Insurtech applications in general insurance

In 2015, Klaus Schwab popularised the idea that the world is at the beginning of the ‘Fourth Industrial Revolution’, whereby cyber-physical systems are developed to connect the ‘real’ physical and biological worlds to the ‘virtual’ digital world. Within this broad context, insurtech serves as the confluence of new technologies with the insurance industry. In a forward sense, new technologies enhance the capture of new data sources and improves the processing and storage capabilities to be delivered in improving all aspects of the value chain. Simultaneously, such technologies also transform the risk landscape, economy and society, which ought to be relevant and considered by the insurance industry.

The Working Party has identified four broad areas of insurtech development.  

1. Internet of Things (IoT)

Sensors embedded in ‘things’ communicate state information to a data platform. This includes temperature, pressure, light, and acceleration, which are processed through layers of connectivity, edge gateway, data storage and processing, to reach the final reporting and visualisations that ultimately feed back into business processes and human collaboration. IoT may be utilised in many fields which are relevant to general insurance. This includes:

• Vehicle telematics, which impacts the nature of motor insurance risk.

• Smart home devices which have the potential for fire prevention.

• Energy consumption and other applications relevant to home and content insurance.

• Health and safety space as relevant to liability lines of insurance.

2. Image, video & audio data

There is a huge volume of data being captured in the form of images, video, and audio, which includes aerial photographs, satellite images, phonetic indexing, automatic speech recognition and medical imaging. These data sources can be utilised to improve underwriting, pricing and claims decisions for insurers, lowering the costs of acquiring high quality, verified data and enhancing risk management and fraud detection. Interpreting audio-visual data has been straightforward for humans, but the ever-evolving technology is now providing the capability to automate and scale this analysis.

3. AI and Intelligent Automation (IA)

AI has been a hot topic for years. Robotic process automation seeks to replace tasks, previously completed manually, to be captured in an algorithm to enhance productivity, efficiency, and/or quality in the value chain of an industry. Such algorithms increasingly involve a ‘learning’ component in coping with new input data, such as incorporating information on how humans deal with exceptions. This enhances the availability and utilisation of (big) data throughout industry, which are captured through interactions with stakeholders, internal operations, online sources as well as unstructured data (e.g. scanned documents, images) previously too expensive to analyse.

The investment of AI technology into Insurance covers most of the value chain. Most investments relate to the operations and claims functions of (re)insurers, suggesting a focus on improving efficiency in administrative tasks. Other areas of investment include underwriting/pricing, customer experience and product/proposition functions.

These seek to utilise AI in improving model algorithms, improving data collection and applying an initial screening of risks to discover any unusual patterns, as well as real-time capturing of exposures to risks and fundamental changes to insured risks, such as driverless cars and PAYG insurance.

Such investments in AI are driven by large composite insurers with greater resources, but small/medium firms may exhibit more agility in their approach to AI and the future of the industry. High-profile IPOs and other new insurtech entrants have attracted significant capital investment, but many rely on existing insurers for underwriting capacity.

4. Parametric insurance

Traditional indemnity-based insurance may be ill-suited to protecting intangible and virtual assets. In its place, parametric insurance had been developed so payment is triggered based on some index parameter that is correlated to, or acts as a proxy for, losses that might be suffered by the insured. This approach appears more transparent and will likely result in faster pay-outs and lower claims administration costs i.e., it removes the uncertainty of the policy being triggered.

Developments in insurtech has enabled a broader range of offerings for parametric insurance by making available more indices and triggers, and digital delivery can also dramatically reduce the cost of delivering such products. Parametric insurance is also seen as a potential solution to systemic or non-diversifiable risks. For instance, natural catastrophe events are not thought to be greatly correlated to global market returns. Such risks are viewed as diversification investments for capital providers, thereby constitutes alternative capital sources to support a simple parametric proposition without the need for full analytical and underwriting capabilities.

How does insurtech interact with the actuarial context?

As discussed, insurtech is likely to impact and transform many core actuarial fields, such as pricing and reserving. Additional data available from IoT, audio-visual and other ‘big data’ sources enhance the richness of information and potentially accuracy of exposures, which may demand new skillsets in data and analytics. This then leads to expanding actuarial contribution to product development shifts from pure insurance models – where the insurer pays out after something bad happens – to risk prevention and mitigation services.

More detailed and timely data about claims and the surrounding circumstances may be available to increase the accuracy of case reserves and speed up settlements. In the short-term, transition issues may arise, including changes in claims development patterns. If insurance products do morph into hybrid ‘risk as a service’ type products that blend elements of insurance with risk management, IFRS-17 may require similar treatment to actual insurance. AI may also help streamline capital modelling.

Evidently, insurtech represents a tremendous opportunity for actuaries. These include new data and analytical techniques than enhance actuarial contribution to traditional fields and other parts of the value chain, as well as the reduction of time spent on low added-value tasks and errors in data processing through automation. However simultaneously, insurtech brings about threats such as the lack of capability to deal with new data, the replacement of some actuarial work by automated machinery, as well as potential over-regulation of the profession. Actuaries should embrace the aforementioned opportunities and overcome potential threats in order to become innovation leaders taking the insurance industry forward into the digital future.

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