The Race to Find the Next Insurance Credit Score (or How, Maybe, to Reinvent P/C Insurance Pricing)

The Race to Find the Next Insurance Credit Score (or How, Maybe, to Reinvent P/C Insurance Pricing)

What is an insurance credit score? Basically it is a set of algorithms applied to data from credit reports which provide guidance for pricing and underwriting personal lines insurance. Although it has been a source of political and regulatory controversy over the years, the use of insurance credit scores is now widespread.

Much of the controversy has been over possible disparate impacts on various societal groups. But a root cause of the controversy has been the non-intuitive relationship between a given person’s use or misuse of credit on the one hand—and that person’s probability of incurring insured losses on the other hand. It just doesn’t seem to make much sense. But statistically there are correlations, which in general have passed regulatory review.

Insurance credit score controversies now ancient history (i.e. were settled before most millennials graduated from high school).

But suddenly something interesting is happening.

The race is on to find the next insurance credit score—and the winners (if there are winners) will gain a pricing (and underwriting) edge.

There are only two requirements to enter in this race.

  1. You have to forget about all the kinds of data and information that insurers have been using to price and underwrite risks.
  2. You have to use your digital imagination to find some new data and models which provide the same or better lift as the old data and models which you have just thrown out the window. (Lift is the increase in the ability of a new pricing model to distinguish between good and bad risks when compared to an existing pricing model.)

So what kind of new data might a digital imagination look at?

  • For personal auto, connected cars will provide a rich data set to mine. How about whether a car is serviced at the manufacturer’s suggested intervals (correlated with whether the car is serviced by a dealer or by an independent repair shop)? Or the use of a mobile phone while the car is in motion (correlated with time of day, precipitation, and whether satellite radio is also playing)? Or use of headlights during daylight hours (correlated with the frequency of manually shifting gears in a vehicle with an automatic transmission).
  • For homeowners insurance, connected homes could supply all types of new data. For example, whether Alexa (or other IPA) controls the home’s HVAC systems, correlated with setting security alarms before 11 pm). Or, electricity and gas consumption, correlated with use of video streaming services on week nights. Or the number and type of connected appliances, correlated with the number of functioning smoke, CO, and moisture detectors.
  • For commercial liability insurance, telematics and IoT will be the key data sources. Does a business with 10 or more commercial vehicles use both fleet management and telematics solutions? What mobile payment options are offered (correlated with dynamic pricing capabilities)? The business’ use of social media and messaging apps, correlated with the degree of supply chain digitization.

Of course obtaining a lot of this data will require permission from policyholders—and even with permission these methods may raise social or political issues. But premium discount and loss control incentives for telematics programs have proven effective. And for better or worse, Scott McNealy got it right in 1999.

The Great AI Wars

The Great AI Wars

Last week saw one of the last big players make their position in machine learning and AI clearer at Apple's WWDC event with the launch of their machine learning options. These days you're not a credible large cloud provider if you don't provide some interesting APIs around machine learning and AI with the likes of Google, IBM, Amazon, Microsoft (Azure), and Alibaba (Aliyun) to name but a few. Apple's discussion focuses on being able to embed these technologies on the device with the Apps rather than perhaps the building of the models and the execution – much less focus on pushing data into the cloud.

The war I speak of in the title however, is not some dystopian future where humanity fights for survival but rather the current war over talent that enables the use of these technologies. Insurers going through digital transformations and looking deeply at their analytics are finding they are competing with ever more unlikely companies for talent including rising InsurTech firms as observed in previous blogs. The good news is that basic machine learning capability and training is increasingly available as the democratisation of machine learning continues apace – in fact if you look at Apple's documentation this discusses the ease downloading and converting models and integrating them to Apps rather than the nuances of various training algorithms.

Machine learning isn't new to insurance with coverage in our predictive analytics reports courtesy of Nicolas Michellod and case studies. It is clear however that these tools and techniques are increasingly being embedded into solutions throughout the insurance eco-system and beyond – and they are raising customer expectations. A discussion on what this means for core systems is given in my recent report here, as well as a discussion on what this means for new front end opportunities with the rise of chat bots in our discussion on conversational systems and a broader discussion on the differences in designing intelligent systems versus programmed ones is discussed in designing the aware machine.

While AI is a battleground for the big players for insurers it is becoming an increasingly accessible source of new approaches and automation – both an opportunity to better serve customers as well as cut costs. The ease with which machine learning and AI can be embedded into simple applications now will only increase adoption and there are small things any insurer can do. Of course if you want to go much deeper, as pointed out in this Harvard Business Review article, if your company isn't good at analytics, it's not ready for AI. I disagree a little with the authors perhaps, we're in a world where anyone can do something – one can just download and convert a model and incorporate it into our systems as pitched by Apple.

For those looking to go further, the good news is there are many vendors that can help, and many partners too of all shapes and sizes. I'm happy to say the InsurTech investments in the industry are only increasing this number and the opportunities for applied AI in insurance. Further, there are many conferences discussing both analytics and the rise of AI – if you're attending or looking for them do get in touch, I or my colleagues would love to discuss.

Reflections from the Digital Insurance Agenda, Amsterdam

Reflections from the Digital Insurance Agenda, Amsterdam

Earlier this month Craig Beattie and I ventured off to Amsterdam to attend the Digital Insurance Agenda (DIA), where we also delivered a keynote. This was the event’s second year and, within just 12 months, it has grown significantly to around 850 people – attracting insurers, innovative technology players (from both the establishment and budding entrepreneurs), and investors from across Europe and beyond. The format is a sprightly mix of keynote presentations, panels, and live demonstrations. And, like last year, it was another great mix of people and ideas, each focused on driving change in customer engagement across the industry through technology.

(Venue: Gashouder at the Westergasfabriek. An impressive venue – with Celent on stage somewhere up there at the front :-))

Key take-aways for me were:

  • Distribution and front-end engagement remains a strong area of focus for innovation. However, unlike recent history where investment has been heavily channelled into mobile or touch-enabled browser experiences, the presence of chat and other app-less modes of interaction were strongly evidenced throughout most of the live demos. This has been a hot trend over the last 12 months, and where Celent has explored both insurer and consumer attitudes towards it (see Celent report: Applying Conversational Commerce to Insurance: Aligning IT to the Machine World). Given the issues that many insurers have had with trying to encourage customers to download their apps and engage with them through them, it’s not hard to see why 'smart chat' is being pursued so aggressively.
     
  • Heavier focus on the use of data for risk profiling and the application of emerging AI techniques (beyond chat use-cases). Personally, I find it incredible just how low the entry barriers have become for experimenting with data and AI. The perfect storm of huge compute power via the cloud, open-source and pay-per-use models for advanced technology enables those with relatively modest means and a great idea to get started. For me, this continues to be one of the most interesting areas in our industry for mining value. It’s also an area that insurers still find a challenge (see Celent report: Tackling the Big Data Challenges in Global Insurance: Differences Across Continents and Use Cases).
     
  • Celent has been tracking the development of innovation partnerships across the industry for a number of years (see Celent report: Insurer-Startup Partnerships: How to Maximize Insurtech Investments). At DIA, it was easy to see this in action. The vast majority of firms presenting were not a direct threat to the industry at large, but instead were exemplars of better ways of doing things through the use of smart technology. It’s not hard to envision that a few of the firms demonstrating at DIA will walk straight into pilots following the event.

The event was closed with a keynote from Scott Walcheck of Trov. Scott shared openly some of the progress that they have been making – which, to me, feels impressive. For example, they now have ~60-70 engineers working on the team and claim to be growing revenue by ~44% month-on-month (albeit from a starting position of zero).

Out of all of the insurtech start-up activity globally, there are just a handful of firms (in my opinion) who have the potential to really shake things up – and Trov is one of these.  They now have the capital, the engineering capacity and the partnerships to do some truly incredible things – if they choose to.

I also found it interesting to hear that they have started to evolve their business model into three focus areas, being: (1) Trov as a direct brand; (2) White-labelled Trov; and (3) Insurance-as-a-service, where they will rent their platform to partners – plus with an aspiration to evolve it into auto, home and other lines.  Given Celent’s focus on technology research across the industry, this last model-type is of keen interest. Trov’s engineering capacity is already a similar size to (and in some cases larger than) many mid-to-small insurance carriers. It is also larger than some of the traditional independent solution technology providers out there. Could they be the next big technology player on the scene in addition to their existing branded business?  Only time will tell, but it is clear they are already demonstrating how insurtech represents a new way of delivering insurance product development.

For more commentary on DIA, see Craig Beattie’s Moments on Twitter.  Also, keep checking the DIA website as they will shortly release some of the videos from the event.

The Death of Processes and the Birth of High Frequency Underwriting

The Death of Processes and the Birth of High Frequency Underwriting

Let’s consider a car insurance market where all new contracts go through online aggregators. Let’s assume all the car’s information (vehicle brand, type, category, plate number, etc.) and potential insured data (driver’s name, age, address, historical claims, etc.) is standardized, packaged to include all relevant information needed by an insurer to price a motor insurance risk, and instantaneously electronically transferred to an aggregator as soon as the car is purchased (payment triggers the packaged data transfer, quoting, binding, and contract sealing). With today’s technology and connectivity, this quote and bind process can be done in less than a second.

In insurance, a process is a series of tasks to turn raw data into valuable information to make a business decision. In other words, a process requires time between its inception and its end and almost always human intervention. Indeed, today’s quote and bind process captures relevant data about a car and a driver through a questionnaire. Then insurers need to evaluate the risk involved and price it before a quote is given and potentially accepted by the driver. Even though many digital interfaces can be offered to perform the whole process, the potential customer still has to respond to a set of questions and click on a button to accept a proposal. In our extreme digital example, all the tasks are reduced to a minimum of time and fully automated; the driver doesn’t even have to fill in a questionnaire since all relevant data is packaged, transmitted, analysed, and sealed into a new car insurance contract in less than a second as soon as the car purchase is triggered. Can we still call it a process when there is an instantaneous business decision (underwriting and pricing) made and an outcome (contract sealed) produced, and this without human intervention? Well, I think with extreme digital, processes as we know and define them today are dead.

The next question is: How can insurers differentiate in a world where customer engagement is nonexistent? One might think that it would be price. Actually, it is speed. Indeed, I think that the ability to match a specific demand faster than competitors will allow an insurer to win the deal. To do so, they’ll need to support what I call high frequency underwriting in order to have their quote matched with the demand side within milliseconds (faster than competitors’ quotes). Indeed, for the same price, the fastest proposition will win the deal.

Now, let’s get back to my initial assumption: all new contracts go through online aggregators. Let’s consider an aggregator owned by an insurance player. If this insurer can get time advantage versus other insurers feeding its aggregator, it will be able to adapt its quote to optimize its margin using competitors’ information and leverage the speed advantage, even though it is about milliseconds.

This scenario is not unrealistic, because high-frequency trading is about milliseconds. So, who knows, maybe one day we will see insurers not thinking much about processes, but focusing more on speed.

A Day to Celebrate: Celent 2017 Model Insurer Winners

A Day to Celebrate:  Celent 2017 Model Insurer Winners

Last April 4, Boston, a city surrounded by history of patriotism and independence, was witness of Celent Innovation and Insight Day (I&I day), an event in which 16 insurers were recognized as Model Insurers for their technological initiatives that, I’m sure, inspired more 280 professionals of the Financial Service industry by the efforts and ideas on how other insurers could implement them within their organizations.

Andrew Rear, chief executive of Munich Re Digital Partners was the Model Insurer keynote speaker. He discussed the role of Insuretech for large insurers and spoke of how these insurers could acquire agility, the pathway that they needed to choose, and more importantly, the risks they had to bear. He also discussed how Financial Services were redefining the way financial products are sold, delivered, and serviced.

No sensible website asks you for your email address anymore. They should know who you are by other means

~Andrew Rear

 

In the afternoon, our analysts participated in a series of debates focusing on the Internet of Things (IoT); Artificial Intelligence (AI); and Blockchain which was lively discussion. In between, Celent presented its Model Insurers for five categories and the Model Insurer of the Year.

Digital and Omnichannel

  • CUNA Mutual Group

The rapid development and launch of a simplified-issue term life insurance product that enables members to apply entirely online, answering only two health questions supported by a completely automated underwriting platform that delivers an instant decision in minutes.

  • Lincoln Financial Group

Lincoln Financial created a digital process to meet customer expectations of doing business, automate underwriting, reduce cycle time, and minimize human touch.

  • New York Life

The New York Life Portal initiative utilized digital connectivity and a ratings engine cloud-based platform to achieve a faster process and empower various actors across the organization.

To learn more of these Model Insurers, please read our report here.

Legacy and Ecosystem Transformation

  • Republic Indemnity

Republic Indemnity’s previous home-grown, legacy policy administration system was implemented in 1994 as a single state, Workers Compensation policy administration system. As the previous system could not issue multi-state policies and with the concern of technology obsolesce, Republic Indemnity looked for a new solution to replace its home-grown, legacy system.

  • ERS

Under new management, the business had to transform itself rapidly and replace 20-year-old technology. It had a major license renewal date in two years and would have been locked in by the vendor to a prohibitively expensive contract. It set about transforming claims first, and then policy with full data migration and scheme rationalization, all while growing the underlying gross written premium

  • Insurance Corporation of British Columbia

At the beginning of 2013, the Insurance Corporation of British Columbia (ICBC) launched the Insurance Sales and Administration System (ISAS) policy transformation program. This was the last project in ICBC’s overall $400 million Transformation Program, which had already successfully replaced legacy claims systems and implemented a new Enterprise Data Warehouse and an enterprise service-oriented architecture.

To learn more of these Model Insurers, please read our report here.

Innovation and Emerging Technologies

  • Suramericana de Seguros S.A.- Wesura

Wesura (Sura) created a peer-to-peer Insurance platform around social networks. It develops private insurance communities so final users can share risk and underwrite people who wants to belong to the private community, the bigger the community the more benefits one can receive.

  • Church Mutual Insurance Company

Church Mutual Insurance Company has partnered with The Hartford Steam Boiler Inspection and Insurance Company (HSB), part of Munich Re, to provide temperature and water sensors connected to a 24/7 monitoring system. This innovative Internet of Things (IoT) technology solution is designed to alert customers to take action before damages and disruptions to their ministries can occur.

  • Markerstudy Insurance

Markerstudy launched VisionTrack in February 2016 to tackle the challenge insurers are facing with rising fraudulent motor claims and to help improve driver behavior.

To learn more of these Model Insurers, please read our report here.

Operational Excellence

  • Aflac

Aflac was in need of some modernizing and is still likely to undergo more change as the industry continues to capitalize on social, mobile, and wearables. In response, the Aflac IT Division implemented an Agile Transformation to its projects and processes to meet the changing needs of the customers.

  • Saxon

Saxon serves the Cayman island community. With a limited pool to hire from or sell product to, Saxon realized that to remain viable in the insurance market, it needed to employ technology to better serve the needs of its customers and grow the business.

  • MassMutual

MassMutual offers a Data Science Development Program (DSDP) in Amherst, MA that trains promising, recent graduates to become well-rounded data scientists over a period of three years. The program combines rigorous academic coursework and practical data science projects for MassMutual — a unique and valuable combination.

To learn more of these Model Insurers, please read our report here.

Data Analytics

  • The Savings Bank Life Insurance Company of Massachusetts

SBLI implemented an advanced risk assessment solution using predictive modeling and data analytics to help reduce cycle times, decrease dropout rates, and eliminate the need to pull fluids and conduct exams, while pricing policies more competitively, placing applicants into appropriate risk classes, and improving customer experience.

  • StarStone Specialty Insurance Company

The initiative is based on the implementation of analytics tools to measure and reduce risk. The solution uses data from internal and external sources. The data may be structured or unstructured. This tool helps underwriters make better decisions.

  • Meteo Protect

Although a broker, Meteo Protect gives clients a means to evaluate how climate variability contributes to their companies’ results by analyzing the relationship between each business activity and the weather. It couples this with a platform to price and underwrite fully customized index-based weather insurance, for any business anywhere in the world.

To learn more of these Model Insurers, please read our report here.

CSE, Model Insurer of the Year

In 2017, CSE has been awarded Model Insurer of the Year for its aspiration to achieve “the best product in the industry.” This meant they had to overcome legacy thinking and practices to re-think all the features including coverage, pricing, rules, process, and communications To do so, they sought inputs from customers and analyzed the market using two common analyses: 5 Cs and SWOT. From this point on, CSE assembled and adapted its core system.

To learn more of the Model Insurers of the Year, please read our report here.

The quality of the submissions this year is a clear indication the industry is turning a corner and embracing transformation, digital initiatives, innovation and valuing data analytics.  It is inspiring to see the positive results the insurers have achieved and a pleasure to recognize them as Model Insurers for their best practices in insurance technology.

How about your company? As you read this, are you thinking of an initiative in your company that should be recognized? We are always looking for good examples of the use of technology in insurance. Stay tuned for more information regarding 2018 Model Insurer nominations.

The ABCD of Emerging Technology

The ABCD of Emerging Technology

Alphabet Blocks A to D

Celent has mapped over 45 emerging technologies in P&C and a similar number in Life & Health. That's way too much for an insurer to handle and the pace of technological change outpaces the industry's capacity to absorb it. You could say though that there is a set of 4 emerging technologies with the most potential to profoundly affect insurance; the ABCD of emerging technology:

  • Artificial Intelligence
  • Blockchain
  • Cloud
  • Data (big and small)

The four altogether become a strong enabler for Digital. Digital interactions, digital products, digital claims, everything digital. Digital becoming important to meet the expectations of customers that want insurance to be simple, right now, as I want it, when I want it, and relevant to me. On the other hand, many consumers are still not being attracted by insurance; creating a protection gap. Digital comes as a possible response to close this gap, and in the process has the ability to profoundly change insurance as we know it. Actually, we may not call it insurance anymore. It may just be something that comes as a warranty of the product or service. Have I gone mad?

Imagine cars with assisted driving. There is an accident involving the autopilot function and the manufacturer claims no responsibility. Who is going to buy this car after that incident? No surprise then to see some car manufacturers, vested in automated driving, indicate that they will assume liability. Of course they will, and in the process what they are doing is to offer their customers a guarantee that the product will perform as indicated in the user manual. By being able to monitor the car status they are also able to prevent accidents or breakdown. So in the future will you get car insurance or a manufacturer warranty?

You can imagine any other product that can be monitored, for example as part of the IoT. All these products will generate data, and that data will enable their manufacturers to provide a service; in many cases that service will be a preventive one. See the trend here?

Today many digital initiatives in insurance still rely on the use of a call center. That's not digital because it implies human to human interaction. Each interaction needs of a human in the call center, so each interaction adds cost as there is no way you can make the human person be digital. The use of chatbots or robo-advisors enabled by artificial intelligence and natural language capabilities allow digital interactions, where each interaction can be taken simultaneously by a robot with no, or marginal, cost to do it. By robot don't think about a physical robot but software instead. Just as the one used by Lemonade to settle claims fast.

Artificial intelligence with machine learning capabilities also allows us to mess with a huge amount of data; discovering new patterns. The more information ingested to these machines the better answers you get. The more is used, the more it learns, the smarter it gets. Even most importantly, this technology today is very good at taking repetitive and predictable processes and doing it faster, better and cheaper than humans. You are smart, you don't need me to explain how this is relevant to insurance, do you?

Technology as the one described here is available on demand and in the cloud. Need more computing power? being in the cloud can solve that problem very easily. Pay as you go? cloud deployments make this technology available at a per use price. Basically cloud makes technology accessible to anyone.

Blockchain is the glue that can hold it all together. Digital and physical assets (that can be digitized) can be stored in the blockchain. The IoT could be linked to blockchain. Then, any rules applicable to digital transactions can rely on smart contracts. Finally by providing trust and provenance through a decentralized body blockchain becomes the basis to catapult digital in any scale, even when peers don't know each other.

Are you mastering the ABCD of emerging technology? Not yet? Don't be left behind; insurers around the world have already started. Want to find more about how insurers can take advantage of emerging technology and innovation? Contact me or any person at Celent. We will be happy to dive into this with you.

How Insurity’s Acquisition of Valen Could Create a Virtuous Analytics Circle

How Insurity’s Acquisition of Valen Could Create a Virtuous Analytics Circle
It’s open season on insurance technology acquisitions in general, and for Insurity in particular. Today’s announcement of Insurity’s acquisition of Valen Analytics is now Insurity’s fourth acquisition in a multi-year string: Oceanwide, Tropics, and in rapid succession Systema and Valen.   The potential for crossing selling among the five customer bases is obvious.   Less obvious, but of potentially even greater value, is Insurity’s ability to invite all of its insurer and other customers to use its Enterprise Data Solutions IEV solution as the gateway to Valen’s contributory database and Valen’s InsureRight analytic platform.   Insurity now has the scale and the means to create a virtuous analytics circle: individual customers contributing a lot of data through IEV to Valens and receiving back analytic insights to feed into their pricing, underwriting, and claims operations.   Good move.

Data in insurance is not only about technology

Data in insurance is not only about technology
In October 2015, I explained that insurers had to hire more data experts if they wanted to better leverage all sorts of data sources they can access nowadays. As I raised this point, I shared the result of a survey we launched in 2015 to identify whether insurance companies were hiring new types of profiles to complement their teams looking at data. For more on this, read the following post: Why the insurance industry needs more data scientists. In March last year, I explained that insurers attempt to hire more data expert had become a clear trend: Insurers are investing in data scientists. With the growing importance of data in insurance and taking into consideration all the activities currently happening around data notably supported by InsurTech companies, we identify that not only insurers are hiring highly qualified data experts but also that these people are getting more and more influential within their organization. Indeed when asked about the level of influence on key business decisions these experts (internal or external consultants) have in their organization it seems that data scientists are gaining more power ​(in % of insurance respondents; n=135): Actually, two categories of experts are gaining influence in insurance: data scientists and user experience specialists. We are not surprised by this result. Insurers deal with a greater amount of data and more sophisticated technologies, therefore they need to hire highly educated experts in order to valuably leverage this data and these technologies. In addition, insurers consider customer interaction to be a key element of their digitization efforts and this is the reason why they are giving more responsibilities to user experience specialists. We will soon publish a report detailing the result of an insurance survey on the use of consumer data and smart technologies. I recommend you take some time to read our report to better understand what insurers are doing around this topic.

Smartphones, Apps, and Other Stuff

Smartphones, Apps, and Other Stuff

In 1985 when I was a kid in school, one of my favorite TV shows was Robotech, also known as Macross in some regions. They had the technology (alien technology by the way) to transform fighter planes into mechanical robots (a bit like Transformers), however they did not have either cellphones or smartphones. Instead, they had mobile cabs that would travel around the city looking out for when to pick the person up. Not to mention, in some episodes, they even had some kind of Google glasses. It was all very cool stuff in 1985.

Fortunately for us all, today we have our own smart stuff in the form of a super computer in our pockets – being the smartphone. Many of us no longer need to run to a red box to make a call; and a long with smartphones we have data usage, internet, and apps.

The great challenge with smartphones for insurers, is how to engage with customers in this mobile world; that is, how to make apps attractive to them beyond the basic proposition of moving consumers to the mobile channel in order to lower the operating cost.

In insurance, availability of mobile apps varies by region and by country, so does functionality.  In most countries property and casualty insurers are taking the lead, especially to connect to auto insurance policy holders to provide them with a very array of self-servicing features through the app. In many countries, insurers need to work with the regulators hand in hand to find the best ways to boost financial inclusion and the use of insurance through digital channels.

In a recent Celent’s report, we found that at least 80% of P&C insurers in the United States, the United Kingdom, Spain, and Portugal offer apps to their clients"

In Latin America availability of consumer-focused apps in insurance grew from 21% in 2013 to 39% in 2016"

So we expect in the following years that Latin American insurers keep up other regions. Not to mention that Insurers are very interested in mobility and they plan to invest in this technology.  To learn more about this report, please click here.

Going back to my story, there were occasions where the main character couldn't be contacted because there were no mobile phones, only robots, and maybe the outcome of the story might have changed.  It was 1985 for a story created much earlier; more than 30 years ago, but now mobility, artificial intelligence, robotics, and analytics are a reality.

Technology is playing a very important role enabling insurers to engage customers, and as part of the insurance industry, we need to be aware of these advancements. If you are interested in insurance technology and want to know more of case studies around world, Celent will be awarding the best technological initiatives in our 2017 Innovation & Insight Day in Boston on April 4, 2017

Also, if you are or know of an insurance company which exhibits best practices in the use of technology, please click here and complete the nomination form. Submissions are being accepted until December 16, 2016.  Categories include:

  • Digital and Omnichannel
  • Legacy and Ecosystem Transformation
  • Innovation and Emerging Technologies
  • Operational Excellence
  • Data Analytics

For more information about the Model Insurer program click here, leave a comment, or email me directly at lchipana@celent.com. I’d be more than happy to talk with you. The Celent team and I are looking forward to hearing from you and meeting you in person at the 2017 Innovation & Insight Day.

The Rise and Rise of Analytics in Insurance

The Rise and Rise of Analytics in Insurance

As noted in our prior research insurance has always been an industry that relies on advanced analytics and has always sought to predict the future (as it pertains to risk) based on the past. (For research on advanced analytics in insurers see here, here and here).

As observed in the last post here analytics, AI and automation has been a key focus of InsurTech firms but do not assume that the investment is limited to newbies and start-ups. I have for a few years now been attending and following the Strata+Hadoop conferences and others focused on advanced analytics and the broad range of tools and opportunities coming out of the big data organisations. This last week I attended a conference focused on the insurance industry and was surprised to see the two worlds have finally, genuinely overlapped – just take a look at the sponsors.

As Nicolas Michellod and I have noted in the past, insurers have already been investing in these technologies but only those that have made the effort to speak “insurance”. What the conversations at Insurance Analytics Europe (twitter feed) demonstrated was a new focus on core data science tools and capabilities. This continued the theme from DIA Barcelona (twitter) earlier in the year.

The event followed InsTech London’s meeting (Twitter) looking at data innovation and it’s opportunities for Lloyd’s, the London market and the TOM initiative. Here the focus was on InsurTech firms that would partner on analytics, would sell data or would enable non-data scientists to benefit from advances in machine learning, predictive analytics and other advanced analytics disciplines.

While this trend of democratising advanced analytics was discussed by analytics heads and CDO’s at the analytics conference the focus was much more on communicating value, surfacing existing capability and tools within the organisation and to put it bluntly, getting better at managing data.

In short – AI, Analytics, Machine Learning, Automation – these were all hot topics at InsurTech Connect and similar events but for the insurers out there – don’t assume these are purely the domain of InsurTech. Insurers are increasingly investing in these capabilities which in turn is attracting firms with a great deal to offer our industry. For those big data firms that ruled out insurance as a target market a couple of years ago – look again, the appetite is here.

As a techy and AI guy of old I am deeply enthused by this focus and excited to see what new offerings come out of the incumbent insurers and not just InsurTech.

Do have a look at the aware machine report and the blog too. We’re increasing our coverage in this area so if you have a solution focused on this space please reach out to Nicolas, Mike or myself so we can include you and for the insurers look out for a report shortly.