What is Insurtech?

What is insurtech?

Insurtech is a broad term to describe any of a new wave of technologies and companies that are actively trying to digitise insurance. Insurtech can come in many forms, but it is united by the focus on bringing digital benefits – such as automation of processes, online customer experiences, and machine learning – into the insurance space.

Examples of insurtech companies in the UK
Zego – https://www.zego.com/

Percayso – https://www.percayso-inform.com/

Wrapper – https://www.wrapperinsure.co.uk/

For a general overview of some of the most exciting UK insurtech’s visit: https://insurtechuk.org/

Why invest in insurtech?

Insurance is an area that has historically been slow to adopt technology. Industries like social media, travel, and fashion have stormed ahead, where financial services and insurance have lagged behind. Insurance is nonetheless a massive global industry with in excess of $5 trillion a year spent within it per year. The last 2-3 years have seen a considerable upturn in investment in this sector. Success stories of companies such as Lemonade and Hippo in the US and Zego in the UK have shown investors that there are sizeable returns to be made in Insurtech.

Willis Towers Watson 2020 Insurtech report

What are the benefits?

All participants in the insurance lifecycle benefit from quality Insurtech.

Insurers stand to benefit through better insights into their products through data access, enrichment and analytics.

Brokers can renew their focus on customer service when decent insurtech automates many of their manual processes.

The end customer is more likely to deal with and stay with insurtech-led brokers because their online experience is more pleasant, simpler and integrated.

Insurtech and AI

AI is a buzzword in many industries, and in insurance we can make use of AI in a number of ways. In truth the industry has some way to go before AI (or more likely Machine Learning – ML) is a backbone of all parts of the process. ML is already used by actuaries, but only used to a limited degree so far in customer-centric interactions such as next-best-step decision making and UX enhancements.

Insurtech is blazing a trail that is disrupting the old guard of insurance and the plethora of recent successes and scope for improvement in the market in general can only mean more interest, investment and innovation in this space over the coming years.

Microservices

The Ignite Broker platform provides a lot of functionality. That’s great, but it’s not always all relevant to our clients. The modern insurance broker needs to acquire best-of-breed software and that means picking and choosing functionality from different providers.

To allow brokers to do this Ignite Broker is built in a modular fashion. Brokers can choose which modules they need and substitute out or enhance ones that they can get elsewhere.

For example a broker might already have a great customer journey and website. No problem: Ignite has an API for that and the broker can use the Ignite policy administration and rating functions without changing their website.

Another example: a client might already have a rating engine (internal or external) that holds their pricing algorithms. Ignite’s policy administration system can simply call to this new rating engine as well as the Ignite proprietary one.

Most recently Ignite has been chosen to provide the Ignite Accounting module for a major broker platform supporting over £180m of premium. APIs between Ignite and the client mean both systems can run and be deployed independently, with loose coupling, and remain highly maintainable and resilient.

How is this done? Rather than having a large system that relies on all its parts to function, Ignite Broker is a series of smaller services that talk to each other, also known as “microservices”. Think of it like a honeycomb, where each of the cells are built on one another; independent but also integrated. For each cell we use the best available tech and, when something better comes along we crack the wax seal (as it were) and substitute in something better.

Some of our microservices include: rating engine, customer portal, commissions, complaints, and more.

If you’d like to read more about microservices go here

If you’d like a demo of the Ignite honeycomb (sort of), go here

The journey to Artificial Intelligence in Insurance

There have been a bunch of crazy developments in the field of Artificial Intelligence (AI) in the past few years. Google’s AlphaZero beat the leading Go player in 2016 using techniques called Neural Networks and who knows what they’ve achieved in the 5 years since. If you’re interested they publish most of it here: https://deepmind.com/

This blog isn’t really about AI though, it’s about the journey that companies need to go on in order to achieve AI-readiness.

What is AI?

Artificial intelligence (AI) is intelligence demonstrated by computer programmes that mimics human-style intelligence such as learning and problem-solving, as opposed to more traditional ‘computer’ skills of calculation or processing.

Why is AI relevant for insurance brokers?

AI has wide relevance for almost all spheres of industry, but insurance particularly because of the vast amount of data and variables available to analyse. Healthcare has seen a huge surge in AI investment in recent years and it is inevitable that some of this will trickle down to insurance products, journeys and pricing too.

What are the stages to get there?

Most businesses are a long, long way off achieving AI. But there are steps along the journey that they can be making.

Data Access

AI feeds off data. Access to data is therefore mission critical the beginnings of any AI venture. Companies should ensure that their data is readily accessible, structured, and as full as possible to make the most of it, now or in future.

Data Analytics

Humans are good at problem-solving. Better than most machines. Once data has been assembled there are good tools (like PowerBI or QlickView) for visualising it. Businesses should layer such tools onto their data warehouses/lakes and make the effort to analyse the results of just looking at trends and correlations.

Machine Learning

Machine Learning (ML) is the step between looking at an Excel spreadsheet and AI. ML is not ‘intelligent’ in that it needs to be told ‘what’ to do, but it can do what it is told to do better than a human can. ML, properly trained, will notice correlations within data that the human eye cannot spot: it will see multi-factor or multi-dimensional correlations for which there are no easy visualisation tools. Microsoft Machine Learning provides an excellent toolkit for such projects. Building supervised learning algorithms will likely require an experienced or trained data analyst or programmer.

Artificial Intelligence

Once these initial steps have been taken, the door to real AI is open.

Very few insurance companies truly stand at this hallowed portal as yet, but for those that aspire to do so, the previous steps must all be taken. And there is great benefit to be had for the business and its’ customers in doing so.