enables an insurer to allow a customer to interactively manage all their policies from one dashboard

Digital Claims Platforms RightIndem, Synergy Cloud, Snapsheet, ClaimsGenius, Salesforce Industries, Five Sigma, 360Globalnet, etc

Auto Ecosystems Verisk, Core Logic, LexisNexis, Mitchell International, CCC etc

Point Solutions Weathernet, Tractable, Audatex/Solera, Shift, Friss,, and many others

Combined claims services and technology providers Crawford & Company, Sedgwick, Davies Group, Claims Consortium Group. Control€xpert etc.

No-Code/Low-Code app building platforms i.e. build yourself but with built in accelerators from Unqork, Netcall, Ushur …

Embedded insurance Wakam, Wrisk, Qover, etc

If you would like to discuss this more please contact my via LinkedIn or via my personal email