Life Insurance Automated Underwriting – A 25 Year Journey

Automated underwriting has come a long way in the last 25 years. It may be surprising that there was automated underwriting 25 years ago. At that time, it was called ‘expert’ underwriting. The idea was right, but the timing was wrong. The underwriting engines were black box algorithms; there was no user interface; data was fed from a file to the system; programming was required to write rules; and specialized hardware was necessary to run the systems. Not surprisingly, this attempt at automating underwriting was dead on arrival.

The next major iteration occurred about ten years later. Automated underwriting systems included a user interface; rules were exposed (some programming was still required to change the rules); data interfaces were introduced to collect evidence from labs and the medical inquiry board; underwriting decisions could be overridden by the human underwriter; and workflow was provided. Some insurers chose to take a chance on this new technology, but it was not widely adopted. There were two strikes against it: cost and trust. The systems were expensive to purchase, and the time and costs involved in integrating and tailoring the systems to a specific company’s underwriting practice could not be outweighed by the benefits. The lack of benefits was partially because the underwriters did not trust the results. Many times this caused double work for the underwriters. The underwriters reviewed the automated underwriting results and then evaluated the case using manual procedures to ensure the automated risk class matched the manual results.

Moving ahead fifteen years to today, changes in the underwriting environment place greater demands on staff and management. Staff members are working from home, and contractors are floating in and out of the landscape, all while reinsurers are knocking on the insurer’s door. There are now state-of-the-art new business and underwriting (NBUW) systems that address the challenges associated with the new demands. The solutions do not just assess the risk but provide workflow, audit, and analytics capabilities that aid in the management process. Rules can be added and modified by the business users; evidence is provided as data so that the rules engine can evaluate the results and provide the exceptions for human review. Subjective manual random audits of hundreds of cases evolve into objective, data-driven perspectives from thousands of cases. Analytics provide insights on specific conditions and impairments over the spectrum of underwritten cases to provide a portfolio view of risk management. Underwriting inconsistencies become easy to find and specific training can be provided to improve quality.

.In our report, Underwriting Investments that Pay Off, Karen Monks and I found that the differences between insurers who are minimally automated and those that are moderately to highly automated are substantial.  For minimally automated insurers, the not in good order (NIGO) rates are four times higher, the cycle times are 30% longer, and the case manager to underwriter ratio is almost double compared to the metrics for the moderately to highly automated insurers. This outcome may not reflect your specific circumstances, but it is worth preparing a business case to understand the benefits. With the advances in the systems and the advantages provided for new business acquistion, there are few justifications for any company not to seek greater automation in their underwriting.  

To learn more about the adoption of current NBUW systems and the functionality offered in them, please read our new report, What’s Hot and What’s Not, Deal and Functionality Trends and Projections in the Life NBUW Market or join our webinar on this topic on Thursday, September 29.  You can sign up here.

 

 

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The Muslin is off the Lemon — Lemonade Launches

Today’s announcement by Lemonade provides an example of what actual disruption in insurance looks like. Disruption — the term is overused in the hype around innovation. In Celent’s research on innovation in insurance, we see that what is often tagged as disruptive is actually an improvement, not a displacement, of the existing business model.

The information released describes how Lemonade seeks to replace traditional insurance. Yes, they have built a digital insurance platform. Beyond that significant feat, they seek to replace the profit-seeking motive of their company with one based on charitable giving, acting as a Certified B-Corp (more info on B-Corps). They are also using the charitable motive as the guide to establish their risk sharing pools, thus creating the peer-to-peer dimension. Unlike other P2P efforts, Lemonade goes beyond broking the transaction and assumes the risk (reinsured by XL Catlin, Berkshire Hathaway and Lloyd’s of London, among others).

However, like other P2P models, such as Friendsurance, Lemonade faces a real challenge regarding customer education. The Celent report Friendsurance: Challenging the Business Model of a Social Insurance Startup — A Case Study details the journey of the German broker along a significant learning curve regarding just how much effort was required to teach consumers a new way to buy an old product.

The next few weeks will surface answers to they second-level questions about this new initiative such as:

• How/if their technical insurance products differ from standard home,renters, condo and co-op contracts;
• What happens to members of a risk sharing pool when the losses exceed funding;
• Will the bedrock assumption, that a commitment to charity will overcome self interest and result in expected levels of fraud reduction?

It is refreshing to see some disruption delivered in the midst of all the smoke around innovation. Celent toasts Lemonade and welcomes this challenge to business as usual!

 

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Vrooom: New Federal Guidance Should Accelerate Development of Autonomous Cars

September 20 was a good day for the development of autonomous cars. The Feds, as embodied by the Department of Transportation and the National Highway Traffic Safety Administration (NHTSA), have issued guidance and principles for the development of autonomous cars.

There are two key takeaways:

1. By issuing guidance, rather than regulation, the Feds are trying to facilitate, but not control, the technological developments that will lead to street-ready autonomous cars
2. The guidance makes some common sense delineations between what the federal government should do and what states should do

• The feds want one national standard for how manufacturers conduct driverless car R&D–following a 15 part safety assessment protocol (covering data recording, system safety, human:machine interface, etc.).
• The feds want the states to focus on vehicle licensing and registration, traffic laws, and motor vehicle insurance and liability

If actually followed (are you listening California?) the political and regulatory environment should speed the day when a consumer can walk into a dealer, and be driven out by a shiny, brand new autonomous car.

That day will be good for car buyers, for manufacturers, and for society as a whole.

However, for insurers that day will also hasten the decline of auto insurance—per the recent Celent report The End of Auto Insurance: A Scenario or a Prediction.

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Changing the Landscape of Customer Experience with Advanced Analytics

That timeless principle – “Know Your Customer” – has never been more relevant than today. Customer expectations are escalating rapidly. They want transparency in products and pricing; personalization of options and choices; and control throughout their interactions. For an insurance company, the path to success is to offer those products, choices, and interactions that are relevant to an individual at the time that they are needed. These offerings extend well beyond product needs and pricing options. Customers expect that easy, relevant experiences and interactions will be offered across multiple channels. After all, they get tailored recommendations from Amazon and Netflix – why not from their insurance company? Carriers have significant amounts of data necessary to know the customer deeply. It’s there in the public data showing the purchase of a new house or a marriage. It’s there on Facebook and LinkedIn as customers clearly talk about their life changes and new jobs. One of the newest trends is dynamic segmentation. Carriers are pulling in massive amounts of data from multiple sources creating finely grained segments and then using focused models to dynamically segment customers based on changing behaviors. This goes well beyond conventional predictive analytics. The new dimension to this is the dynamic nature of segmentation. A traditional segmentation model uses demographics to segment a customer into a broad tier and leaves them there. But with cognitive computing and machine learning an institution can create finely grained segments and can rapidly change that segmentation as customer behaviors change. To pull off this level of intervention at scale, a carrier needs technology that works simply and easily, pulling in data from a wide variety of sources – both structured and unstructured. The technology needs to be able to handle the scale of real-time analysis of that data and run the data through predictive and dynamic models. Models need to continuously learn and more accurately predict behaviors using cognitive computing. Doing this well allows an carrier to humanize a digital interaction and in a live channel, to augment the human so they can scale, allowing the human to focus on what they do best – build relationships with customers and exercise judgment around the relationship. Sophisticated carriers are using advanced analytics and machine learning as a powerful tool to find unexpected opportunities to improve sales, marketing and redefine the customer experience. These powerful tools are allowing carriers to go well beyond simple number crunching and reporting and improve their ability to listen and anticipate the needs of customers.

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The 2017 Model Insurer Nominations Start Now

It’s been five months since we awarded Zurich with our top distinguished award, Model Insurer of the Year, during our Innovation & Insight Day (I&I Day) on April 13. I&I Day has been growing and gaining recognition since its inception over 10 years ago. Over last two years, more than 200 financial services professionals joined us in New York City at Carnegie Hall in 2015 and at The Museum of American Finance in 2016 to celebrate the Model Insurer winners.

From September 15, we will be accepting Model Insurer nominations. The window for new entries will close on November 30. We are looking forward to receiving your best IT initiatives. You may be announced as a Model Insurer at our I&I Day in 2017. The Model Insurer award program recognizes projects that essentially answer the question: What would it look like for an insurance company to do everything right with today’s technology? It awards insurance companies which have successfully implemented a technology project in five categories:

• Data mastery and analytics.
• Digital and omnichannel technology.
• Innovation and emerging technologies.
• Legacy transformation.
• Operational excellence.

Some examples of initiatives that we awarded early this year are:

Model Insurer of the Year   

Zurich Insurance: Zurich developed Zurich Risk Panorama, an app that allows market-facing employees to navigate through Zurich’s large volumes of data, tools and capabilities in only a few clicks to offer customers a succinct overview of how to make their business more resilient. Zurich Risk Panorama provides dashboards that collate the knowledge, expertise and insights of Zurich experts via the data presented.

Data Mastery & Analytics

Asteron Life: Asteron Life created a new approach to underwriting audits called End-to-End Insights. It provides a portfolio level overview of risk management, creates the ability to identify trends, opportunities and pain points in real-time and identifies inefficiencies and inconsistencies in the underwriting process. 

Celina Insurance Group: Celina wanted to appoint agents in underdeveloped areas. To find areas with the highest potential for success, they created an analytics based agency prospecting tool. Using machine learning, multiple models were developed that scored over 4,000 zip codes to identify the best locations.

Farm Bureau Financial Services: FBFS decoupled its infrastructure by replacing point to point integration patterns with hub and spoke architecture. They utilized the ACORD Reference Architecture Data Model and developed near real time event-based messages.

Digital and Omnichannel

Sagicor Life Inc.: Sagicor designed and developed Accelewriting® , an eApp integrated with a rules engine; which uses analytic tools and databases to provide a final underwriting decision within one to two minutes on average for simplified issue products.

Gore Mutual Insurance Company: Gore created uBiz, the first complete ecommerce commercial insurance platform in Canada by leveraging a host of technology advancements to simplify the buying experience of small business customers.

Innovation and Emerging Technologies

Desjardins General Insurance Group: Ajusto, a smart phone mobile app for telematics auto insurance, was launched by Desjardins in March 2015. Driving is scored based on four criteria. The cumulative score can be converted into savings on the auto insurance premium at renewal.

John Hancock Financial Services: John Hancock developed the John Hancock Vitality solution. As part of the program, John Hancock Vitality members receive personalized health goals. The healthier their lifestyle, the more points they can accumulate to earn valuable rewards and discounts from leading retailers. Additionally, they can save as much as much as 15 percent off their annual premium.

Promutuel Assurance: Promutuel Insurance created a new change management strategy and built a global e-learning application, Campus, which uses a web-based approach that leverages self-service capabilities and gamificaton to make training easier, quicker, less costly and more convenient.

Legacy Transformation

GuideOne Insurance: GuideOne undertook a transformation project to reverse declines in its personal lines business. They launched new premier auto, standard auto, and non-standard auto products, as well as home, renter and umbrella products on a new policy administration system and a new agent portal.

Westchester, a Chubb Company: Chubb Solutions Fast Track™, a robust and flexible solution covering core business functionality, was built to support Chubb’s microbusiness unit’s core mission of establishing a “Producer First,” low-touch mindset through speed, accessibility, value, ease-of-use and relationships.

Teachers Life: Teachers Life has achieved a seamless, end-to-end online process for application, underwriting, policy issue and delivery for a variety of life products. Policyholders with a healthy lifestyle and basic financial needs can get coverage fast, in the privacy of their own homes, and pay premiums online in as little as 15 minutes.

Operational Excellence

Markerstudy Group: Markerstudy implemented the M-Powered IT Transformation Program which created an eco-system of best in class monitoring and infrastructure visualization tools to accelerate cross-functional collaboration and remove key-man dependencies.

Guarantee Insurance Company: In order to focus on their core competency of underwriting and managing a large book of workers compensation business, Guarantee Insurance outsourced its entire IT infrastructure.

Pacific Specialty Insurance Company: Complying with their vision is to become a virtual carrier, meaning all critical business applications will be housed in a cloud-based infrastructure, PSIC implemented their core systems in a cloud while upgrading infrastructure to accommodate growth in bandwidth demands.

If you have completed a project during the last two years that you feel is a role model for the industry, don’t hesitate to send us your initiative here. You may be the next Model Insurer of the year.

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.

See you there!

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Using private consumer data in insurance: Mind the gap!

Insurance is no different to other industries when it comes to capturing valuable data to improve business decisions. At Celent we have already discussed how and where in their operations insurance companies can leverage private consumer data they can find on social networks, blogs and so on. For more information you can read a report I have published this year explaining Social Media Intelligence in insurance.

Actually there are various factors influencing insurers' decision to actively use private consumer data out there including among others regulation, resources adequacy, data access and storage. I think that an ethical dimension will play a more important role going forward. More precisely I wonder whether consumers and insurers' perceptions about the use of private consumer data are divergent or similar:

• What do consumers really think about insurance companies using their private data on social networks and other internet platforms?
• What about insurers; does it pose an issue for them?

In order to assess this ethical dimension, we have asked both insurers worldwide and also consumers (in the US, UK, France, Germany and Italy) what where their view on this topic. To insurers, we simply asked them what best described their opinion about using consumer data available on social networks (Facebook, Twitter, LinkedIn, etc.) and other data sources on the internet (blogs, forums, etc.). To consumers, we asked what were their opinions about insurers using these open data sources for tracking people potentially engaged in fraud or criminal activity.

The following chart shows the result and indicates that there is a big gap between the two sides:

Overall what is good for consumers is not necessarily good for insurers. In the same way, what insurers want is not always in line with what consumers expect from their insurers. Going forward the question for insurance companies will be the find the right balance between the perceived value of private consumer data and customers' satisfaction. In addition, it will be tough for them to figure out the impact (pros and cons) of all factors at play in the decision to invest in technologies allowing for the efficient use of private consumer data accessible on the Internet.

At Celent, we are trying to define a framework that can help them structure their reasoning and make an optimal decision. So more to come in the coming weeks on this topic…

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