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What Are The Challenges To AI Adoption In Insurance? 11 Experts Share Their Insights

  • 23 September 2019
  • Sam Mire

Living without insurance is living quite dangerously. Having insurance but failing to know what you're buying into is a risky game of its own. You don't have to be in the dark, simply listen to the experts about how technology and other factors will alter the future of insurance. For example, these industry pros shared the greatest challenges to AI adoption in insurance. Here's what they said:

1. Jeremy Jawish, CEO & co-founder of Shift Technology

In many ways, AI is still viewed as a buzz word by a good number of people in the insurance industry. It’s also not a “solution” in and of itself. It’s a tool. It’s a piece of the puzzle. And that distinction isn’t always easy to make. Take Shift for example. We offer our clients solutions to address the business requirements of fraud detection and claims automation. We use AI within our products. We believe it’s the best way to achieve the desired outcome in these two particular areas. What we don’t do is offer our insurance carriers artificial intelligence. It’s a crucially important point to understand.”


2. Scott McConnell, President of Insurance for NTT DATA Services

There are several challenges, but the primary challenge is access to *quality* data. It’s a great first step to explore using AI. But if your data is a mess, you won’t realize all of the potential benefits. As they say, “you get out what you put in.” 

In addition, access to talent is a significant issue in insurance. IT talent is in short-supply across industries, and separately, insurance is having challenges attracting new, diverse talent. Without a talented team, it becomes more difficult to prioritize and deploy AI solutions.”


3. Chad Hawkinson, Senior Vice President, Data & Analytics at Vertafore

“The #1 challenge in AI adoption is the lack of truly proven, production-ready solutions. There are many experiments and proof of concepts being done, but very few that have been proven in the marketplace. Given the investment and changes in the insurance industry, I expect this to change rapidly over the coming 1-2 years as we start to see vendors, Vertafore included, offering game-changing capabilities that are proven to work in production.”


4. Ji Li, Director of Data Science at CLARA analytics

“AI is a powerful form of technology with the ability to transform the insurance industry in a number of ways. So far, the impact of AI in insurance has been minimal. The insurance industry has only begun its venture into AI, with many traditional insurers experimenting with new ways to incorporate it into their day-to-day operations in anticipation of further technological development. The biggest challenge to insurers is leaving legacy systems behind. This is a scarier proposition than AI to many companies. Yet, with the proliferation of cloud-based applications and services, there is an opportunity for organizations to no longer feel locked into a singular way of doing business, which is a game-changer. The cloud provides an unprecedented level of freedom to innovate in ways that are far less permanent and expensive.”


5. Ryohei Fujimaki, Ph.D., Founder & CEO of dotData

“The two biggest challenges in insurance are operational efficiency and customer relationship management. AI can be used to address both of these challenges, but implementing a data science program is not easy.

A typical enterprise data science project is highly complex and requires the deployment of an interdisciplinary team that involves assembling data engineers, developers, data scientists, subject matter experts, and individuals with other special skills and knowledge. This talent is scarce and costly. This is neither scalable nor sustainable for most organizations.

This challenge can be overcome with data science automation. Data science automation is an emerging AI-based technology that automates end-to-end data science process from business source data through data and feature engineering to machine learning in production. This frees up data scientists from lots of manual efforts, letting them focus on what to solve rather than how to solve. It also enables existing resources to execute data science projects, democratizing data science across the organization and establishing a data-driven culture.”


6. Christian Wiens, CEO of Getsafe

The biggest hurdle for insurers is their outdated IT infrastructure. On the one hand, life, health and property & casualty insurance sectors are separated into independent legal entities, which often work with different IT systems. On the other hand, cost and competitive pressure in recent years have led to smaller insurance companies being bought up by the “big ones” without integrating the IT infrastructure. The result is a patchwork of incompatible hardware and software in which customer data cannot be exchanged within a group or across multiple departments or divisions. Under these circumstances, it is difficult to realize a digital customer experience.”


7. Ryan McMahon, Vice President of Insurance at Cambridge Mobile Telematics 

“Insurers are preparing themselves for the future. Many people may not realize this but historically, insurers have been among the first adopters of technology to advance their business. Over the past several years, insurers have undergone a transformation to upgrade their core systems to be able to respond and utilize new technology. This process is still underway across the industry and many insurers are dedicating a large share of their resources to achieve the benefits of the transformation.”


8. Amir Cohen, co-founder and CTO of Planck

“After the AI-related use cases are well defined and technology barriers due to complex legacy IT environments are clear, AI will suffer at the beginning from lack of trust. Change is never an easy thing for people or organizations, and as it will permanently change processes that carriers have been running manually for decades, the switch will not happen in a day. Nonetheless, as insurance is a rather heavily regulated industry, it might require more careful and supervised use in order to meet the regulator standards.”


9. Saty Mahajan, co-founder & CTO of Bento

“There are really two challenges: expertise and implementation. The bulk of AI researchers and engineers are at powerhouse educational institutions, established tech companies, or startups. Hiring the best and brightest AI folks is always a challenge, but even more so with AI given the interesting projects across the industry and companies that are competing heavily for talent. Implementation is the other primary challenge. The systems used by insurance companies are older and often built using legacy technologies. Understanding how to adapt the current systems, or re-write them, without skipping a beat is a non-trivial challenge.”


10. Tanguy Touffut, co-founder and CEO of Descartes Underwriting

The challenge for AI today is not the lack of advancement in technology, but instead, 1) that insurance is more bureaucratic than other sectors, 2) data is constrained, and 3) AI increases demutualization.”

 


11. Dan Peate, CEO and Founder of Avinew

“The biggest challenge to AI adoption in the insurance industry is systems integration within big insurance companies. For example, if you are a Managing General Agent (MGA) for an insurance company, and the insurance company is relying on all kinds of really old legacy systems, it means you have people working with dated code. Integrating and updating legacy systems with the latest and greatest technology – like AI – is extremely difficult. If the insurance company isn’t able to adopt a modern platform, then as an MGA, you are dependent on slow-moving legacy systems to manage your data. The only way to move quickly in AI adoption is to become an insurance company and create your own, more modern platform.”


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About Sam Mire

Sam is a Market Research Analyst at Disruptor Daily. He's a trained journalist with experience in the field of disruptive technology. He’s versed in the impact that blockchain technology is having on industries of today, from healthcare to cannabis. He’s written extensively on the individuals and companies shaping the future of tech, working directly with many of them to advance their vision. Sam is known for writing work that brings value to industry professionals and the generally curious – as well as an occasional smile to the face.

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