As we welcome the new year, it's natural to reflect on the year that passed and look ahead to the challenges and opportunities that lie ahead, and more specifically how new technologies might impact the insurance industry. As always, we must separate the signal from the noise. For many, artificial intelligence is a perennial buzzword, but paradoxically, it appears the technology is largely still in its infancy in the insurance industry, and especially in the home insurance space. Regulators and insurers alike are understandably grappling with challenges created by the lack of model explainability, presenting challenges for the widespread use of AI to directly evaluate and price risk for homeowners insurance in the near future. Instead, major technological innovation in homeowners insurance in the coming year will likely come from solutions and tools designed to improve the ingestion and processing of data in ways that positively impact the consumer experience throughout their homeownership journey.

Insurance technology brings in more innovative solutions for homeowners and insurers thanks to artificial intelligence (AI). AI is disrupting the industry by allowing for faster and more user-friendly claims and creating more transparent and customized policies that suit the client's situation. This replaces the traditionally long and painful process of getting a claim or settling for a one-size-fits-all property insurance policy. AI Property from Tractable, for example, allows anyone with a smartphone to access damage quickly and efficiently to buildings caused by hurricanes, floods, and other natural disasters. Use its mobile-friendly web-based app to take photos of the external conditions and submit them to Tractable's AI platform, trained on an extensive database of claims and damaged property.

The Insurance industry has been dealing with vast volumes of data for years, but analytics, Artificial Intelligence (AI) and Machine Learning (ML) techniques are increasingly being used to help insurance providers make faster data driven decisions. Given the exponential level of data available today with AI/ML, insurance providers can now efficiently extract new insights into their customer's needs and create stronger long-term value. Starting with how the market calculates premiums, the insurance sector now has access to thousands of data points to help them calculate premiums. Machine learning algorithms expedite the identification of the most predictive attributes driving claims losses – the most recent data points being historical cancellation data and gaps in cover. This helps insurers become more competitive, match their risks to the most appropriate pricing strategies and write the risks that meet their underwriting appetite.

Naked, South Africa's first end-to-end artificial intelligence-driven insurance platform, is building on its significant success in car insurance by bringing its next-generation insurance to the home insurance market. Customers can now get comprehensive, instant, and hassle-free cover for their home and the things they own through Naked's completely automated digital process. Naked offers customers a comprehensive set of short-term personal insurance products that are built on new generation technology and a fairer business model. In April 2018, Naked launched an award-winning* car insurance offering that uses automation to offer significant premium savings and higher levels of customer control over the insurance experience. Naked's comprehensive product range now includes home cover (building insurance up to R10 million) and contents insurance (up to R2.5 million).

Raghav serves as Content Lead at Emerj, covering our major industry areas and conducting research. Raghav has a personal interest in robotics, and previously worked for research firms like Frost & Sullivan and Infiniti Research. Insurers are looking to leverage all of the digital customer data that is now available to them, including one new data source that some of the largest insurance enterprises claim are actively collecting: real-time data streams from the Internet of Things (IoT). IoT devices, such as in-car sensors, smartphones, and smart appliances, can send insurers data on product usage and driving habits among other behaviors. In turn, this data could be fed into AI algorithms that may allow insurers to offer risk-based pricing and other popular services.

So why has the process of taking out an insurance policy – and making a claim – become so impersonal? The average home now houses contents worth £35,000, according to the Association of British Insurers – nearly £1 trillion in total. And that doesn't include the value of property itself. With the cost of fire, theft or water damage so high, it is no wonder householders choose to take control of the risk of damage, by taking out home insurance. But, sometimes, consumers feel like the partnership with their insurer is unbalanced and that the supplier holds all the cards.

Dramatic advances in artificial intelligence and machine-learning technologies have accelerated the ability of insurers to predict risk. Algorithms can find trends and patterns that help forecast the probability of a risk situation occurring again. By utilising internal and external data sources, algorithms are selected according to how a specific model fits with the insurer's data. This model is applied to predict or detect the likelihood of an event happening, such as a person needing medical attention abroad for travel insurance or a house flooding for home insurance. Insurance and assistance provider The Collinson Group uses a variety of predictive analytical tools to flash through terabytes of data to find variables, some of which it hadn't considered, to help predict customer risk and purchasing behaviour.