insurance EasyEquities Sanlam Indie
Feature Stories Insurtech Trending World-Region-Country

The Future of Insurtech; With Koffie, STX Next, Roots Automation, mea, LifeSearch and Home365

For 50-odd years, the way insurance has worked has remained the same. But in the last few years, catalysed by the pandemic, the rise of digital solutions and insurtech looks to break down historical insurance preconceptions have emerged.

Innovative solutions continue to change how insurance companies interact with customers, how they underwrite and price policies, and how they manage risk. But what does the future hold for insurance technology?

We hear from a number of industry experts, who answer that very question and offer their perspectives on the topic.

The future of insurance technology: AI and machine learning?
Ian White
Ian White, co-founder and CEO at Koffie Financial

Ian White, co-founder and CEO at US-based transportation insurance provider Koffie Financial, explained which technology insurance could look to next when innovating the sector.

“The insurance industry will likely continue to embrace AI and machine learning while expanding the use of telematics and blockchain.

“Through this, we’ll be able to witness continued growth in the insurtech sector, and see insurance products develop as they address emerging risks and technological innovations.”

Krzysztof Sopyła, head of machine learning and data engineering at STX Next, mirrors White’s suggestion that AI and machine learning will become an important part of insurance technology.

Krzysztof Sopyła
Krzysztof Sopyła, head of machine learning and data engineering at STX Next

“The future of insurance technology looks promising, with the continued growth of insurtech startups and increased investment from traditional insurers. The incorporation of AI and ML will be more prevalent, becoming a fundamental aspect of insurance services with self-service portals, virtual-assistant and many more.

“Furthermore, the growth of IoT and other technologies will provide insurers with more data and insights, which they can use to offer more personalised and targeted products and services.

“We can also expect to see more collaboration between insurers and technology companies, as they seek to leverage each other’s strengths to improve the customer experience and drive innovation in the industry.”

Large Language Models
Chaz Perera
Chaz Perera, co-founder and CEO at Roots Automation

Chaz Perera, co-founder and CEO at Roots Automation, discusses how Large Language Models (LLMs) could shape the future of insurtech:

“Looking forward, we see significant advancements in LLMs, similar to Chat GPT, to improve how information is surfaced in deep datasets or complex documents (for example, terms within demand packages).

“At Roots, we’re at the confluence of transacting business which is not on the public internet but is behind private doors. There is consistency across the business that we transact, be it within the claims or underwriting space in particular.

“Having the ability to leverage these models and fine-tune them on the claims and underwriting language space will enable unique versions of these large language models to be more accurate, more predictive, more capable in the insurance space than you would get if you were to try them out on the Chat GPT.

“LLMs cannot learn nuances of a particular industry with data that isn’t available. For example, insurance forms. They cannot be available publicly on the internet because they contain private information. Now, if we had access to tens of millions of forms in the insurance space, that puts us in a unique position to train these models on data that no one has seen before.

“Another next step is to explore computer vision plus language models, also known as multimodal models. For example, we rely on computer vision plus natural language together to not only see but truly understand what is happening on a computer screen or within a document. Therefore, the next area of research is how we can take these LLMs and further train them with a combination of images and words for use in the more complex, cognitive areas of claims management and underwriting.”

The future is in automation
Martin Henley
Martin Henley, CEO at mea

Martin Henley, CEO at AI insurance processing platform mea, also gave his view on the future of insurtech: “I’m optimistic about 2023 for the insurance and insurtech sector. Growth and productivity in the market have indeed been stagnant, but this is the right climate for focusing on initiatives that truly help the combined ratio rather than just drive cost.

“Until now, insurtech hasn’t worked as it intended, and scepticism around it is rising. This is because Insurtech has tended to focus on very specific, niche problems without addressing the real underlying issue: manual and time-consuming processes.

“The future of insurtech is finding a way to automate these manual processes, which will transform the productivity of insurance firms. Underwriters currently spend a lot of time chasing down documents and data but imagine if they – and everyone working across insurance – had all the data they needed exactly when they needed it. And innovative technologies, such as cloud, which are playing a greater role, are driving a new mindset in the industry.

“The ability to ‘consume’ services as needed allows the industry to focus on what it is good at, such as underwriting risks and managing claims, as opposed to navigating these complex systems and processes – which they don’t have the time or resources to do effectively.”

‘Creating value for both insurer and consumer’
Sam Stafford
Sam Stafford, chief data officer at LifeSearch

Sam Stafford, chief data officer at UK-based insurance broker LifeSearch, commented on how data is central to the future of insurtech:

“There is so much potential for AI and machine learning to transform the insurance sector, in particular the data value exchange between the insurer and consumer, contact strategy and speech analytics.

“In the protection insurance space consumers are generating more data than ever before through a plethora of health apps, fitness clubs, food tracking etc. The data could be used to provide better predictions of health-related claims and potentially drive down premiums, thus creating value for both insurer and consumer.

“From a technology perspective the challenges, which include connecting the data sources and then applying the algorithms, are not insurmountable, but the ethical and regulatory challenges need to be addressed. Many consumers remain sceptical of the benefits and often lack trust in big organisations. As the use of telematics data in motor insurance becomes more popular, it may not be as big a leap of faith to start sharing some health data too.

“Contact strategy and speech analytics are related in an advised insurance business because much of the contact strategy is anchored around connecting consumers with advisers. Technology, AI and machine learning play a crucial role in this space because they can be used to determine the optimal channels, timing and frequency of contact, and then be used to analyse the actual conversation itself.”

‘Blending together service and insurance models’
Chad Gallagher
Chad Gallagher, co-founder and head of growth and investments of Home365

Chad Gallagher, co-founder and head of growth and investments at property management company Home365. Gallagher explains how AI and machine learning can specifically change the real estate industry.

“Machine learning and smarter tech platforms allow for the first time to combine the service and insurance models into one where the property can be under-written in real-time and the tech company takes on much larger financial responsibility for the asset. Ultimately, a real estate investor is looking to mitigate risk well beyond just extreme fire, flood or weather damage to the property.

“Now we are seeing products where the real estate investor can also include rent payment risk, day-to-day repairs of the property. leasing and management fees on top of the traditional property insurance product. AI and ML are essentially underwriting a property when combined with day-to-day service levels. This means much lower variance for an investor in terms of end results of the investment.

“The investment for both small retail investors and larger institutions starts to look a lot more like a bond than a traditional high-risk real estate investment. Ultimately, this will lead to real estate investment properties trading at higher values in the future due to the more predictable returns of the asset. I think we are just in the early days of service and insurance models blending together to create a much stronger end product across multiple industries including real estate.”

Author

  • Tom joined The Fintech Times in 2022 as part of the operations team; later joining the editorial team as a journalist.

Related posts

Moorwand to Issue First Prepaid UnionPay Branded Cards to Customers in UK

Jason Williams

Mambu Finds the Retail, Health and Education Sectors Are the Most Likely to Uptake Embedded Finance

Francis Bignell

SME Leaders Confused About Governments “Levelling Up” Plans

Polly Jean Harrison