In Part 1 of our Artificial Intelligence (AI) Impact on the Insurance Value Chain series, we looked at the impact it has on underwriting and risk. However, before an individual can be underwritten, an insurance product still needs to be sold and distributed, which is what Part 2 of this series will focus on. According to McKinsey, this step of distribution and selling is the most crowded in insurtech, with nearly 40% of startups attempting to change how consumers buy insurance.
The hottest category of Insurtech in 2017 has arguably been artificial intelligence (AI) as 5 of the largest 15 deals have gone to AI startups. This builds off a strong 2016 that saw 45% of investments in Insurtech going to startups utilizing big data analytics and AI (Rise of the Insurtech – Accenture report). The increased interest and investment in AI is largely due to the prevalence of human interaction and manual processes throughout the insurance value chain.
In my previous blog, I explained various factors Information Technology (IT) teams should consider when implementing solutions for reinsurance operations. I also touched on the fact that IT and reinsurance operations should go together like peanut butter and jelly. But I realized that this is not always the case. In fact, our 2015 Industry Study on Life Reinsurance Operations & IT uncovered gaps in the relationship between the two departments. So why do these gaps exist and what can you do to improve the relationship between IT and reinsurance operations? Find out below.
Now that you are back into the swing of things at work you’ve probably overheard or even talked to colleagues about New Year’s Resolutions. Maybe you want to read more books, go to the gym more, dedicate time for a digital detox? You’ve thought about resolutions for your personal life but what about at work?
Aside from perhaps being more organized or clearing out your inbox, have you considered what you hope to achieve as a business or even industry? I was curious to find out what this might look like for the insurance industry. What will its resolutions be for a successful New Year? I went on a mission to find out.
How? You might ask. I looked at our top performing blog topics in 2016 – and there were a few that our readers really sank their teeth into. I figured since these were top of mind in 2016, the industry might be looking to implement strategies around them in the new year. Find out my predictions below.
Did you know that the insurance and reinsurance sectors are among the industries with the lowest personal interest rates of social media users? Yet there are so many ways for insurance professionals to benefit and profit from being active users across a variety of social platforms! From finding exciting career opportunities to building a network of connections and getting involved in important industry discussions – just to name a few. Earlier this year I shared 10 Reasons why Insurance Professionals Should Be on Social Media, and while those reasons all hold true, a social media ‘disparity’ still exists in the life insurance industry.
Did you know that tweets could soon be used to help insurers price premiums? That’s right! Insurance companies are looking into ways to combine social media data with artificial intelligence and predictive modeling to inform coverage among other aspects of insurance programs. Or at least that is what Paul Lucas of Insurance Business Magazine explained in his recent article!
As someone new to insurance, reading about innovative ideas such as this gets me excited for the future of the industry. I mean, it really seems like the opportunities are endless! I had a similar ‘awe-inspired’ experience after recently hearing insights from The Insurance Disrupted Conference.
Have you heard of behaviour economics? It has to do with studying the effects of psychological, social, cognitive and emotional factors on the economic decisions of individuals. It was also one of the topics discussed at the ELHUA conference recently held in Madrid. So why was this a topic of discussion at a European underwriting conference? Find out below where I breakdown insights from this topic and other highlights from the conference.
If you had the chance to read my colleague’s article on our trip the Silicon Valley earlier this year, you’ll know that the visionaries and innovators in this space have a keen interest in the insurance industry. During this trip, we learned about the Silicon Valley Insurance Accelerator (SVIA). An organization that connects the established insurance industry with the Valley’s network of innovative technology, InsurTech Startups, Venture firms and other global innovation hubs. The goal is to facilitate the flow of market, technology insights, and investment, ultimately accelerating innovation in the insurance industry.
They recently hosted the Insurance Disrupted Conference to talk about the next big wave disrupting InsurTech. We had the chance to hear about big data, analytics and AI from some seriously innovative companies including Lumiata, Banjo, XL Innovate and NAUTO. All players in the Insurtech space. Being part of a company that is both facilitating and embracing the changing world of insurance, this conference left me feeling energized and excited for what is to come. Which inspired me to share 3 things the insurance industry can learn from the SVIA Insurance Disrupted Conference.
How does big data truly relate to life insurance? In Part I of this series, I defined Big Data, the difference between structured and unstructured data, data analytics vs predictive analytics and briefly shed light on the current buzz big data is having in the life insurance industry. Now it’s time to expand more on that last note, and really understand the role big data has in life insurance and how it affects the profession of life underwriting. So let’s dive in:
Big Data is a term that has gained momentum in recent years but the cause of it has been around for much, much longer. Industries have been collecting data for as long as they have been around but in recent years, more and more of it has been streaming in at incredibly fast speeds and volumes due to the facilitated process we know today. Additionally, the type of data coming in is far more different in format: numerical, text, video, email, plus many more. The industries in which we operate, life insurance and reinsurance specifically, we can certainly attest to the fact that big data is on the rise.
So Big Data = extremely large volumes of data which can be analyzed to detect patterns... But how is the data classified, and how does it relate to the life insurance industry?