Machine learning and AI in insurance
Charles Dugas Charles Dugas
February 28 5 min

Machine learning and AI in insurance

Prudence is a coveted and natural trait for an insurance executive. Yet today’s shifting customer expectations for on-demand, instant service coupled with the disruptive impact of new insurtechs or category crossovers by giants such as Amazon, means that insurers who want to maintain a competitive edge need to reinvent their existing ways of working.

Traditionally, the world of insurance is document and process heavy forcing insurance executives to seek the best solutions for improving tedious workflows, with a priority on claims processing. The challenges are significant: even today’s best-in-class software lacks the power to optimize the huge amount of data and variables originating from all the different clients, coverage plans and claims, and to read the details — all the fine print — for every case would be a near-endless task. And as the client base grows and fluctuates due to switching, so does the complexity.

What the insurance industry needs is a new approach, a dynamic ever-adaptive technology that augments key roles within the organisation by providing insights to make better decisions. Insurance needs AI. We believe that the adoption of artificial intelligence — a technology that has made unprecedented advancements in the past few years — will be essential for insurance firms to meet the ever-growing consumer demands for quick response times, precision pricing, and streamlined workflows.

Modern AI in insurance explained

AI in Insurance

In this first article of our Insurance-series, we aim to explore the various ways as to how AI can be used to give insurers a competitive advantage. Let us begin with an aerial view, and then we will cover key topics including:

  • Intelligent decision-making and first-mover advantage
  • How AI augments the role of an underwriter to improve efficiency, precision, and workflow management
  • How AI augments the role of a claims adjuster in workers’ compensation
  • How removing subjective bias can improve decision-making
  • A look at the future of the insurance industry powered by AI and Machine Learning (ML)

Modern AI explained

Technology in the form of algorithms have been standard workhorses in the insurance industry for decades, used mainly to improve efficiency and optimize processes. AI, however, is more than just a static standard technological process. AI systems are dynamic, they have the capacity to learn and as such they can be applied to decision-making areas to augment the quality or quantity of work handled by current employees. With this AI engine, every employee can make better, more informed decisions faster than ever before.

Traditional software requires pre-programming for every workflow, the deterministic approach. If this, then that. Such techniques are limited when it comes to the complex data and processing needs of today’s rapidly evolving insurance industry. Unlike the deterministic software of yesterday, AI systems are trained using probabilities and volatility to identify patterns in data that lead to valuable recommendations and better quality decisions — whether it’s navigating the subtleties of human handwriting to pick out the correct data field from a submission form or detecting a pattern of anomalies or outliers in claims processing.

AI is here to stay

While AI was theorized in the 1950s and some of its mathematical foundations were developed in the 1980s, until recently the data and computing power required to make AI systems work to their full potential did not exist.

What’s different now is that we have the data, power and tools to put AI to work at scale. The internet helped solve the data problem — 90 per cent of the world’s information was created in the past two years, and that has been true for the past 30 years — and the adoption of GPUs (graphics processing unit) to process calculations in support of CPUs (central processing unit) have made scalable, affordable AI systems a reality. Today we can see full-scale implementations of AI in our day-to-day such as with our virtual assistants by Amazon, Google or Apple or the self-driving technologies in Tesla or GM cars that are already in use.

Insurance firms primed for AI adoption

Insurance firms are primed for AI adoption

With data and processing power now readily available, the challenge facing insurance executives regarding AI adoption is political or strategic, not technical or financial. Insurance companies have the luxury of being data rich and with a plethora of opportunity to strategically position themselves to become leaders and innovators in AI.

In our next Insurance-series post we will explore what intelligent decision-making can look like when deployed within an insurance firm, and how insurers can turn these capabilities into long-term market advantages to create a sustained competitive edge.

For more information, contact us!