It’s almost inevitable. Spend your working life identifying, analysing, quantifying and ascribing monetary value to risk, and you’re likely to have a fairly strong aversion to it. Or more accurately, an aversion to undertaking new endeavours with inadequately understood consequences. The insurance industry is, on any number of levels, the very definition of risk-averse.
And yet, insurance still has an appetite for innovation. If the insurtech sector is any indication, an interest in and requirement for new solutions has been recognised and is being addressed.
It may not fully employ the language of disruption that runs through the wider fintech market, but a quiet tech evolution has been taking place in insurance nonetheless. Case in point: the advent of automated underwriting facilitated by more advanced algorithms and data analysis.
Where insurtech does overlap with its more vocal fintech counterparts is in the greater use of artificial intelligence (AI) and machine learning to solve age-old problems around data analysis and interpretation.
About five years ago AI became a topic of conversation in insurance. Since then, it has often felt like a reality that is always just over the horizon – a target that keeps moving even as more and more efforts are directed towards it.
But recent research suggests that the progress made so far has not been in vain. The insurance industry is poised to embrace AI and the innovations that it affords. While the global value of insurance premiums underwritten by AI reached an estimated $1.3 billion this year, as stated by
However, AI is not a singular solution. Its promise of $2.3 billion in global cost savings, to be achieved through greater efficiencies and automation of resource-intensive tasks, will not be achieved in isolation.
Rather, AI remains part of a more complex ecosystem of data gathering and analysis. It can apply new technologies to get the best out of both established and emerging data sources that feature in underwriting offices around the world. It does not require existing investments to be ripped out, replaced or downgraded.
It is more helpful therefore to see AI as the differentiating factor in the latest generation of insurance IT: augmented automated underwriting (AAU).
AAU enables underwriters to spot patterns and connections that are either invisible to the human eye or which take normal, human-assisted processes large amounts of time and resources to identify.
Whereas earlier generations of automation were able to pick up the low-hanging fruit of insurance markets – individuals whose driving history fit into clearly delineated boxes, for example – AAU can take into account the rich complexity of the human experience. It can spot the nuances and individualities that populate the life market, for example, and translate those into accurate policies – good news for both underwriters and their customers.
AAU can significantly reduce the need for separate medical exams, repeated questions, and lengthy decision-making processes, and drastically increase the speed at which a potential insurer can get a quote and coverage, while continually improving the way risk is calculated and managed.
AAU can also ensure that the decision-making process remains in the hands of underwriters rather than IT departments, enabling them to set and update the rules and parameters according to their preferred business model. It consequently makes advanced, complex, and precise decision-making available to a broader range of underwriting businesses – which benefits those businesses, customers, and ultimately, the entire industry.
Augmented automated underwriting is an example of the realisation of AI’s promise. As such, it’s set to become one of the key talking points and disruptive technologies within the insurance industry. Undoubtedly, AAU is a technology that all progressive insurance organisations need to be considering for their future operations.
