Last year brought the insurance market increased competition, changing consumer expectations and agile opportunities, thanks to the pandemic’s working models.As COVID-19 bleeds into 2021, traditional insurers are increasing their investments in digital, agile and partnerships. The certainty of these investment decisions is likely to be with us long after the pandemic fades. We will continue to adapt to new agile operating models – likely at a pace that we historically have never experienced.
Insurers are increasingly comfortable with experimentation, a ‘fail fast’ attitude and quick partnership explorations with tech startups to scale their business. Agile is a matter not just of resources or market reach, but also of creating new bedrock business platforms and processes. Insurers’ ability to work with multiple partners simultaneously enables the quick movement from pilot to market to busi-ness as usual. The apparent winner will be the one that innovates, creates and can scale.
Claims processing, for instance, has always been conducted by an insurance adjuster. This model worked well in the past, but today the average insurance company can expect to have hundreds or even thousands of claims submitted in a single day. The quantity of information on a single claim has also skyrocketed to include information ranging from telematics to property sensors. Despite this surge in data, only 5% of insurance companies currently depend on process automation to review claims.
Why is that? Well, it could be as simple as vocabulary. It’s been documented that most adults have a vocabulary range of 30,000 to 35,000 words. The experts tell us that to be conversationally fluent in a foreign language, we need to know 1,000 to 3,000 words. Applying this logic to insurance, the terms glossary of the US National Association of Insurance Commissioners contains approx-imately 600 definitions, the Construction Design catalogue approximately 500 terms, and let’s add a 1,000-word vocabulary used by every adjuster.
Tools are being built today with that 2,000-plus-word vocabulary and the ability to ingest large amounts of data, including unstructured text, and to parse and learn from that data. My favourite example of this type of deep learning is Google’s AlphaGo. Google created a computer program with its own neural network that learned to play an abstract board game called Go, which requires sharp intellect and intuition. By playing against professional Go players, AlphaGo’s deep learning model learned how to play at a level never seen before in artificial intelligence. It caused quite a stir when AlphaGo defeated multiple world-renowned masters of the game – not only could a machine grasp the complex techniques and abstract aspects of the game, but it was becoming one of the greatest players of it as well.
Insurance executives have long struggled to assess the business value of AI. They understand the potential, but the general lack of institutional AI knowledge has made the evaluation process somewhat uncertain. Despite the uncertainty, executives remain undeterred from doubling down on their AI investments: 71% of AI adopters plan to increase their spending by an average of 26%, according to a recent Deloitte study.
The reason for the flurry of investment is that insurance C suites envision several operational benefits too exciting to pass up.
Vijay Pahuja is corporate SVP of client services for WNS, a provider of global business process management services.
