Two become one: The convergence of insurtech and the industry Part 1

In the first part of a two-part examination into the cross-over between traditional insurance and tech innovation, Jonathan Spry, CEO and co-founder of Envelop Risk explores the data-driven future for the industry, the advantages of artificial intelligence (AI) and the cyber ‘shop window’.

Insurtech and The Industry Part 1

Like many in the insurtech community, I am convinced that AI is the future of the insurance industry. Alongside traditional approaches, the application of AI could increase profitability (as underwriting and operational functions are partially automated) and provide insurance enterprises with a new toolkit which combines the best of human and machine expertise when making risk and pricing decisions.

The advantages of AI

AI and, in particular machine-learning, can be used to analyse large complex data sets; revealing predictive factors which are not easily uncovered by human analysis and conventional computation alone.

If used correctly, AI can overcome the human biases that impede rational decision-making and provide huge benefits over the purely human judgement model of uncertainty and appraisal of risk.

In practice, AI combines several disciplines including statistics, regression analysis, computer science, and human domain expertise which can replace or supplement nearly all previous forms of analytics. If these techniques were applied to insurance in a similar way as other data-rich industries, then AI could be a major force in driving transformation across the industry.

Man meets machine

The combination of AI with human intelligence is sometimes referred to as augmented intelligence. The application of augmented intelligence to underwriting (‘augmented underwriting’) is critical to meeting business objectives. However, the underwriter’s skillset will have to evolve to make the best of this resource.

Machine and human approaches complement one another. Human expertise informs model design and output interpretation while quantitative methods identify trends, predictive indicators, and dynamics that are either new or too subtle for human perception. The result is underwriting that takes the best elements of statistics, expertise, and common sense.

Leading insurtechs see themselves as architects and enablers of the future of insurance, combining the best of what is working now with the grounded, intelligent application of the right technologies. Augmented AI brings additional analytical discipline to underwriting (for instance, by not charging less than a modelled rate plus a margin for new business) but allows the continued benefit of using a specialist labour resource (in peer reviewing the modelled outputs).

AI is not a replacement for human resource, but, when used properly, is a function which enhances human decision-making and brings out the best from underwriters. In a new model of augmented underwriting, expertise will be needed to monitor, ensure quality, and share such insights with brokers, clients and management. It is becoming clear that none of this involves technology alone, but equally that an underwriter who cannot embrace AI is going to be left at a clear disadvantage.

Cyber: The testing ground

Cyber has become both a shop window into insurtech and perhaps the most obvious class of business dominated by a data-driven approach, on both the primary and reinsurance side. We have observed that as software eats the world, cyber takes a large bite of insurance, perhaps overtaking property risk in prominence over the medium term.

That said, the market for cyber-insurance remains imbalanced, with demand outstripping supply. The insurance solutions available contain strict monetary limits, well below potential economic exposure, as well as exceptions for certain types of loss.

The primary barrier to industry growth has been accurate risk assessments to expand coverage at appropriate pricing, further impeded historically by a yet fully formed reinsurance proposition.

The evolution of cyber-analytics technology and its role in the insurance eco-system is key to market development. Currently no dominant design exists for cyber-analytics or for cyber-insurance policy scope.

Anomalies are present in the market due to a lack of transparency and purpose-built products to match emerging demand. System-building and clarity is required for the cyber insurance market to move from an innovation/tech-transfer stage to full commercialisation, with a sophisticated cyber reinsurance product of critical importance.

Power of partnerships

To understand and price the nature of cyber threats, insurers need to access vast computing power and embrace techniques such as machine learning. However, insurers are unlikely to have such technology in-house and could look to gain access to these complimentary assets through partnerships.

If insurers are to truly embrace these disruptive innovations, then they will need to manage the risks attendant with collaboration, innovation and value-chain integration.

In the second part of this exploration into convergence, we will look further into the power of partnerships.

Jonathan Spry
Envelop Factfile