7 minute read 23 Aug 2019
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Three ways AI will transform the insurance industry

By Tapestry Networks

An independent firm

Convening leadership forums in financial services. Based in Waltham, Massachusetts, US.

7 minute read 23 Aug 2019

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Artificial intelligence can be a tremendous benefit if insurers manage the risks and opportunities – and learn from their experiences.  

Artificial intelligence (AI) is rapidly becoming an important technology in the insurance industry, as firms and their regulators investigate its potential and come to grips with its risks. While most boards are discussing AI, many are still in the early stages of understanding the technology and deciding how to oversee its strategic risks and opportunities. Insurance Governance Leadership Network (IGLN) (pdf) participants broadly agree that despite the potential for hype or unrealistic expectations, the impact will be significant. 

It has the potential for transformative changes, for resetting the business, for making big decisions in both property/casualty and life. You will be able to manage massive aggregates of data and use it to make better decisions.
IGLN Participant

On May 30 in London and June 10 in New York, 2019, IGLN participants — directors, executives, regulators, and other specialists — met to discuss AI and its implications for the industry. Three themes emerged: 

  • AI and its expanding capabilities
  • Implications of AI for insurance
  • AI raises new governance, oversight and trust issues

The network is organized and led by Tapestry Networks and supported by the EY organization.

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Chapter 1

AI and its expanding capabilities

Activity in AI is growing steadily – with investment in US start-ups doubling between 2015 and 2018.

AI is a rapidly evolving field that encompasses a range of technologies. The line between AI, advanced data analytics, and older risk-modeling techniques can be fuzzy. Participants noted that much of what is labeled as AI does not actually warrant the name and that the abstract concept of intelligence can muddy the waters. 

If you define intelligence as the way humans solve problems, it’s best not to use the term intelligence.
IGLN Summit participant

One participant tends to use the term machine learning (ML), the branch of AI that has been responsible for most recent breakthroughs. ML uses advanced statistical methods to extract patterns from data and establish its own rules for making decisions, rather than being programmed by people writing instructions. It then applies the rules it has established to new data sets, adjusting its parameters based on its record of successes and failures and improving its performance — in effect, learning from experience. 

Investment in AI has increased significantly, with the number of active AI start-ups in the US more than doubling between 2015 and 2018. Venture capital funding in AI start-ups grew 350% between 2013 and 2017.
IGLN Participant

The technical capabilities of AI and ML algorithms are improving considerably. The time it takes to train a computer to recognize images in a standard benchmark has fallen dramatically — from about an hour in mid-2017 to four minutes in late 2018. ML systems have surpassed humans’ ability to recognize spoken language; this has led to a range of applications, including virtual assistants, instantaneous translation of spoken or written texts, and virtual agents and chatbots that streamline customer service. Several participants cautioned against having unrealistic expectations about AI’s potential. However, others suggested that AI will be the most disruptive technology trend for the next few years and that it would be dangerous to underestimate its potential.

AI will be the most disruptive technology trend for the next number of years. Tech companies like Google, Microsoft, and Alibaba are now AI-first companies.
IGLN participant
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Chapter 2

Implications of AI for insurance

Participants agree the impact will be great, but differ on the pace of adoption and transformation.

While it is still early, insurers are exploring how to deploy AI and ML technology. Participants broadly agreed that the impact will be great, and some are beginning to invest substantially, while others are more cautious. “What problems does the industry think it needs AI to solve?” Insurers need first to identify new and highly competitive business models — the most value-adding applications — then determine how AI and ML can enable them.

Unless you know what you are trying to achieve, you will see bright shiny objects and go on playing with them. Having the right personnel involved in the decision-making process is critical.
IGLN participant

Current applications: improving operational effectiveness

Most AI applications are helping insurers increase efficiency and drive down costs—by automating underwriting, customer service, and claims processing. AI is improving insurers’ operations and business processes in:

  • Customer experience: Chatbots and automated assistants cut costs and allow round-the-clock customer service by automating responses to basic questions or handling simple complaints.
  • Claims management: AI can reduce the number of claims that require human analysis and interaction, thus lowering costs and improving the customer experience by speeding up claims resolution. 
  • Fraud detection and prevention: AI technology is increasingly deployed to detect and prevent fraud, which costs the industry an estimated $40 billion a year. 
  • Talent evaluation: ML systems are improving the assessment and coaching of front-line staff, while natural language processing systems are enabling better compliance and increased sales effectiveness.

Although most applications today focus on better execution of current business models, future applications could significantly change insurers’ understanding of risk, leading to the development of new products and services in ways that could dramatically reshape the industry.

Improved risk selection

AI can help insurers evaluate and price insurance risks through new kinds of modeling and data – even potentially replacing statistical models. IGLN participants suggested that AI and ML systems could enable superior underwriting and deliver a competitive advantage to carriers who are better able to deploy them. Insurtechs also are beginning to bring products to market that use AI to more accurately evaluate and price risk.

In this business, if you make the right risk decision, you win. The more info you have and can apply to making risk judgments will define winners and losers.
IGLN participant

New products and services

Crucially, AI is enabling a shift from the current model of indemnifying against damages to a model focused on risk mitigation and prevention. AI systems, using data provided by sensor technologies, can anticipate and potentially prevent risks. One participant said, “Prevention is much better than paying a claim. The service model is changing to one where you need to help people help themselves, which will reduce costs and reduce premiums.”

AI as an existential threat

Several IGLN participants suggested that AI will become a necessary tool for survival in the industry, as ATMs were in retail banking. The benefits arising from its use—including cost savings, better risk models, and new products—will force insurers to adopt AI technologies to compete. While competitive forces may lead every insurer to adopt AI and ML, some insurance leaders feel that advances in AI pose a threat to the industry as a whole. 

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Chapter 3

AI raises new governance, oversight and trust issues

Success will require focusing on talent, data, third-party relationships and oversight frameworks.

The growing relevance of AI is raising new governance and oversight issues for senior insurance leaders: ethics and trust, implementing organizational changes, navigating evolving regulatory frameworks, and effective board oversight.

Trust in AI has multiple dimensions, including freedom from bias, reliability transparency, and explicability. One challenge is that AI is often held to a higher standard than human beings. One participant pointed out, “The human brain is the most opaque algorithm you can find. We are applying standards to algorithms that we don’t apply to human brains.”

Getting the talent right

AI is shifting human capital demands. It’s particularly difficult to find those who can bridge the gap between business needs and technological capabilities. “You need new skill sets and business translators who can connect, who understand both the art of the possible and your business and your domain,” said one participant. Others acknowledged that insurers will need to reckon with the impact of automation — resulting from the deployment of AI, ML and other technologies — on the workforce. 

Several participants noted the increased need for third-party relationships. Many insurers are partnering with start-ups or technology providers to obtain the necessary capabilities, but insurers face challenges in identifying which start-ups are developing genuinely valuable solutions.

Getting data issues right

Data cleaning, maintenance, and engineering are crucial enablers. “Insurers haven’t invested much in core platforms for 30 years, so they don’t have confidence in the quality of their data,” said one participant. Some question how much data is needed and how to deal with it. 

AI also raises the stakes on cybersecurity. Data can be stolen, or as one participant noted, “adversaries can poison data sets.” The process, called adversarial ML, involves injecting statistical noise or false information into a system’s training data to affect outcomes.

Navigating the regulatory landscape

Widely shared regulatory frameworks for AI have yet to emerge, and there is concern that regulation will not keep up with the pace of change. Regulations differ worldwide. In the US, states are beginning to pass legislation affecting AI and ML, including privacy laws, increasing the complexity of the regulatory environment and leading the corporate community to push for uniform federal privacy legislation. In April 2019, the European Commission released its Ethics Guidelines for Trustworthy AI.

Participants emphasized regulators’ concern with potential bias and harm to consumers. Some suggested that regulators will focus on outcomes rather than prior evidence of bias in the models. For regulators no less than for corporations, changing technology requires new skills and the ability to attract the right talent to carry out supervisory responsibilities in the context of rapid change. One participant asked, “What does it mean to be a supervisor in that new world?”

Developing effective board oversight

One participant asked “At the board level, how do we take directional responsibility for where we want this to go?”. Board members, supervisors, and other stakeholders are working to improve their understanding and oversight of these technologies and are in the early stages of building governance structures and developing the necessary skills and expertise for AI and ML. 

As with other highly technical areas, boards are exploring how best to develop the necessary expertise and skills to govern and oversee AI.

This article is based on the Viewpoints from the recent meetings of the Insurance Governance Leadership Network and aims to capture the essence of network discussions and associated research.


As emerging technologies blur the lines between new and old techniques, insurers are expressing new interest artificial intelligence and machine learning. Investment is increasing and technical capabilities continue to improve dramatically. IGLN participants agree that the impact will be great, though differ on the pace of adoption and transformation. One message is clear: the changes will require new skills and talent to bridge the gap between business and technology. 

About this article

By Tapestry Networks

An independent firm

Convening leadership forums in financial services. Based in Waltham, Massachusetts, US.