Not long ago, ‘big data’ was a scary concept. As is almost always the case with misunderstood technology, people saw big data as a precursor to a dystopian, Orwellian future. What got glossed over during this initial skepticism was the vast benefits to business afforded by big data. Especially financial services (specifically insurtech), continue to benefit from big data. Insurance is one of the oldest financial institutions. Traditionally, policy seekers are assigned to a risk category when applying for a policy. The group is then adjusted, so enough people get lumped together to ensure the policies are profitable for the company.
This approach results in some people paying more than they should, based on the basic data used. Among other things, insurtech looks to tackle this data and analysis issue head-on. Insurtech companies look at the very traditional insurance industry because it’s ripe for innovation and disruption. Insurtech explore options and areas large insurers don’t, like highly customized policies and using big data streams to offer more dynamic premiums based on individuals, instead of traditional norms.
Insurance works because it’s a simple concept – giving people peace of mind for the price of a premium. Every person has a risk value to the insurer, and by pooling everyone’s risk together, insurers work out their underwriting criteria to decide premiums. The contradictory aims of both parties make insurers and customers an unlikely pairing. Insurers want customers that don’t claim, and customers want to avoid the consequences of claims.
Insurtech is the term given to companies that use innovations and technology to optimize efficiency within the insurance industry, first emerging around 2010. By leveraging state-of-the-art technology and third-party data sources, insurtech’s minimize consumer inputs and fill in the gaps using big data and AI, taking the insurance process to a whole new level. The by-product of these optimized processes is often savings – for the insurer and consumer. The sector is on track to achieve a compound annual growth rate of more than 41% by 2023, according to market research firm Technavio.
The term ‘big data’ is often thrown around and misused as much as it’s misunderstood. In its most basic form, big data refers to a data set that is so large (or complex) that it’s incredibly difficult to process and analyze traditionally, without help from technology to lighten the load. Because the amount of data is vast, the benefits of accessing it and using it can be staggering. For example, the retail industry uses big data to optimize stock levels and reduce spending, whereas manufacturing uses big data to fine-tune production numbers to minimize waste (and spend). The big data industry itself only continues to grow as applications become more prevalent – it’s expected to be worth $1trn by 2026.
When a customer needs to claim, it is an insurance company’s moment to shine. Traditionally, the claims process is long and arduous. Adjusters and handlers need to assess a claim to determine liability, likelihood of fraud, and see whether the insurer needs to payout. This can be painful for the customer and frustrating for insurance companies. Perhaps the most profound impact big data has had on insurance is with the claims journey.
Big data empowers the insurer to take decisive actions for the customer quickly, something not always possible for insurance companies. Insurtech lets the insurer assess the claim quicker, more accurately determine the level of appropriate payment, and has the potential to improve the customer journey because it should be quicker significantly.
Predictive analysis (which is powered by big data) helps reduce fraud. A 1% improvement for a $1b insurers’ loss ratio is worth more than $7m on the bottom line. Increasingly, big data is used to settle ‘simple’ claims. Letting big data automatically solve the simpler cases frees up expert claims handlers to deal with more complex cases, which protects jobs and offers customers a streamlined and improved customer journey.
Big data enhances long-established underwriting principles. In a similar way to the claims journey, underwriters are freed up from more straightforward decisions to focus their efforts on more complex cases.
Using enhanced analytics models to underwrite and price policies isn’t new to the insurance industry. But using almost real-time big data helps insurers access more granular, up-to-date information – letting underwriters price policies more accurately. For example, wearable technology gathers data constantly. It is used by insurers to refine their underwriting decisions to give a health insurance customer a more accurate price based on their individual risk, instead of being based on a large group.
Accurately assessing risk to arrive at prices has been the vital point of value creation for the insurance industry for centuries.
Insurtech’s have unlocked new possibilities for the insurance industry by giving them the ability and understanding to access big data capabilities. These big data sets give insurers a more detailed idea of their customers. Simply, it’s a win-win situation. The insurance company quotes accurate, competitive premiums, and customers get quotes that better reflect their level of risk.
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