Why AI In Insurance Is The Key To Survival

It took less than a decade for artificial intelligence to enter every sphere of public life. Its impact is felt in everyday tasks as well as the business-critical applications that underpin our economy.

Insurance is one of these areas, with providers increasingly leveraging AI to understand risk and reduce costs. Examples within the consumer field include "black box" telematics platforms that allow providers to gather all the relevant data points necessary to understand someone's driving style and adjust their premiums. Health and life insurance products often come bundled with fitness wearables, allowing providers to measure physical activity levels and thus identify whether a person has the lifestyle traits that correlate with longer, healthier lives.

These examples are barely the tip of the iceberg, and AI will play an even more meaningful role in the insurance field in the years to come. A recent McKinsey report echoes this sentiment. It highlights that AI will ultimately shoulder a more significant proportion of work currently being performed by humans and, most importantly, more complicated tasks.

McKinsey also predicted that AI would be employed in half of all claims processing tasks by the turn of the next decade, replacing several job titles currently filled by humans. Automated customer service touchpoints will become the norm for routine claims, including voice and text. Computer vision systems, paired with algorithms capable of absorbing the vast swaths of vehicle telemetry data generated by modern cars, will increasingly serve as loss adjusters.

Humans will continue to play a significant role in processing insurance claims, but their remit will shrink to cover the most esoteric and unusual cases. Although painful, this change is necessary for the insurance industry's sustainability (and thus, long-term survival). A 2020 report from BCG estimates that providers will need to reduce costs by 50% in the next five years. As the secondary sector of the economy shows, automation is the best way to achieve those savings without compromising on output.

But the urgency for the insurance industry to aggressively adopt AI transcends the bottom line. The ever-increasing digitalization of working and professional lives offers providers an opportunity to understand risk better and reduce the time taken to process claims. The delicate tightrope walked by insurers – balancing risk with premium costs while making a profit – will become radically easier.

What will this look like in practice? Expect insurers to become voracious consumers of data and expect risk determinations to become more fluid and real-time, with policy costs adjusting dynamically. Data sources will continue to expand, moving beyond the current automotive and wearable categories, and include a wider variety of smart devices, remote sensing and imaging.

With an understanding that data is increasingly necessary for more intelligent risk decision-making and operational efficiencies, insurers will widen their dragnet for data points, taking advantage of the proliferation of data portability and availability. Just as lenders like Klarna use open banking data to determine creditworthiness, insurance providers could efficiently use data to mitigate against fraud or determine a prospect’s risk profile.

The possibilities are endless. Those who successfully take advantage of this new paradigm shift will determine the winners and losers of the next decade, which promises to be a turbulent period for the insurance industry. Enthusiastic adopters will thrive, particularly those that recruit the best talent and find the most creative AI use-cases. At the same time, latecomers likely will suffer an irreversible decline.

Ira Scharf is chief strategy officer with Concirrus. He may be contacted at ira.scharf@innfeedback.com.

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