Tag Archives: Novarica

The Evolution of Telematics Programs

Thirteen years after Progressive launched Snapshot, its usage-based insurance (UBI) rewards program, telematics-based policies represent a modest part of personal and commercial lines insurance. Bullish estimates of double-digit adoption by 2020 haven’t materialized, but it’s clear that telematics-based products appeal to a need within the market. Adoption will likely continue to grow. Insurers should consider telematics strategically, whether they expect to enter the space or not.

Adoption: Modest but Real

Insurer telematics activity in recent years has split into rough thirds: About a third of property/casualty insurers are actively engaged, a third are monitoring the space but not yet acting and a third feel telematics don’t apply to them.

Overall, Novarica estimates the penetration of telematics programs at around 6% to 8% of insurers’ overall books, based on industry research and conversations with insurers. These numbers vary substantially from carrier to carrier. At some, telematics-backed policies can be more than 30% of their books, while, at others, it can be as little as 1%. 

Applications: Increasing in Variety

Insurers predominantly use telematics for underwriting and actuarial or product design. This approach aligns with the stereotypical UBI offering, where insurers rate drivers based on telematics data and offer retention discounts to those who prove to be safe risks. More insurers are also providing pay-per-mile offerings (such as Liberty Mutual’s ByMile and Nationwide’s SmartMiles), which charge customers based on the actual amount they drive.

Applications in other insurance functions are less common, but this is changing as both insurers and vendors innovate to offer new types of coverages and programs, like rewards programs to generate regular customer engagement or teen driving programs that can leverage telematics to create speed alerts. These offerings align with broader industry trends toward creating richer digital experiences, particularly in personal lines.

Insurers should also understand that getting the most out of these advanced features requires technological and business support beyond the telematics offering itself. For example, to support a feature like automatic first notice of loss (FNOL), insurers will need quality data, and they’ll need to be able to move it between systems across the enterprise. A comprehensive rewards program may require focused effort from marketing and customer service to stay on-message and deliver a seamless experience.

See also: Driving Into the Future of Telematics

Program Design: Essential for Success

The variety of telematics capabilities and offerings in the market means that insurers should design or expand their telematics programs with care and forethought. As with any technology initiative, the point of telematics-based insurance offerings is to better manage risk, reduce costs or create a superior customer experience.

For telematics, that means that insurers need to consider a number of factors to guide the features of their offerings. These include the target market segment, the channel through which the offering will be distributed, the services offered and how all of these elements align with existing technological capabilities and processes. There’s no one answer, and anything from a basic UBI product to an engaging rewards program could be the right fit, depending on what an insurer wants to accomplish.

Fortunately for insurers that have taken a wait-and-see approach, there are a number of products available in the marketplace, from turnkey telematics solutions to book-of-business analysis from a variety of telematics service providers and data brokers. Although early adopters like Progressive procured and managed their own telematics devices, insurers don’t have to do this anymore. Carriers that are new to the space shouldn’t spend time replicating technology that already exists.

Telematics Beyond 2020

Telematics adoption will likely continue to increase slowly but steadily over the next several years. Depending on the rate of growth, telematics-based policies could make up between $22 billion and $32 billion of the personal lines auto market by 2025.

COVID-19 will be a major factor in that growth. Anecdotally, Novarica has heard from both insurers and vendors that interest in pay-as-you-drive or pay-per-mile policies has increased in 2020 as more Americans are working from home. How long the pandemic lasts and whether widespread remote work becomes normalized could speed adoption for both insurers and policyholders.

Auto manufacturers have also been active in the space, with a number of recently announced partnerships to share driving data from connected vehicles with insurance companies. This, too, could speed telematics expansion by lowering the initial barrier to entry. 

Telematics-based insurance offerings are a small but real portion of the personal and commercial auto markets that will continue to grow. Telematics isn’t going away, but it also won’t dominate the auto insurance industry in the next five to 10 years.

At the same time, telematics doesn’t have to become dominant to affect consumer expectations around price, convenience and service. Insurers should consider potential impact now so that no matter what decision they make, it’s a strategic one.

To learn more about how insurers are using telematics, read Novarica’s full report Telematics in Insurance: Overview and Key Issues.

How Insurers Are Applying AI

AI is everywhere. Insurers are piloting various AI projects, insurance technology vendors are building it into their solutions, some insurtech startups are all AI-powered and horizontal tech vendors are creating AI platforms that sit underneath it all. Insurers that haven’t experimented with AI yet are benefiting from the technology through third-party relationships, even if they don’t realize it. 

Unfortunately, the broad scope covered by the umbrella term “AI” can cause confusion for insurers — especially because some technology providers use this label to better position their offerings in the marketplace.  

Usage of AI in the insurance world can typically be broken down into four categories:  

  • Machine Learning. The goal of machine learning, a process where an autonomous system learns from a data set to identify novel patterns, is often to refine underwriting or claims algorithms. Applications include advanced predictive modelling and analytics with unstructured data. 
  • Image Recognition. Until recently, images were a type of unstructured data better resolved by humans. Image recognition leverages AI to extract insights from digital image analyses. Applications include photo analysis and handwriting processing. 
  • Audio Recognition. AI-enhanced audio recognition captures any sound (from human speech to a car horn) and turns it into a rich, usable data source. Applications include speech recognition and non-voice audio recognition. 
  • Text Analysis. AI-powered text analysis is pulling out meaningful insights from a body of text (structured or unstructured). Applications include form reading and semantic querying. 

Justifying the Use of AI in Insurance

Novarica’s Three Levers of Value framework can help conceptualize the business value of each AI use case for insurers. Each of these levers — Sell More, Manage Risk Better and Cost Less to Operate — is applicable to a specific AI technology use case. 

Helping insurers identify upsell/cross-sell opportunities, for example, falls under sell more, while accelerating underwriting risk assessment could be categorized as managing risk better and enabling more efficient help desk support helps insurers cost less to operate. 

These are just a few examples of the value AI can bring insurers; AI use cases span categories such as product/actuarial, marketing, underwriting, customer service, billing, claims and compliance. Key use cases include: 

  • Deploying better pricing models. This machine learning use case chiefly falls in the domain of product owners and actuaries, as it applies to the area of predictive analytics. In this case, AI can help actuaries make better decisions when pricing products, thus managing risk better. 
  • Improving marketing effectiveness. This machine learning marketing use case involves using third-party or internal tools to analyze vast amounts of raw data and identify the media channels and marketing campaigns with the greatest reach and engagement levels. Here, big data analytics can help insurers sell more. 
  • Performing better property risk analysis. Using AI-powered photo analysis, underwriters can generate faster and more accurate roof damage estimates. Ultimately, this helps insurers manage risk better. 
  • Leveraging smart home assistants to deflect calls from call centers. Through a voice prompt to their smart home assistants, customers can get quotes, request policy changes and even start a home insurance claim thanks to AI-powered audio recognition. By offering another avenue to help answer customers’ FAQs, insurers free their call center employees to address more complex customer inquiries, decreasing operating costs. 
  • Increasing invoice processing speeds. Through use of text analysis and image recognition technology, AI can help billing staff eliminate error-prone human invoice handling. Using AI-powered form reading leads to greater process efficiencies, which lowers operating costs. 
  • Identifying and mitigating claims fraud. Here, machine learning can help identify potentially fraudulent claims faster. This processing speedup gives claims staff more time to focus on higher-value transactions and leads to better risk management. 
  • Enabling automatic handling of compliance requirements. Machine learning can help team members improve compliance and reporting by automatically handling complex compliance requirements. This results in lower operating costs as compliance staff can direct their attention to tasks requiring human review. 

See also: 4 Post-COVID-19 Trends for Insurers

The AI ecosystem is evolving quickly, with new technology applications emerging every day. We may soon even see further AI and ML processing speedups with the advent of quantum artificial intelligence and machine learning.  

Insurers should not invest in technology-driven projects; instead, governance should search for use-case-driven projects that most benefit the company. However, in the case of important emerging technologies — like AI and ML — it’s valuable to look for ways to deploy that technology and build up skill sets (and culture) within the organization. Additionally, many insurers have an innovation group whose sole purpose is to future-proof the organization by seeking out opportunities to deploy emerging technologies. In these cases, it’s important to refer to actual business use cases and elucidate the concrete value they provide to specific business units.

To learn more on this topic, check out Novarica’s brief, Artificial Intelligence Use Cases in Insurance.

Things Heating Up in Low-/No-Code

In the last few weeks, another tech giant entered the low-/no-code space when Amazon announced a new platform called AWS Honeycode. While the product does not break new ground from a technology perspective and is still immature relative to the market, it has created an avalanche of media activity centered on low-/no-code technology. 

Low-/no-code is a complex space, but it’s worth the effort to navigate. It promises to address three of the biggest challenges in insurance IT: time to market for new capabilities, development capacity and managing cost. 

What Low-/No-Code Looks Like

Low-/no-code is both a development paradigm whose influence continues to broaden and a rich market segment with dozens of vendors completing for mindshare. 

As a development paradigm, low-/no-code is everywhere. It can be found embedded in CRM platforms such as Salesforce and MS Dynamics, in integration tools from vendors like Informatica and Dell, in robotic process automation (RPA) tools like Blue Prism, in digital experience platforms (DXPs) such as Liferay and Sitecore and even in some policy administrations systems.

The market segment is often referred to as Application-Platform-as-a-Service, or aPaaS, because most solutions are now deployed in the cloud using SaaS licenses. Vendors such as Mendix, OutSystems, Microsoft (PowerApps) and now Amazon market their products as general-purpose development platforms to compete with (and eventually displace) traditional development environments like Java and .NET. 

While some of these vendors include industry-specific functionality, most market to a range of industries and seek to compete on horizontal capabilities such as integration, workflow, native mobile support, user experience (UX) and the strength of their partner ecosystem/network. A recent Novarica report covers this segment.

Insurance-Specific Variations

The insurance industry also has its own industry-specific low-code platforms that Novarica refers to as insurance digital platforms (IDPs). Their DNA can be traced back to the agent portal. When core vendors began to offer portals as part of their administration suites, they found that adoption was lower than anticipated, especially among midsize and large insurers who chose to build instead. Packaged portals were seen as too restrictive in terms of customer experience (where insurers like to differentiate) and difficult to extend beyond the basic capabilities offered by the vendors’ back-end systems. 

Vendors answered this need with the IDP. Digital platforms include pre-built integration to a vendor’s back-end system but are typically stand-alone and licensed/deployed independently. Their focus is insurance digital experience (web and mobile) applications, but they can be general-purpose enough to tackle a wide range of front- and back-office problems across the enterprise. 

Like the horizontal players, IDPs often feature a rich partner ecosystem or network of plug-ins and canned integrations, but these tend to focus on insurance-specific capabilities needed for submissions, underwriting, rating, accessing third-party data and payment processing. Examples of IDPs include Majesco’s Digital1st and Sapiens’ DigitalSuite.

See also: Agile, Organizational Realignment

Does Specificity Matter?

The question is whether any of these marketing distinctions really matter. The answer is, as usual, “it depends.” While low-code platforms do overlap in many of their core capabilities, differences become clear when considering the specific uses that an insurer needs to support. 

For insurers already running a vendor’s core systems, there can be compelling advantages to using the IDP from that vendor, especially when the primary use case is building agent or customer portals. If integration with multiple back-end systems is needed, insurers should also consider a broader range of options including the horizontal players. Some platforms are better suited to workflow/BPM, others are better for mobile development and still others excel in building customer-centric applications. 

For an insurer focused on building customer-facing websites that require personalization, content management, social media integration and some lightweight application development, a DXP with low-code technology may actually be the best fit.

Another consideration is the target developer. Some vendors tout the benefits of “citizen development,” where line-of-business resources trained to build their own apps. Other vendors aim to make the traditional developer more productive, and some focus on enabling a mix of both with powerful features for business/IT co-development. 

A final differentiator is licensing and pricing. While horizontal low-/no-code tools and DXPs compete with each other at scale, IDPs are often priced differently, and bundling with a core system purchase can be a pricing consideration.

A recent Novarica snap poll of insurers found that about 50% were using or had piloted a low-code platform. Novarica projects that by 2025, at least 80% of custom development projects in the industry will involve low-/no-code technology. All the big tech players now have a foothold in the space, and that makes it an area insurers should watch closely.

Machine Vision Usage in Insurance

Insurers now have access to an unprecedented quantity of image and video data. Many still manually review these data sources, but this provides limited insight. Carriers are beginning to invest in machine vision technology to process this data, programmatically analyzing risk factors and making sense of these vast image stores.

Machine Vision: What Is It?

Machine vision is the AI-based analysis of images from sources like smartphone photos, drones, low-lying aircraft, satellites and dashcams. Machine vision platforms offer analysis—i.e., the ability to upload images from a proprietary source into a platform—or they can be trained from scratch to work with an insurer’s business. Dedicated platforms can provide a relatively lightweight way to help insurers automate, scale and enhance risk evaluation while seeing gains in operational efficiency and cost reduction.

The Move to Purpose-Built Platforms

General machine learning platforms may be capable of image- and video-based analysis of risk factors in the not-too-distant future. Yet, for the time being, insurers are likely to see more tangible results by implementing a machine vision platform built specifically for insurance needs in claims and underwriting. These solutions are likely to provide more value with fewer resources and less investment.

Some purpose-built machine vision solutions for the insurance industry may use general-purpose platforms from other providers behind the scenes. But the insurance-focused vendors have done the work of training solutions for specific insurance use cases so that insurers don’t have to.

See also: Rise of the Machines in Insurance  

Machine Vision Use Cases

Most current machine vision use cases focus on commercial and personal property underwriting and claims due to the proliferation of property imagery, especially for roof analysis. Usage is emerging for auto claims, where the predominant application is claims damage and estimation. Machine vision is mostly exploratory in other lines of business; one emerging example is life insurance, in which machine vision can perform image analysis to aid in underwriting.

Use of images to determine claims and underwriting risk factors isn’t necessarily a new concept for insurers; underwriters have been using sources like Google satellite images for years for this precise purpose. Yet unstructured sources of photo and video data continue to proliferate, and machine vision can help insurers evaluate a broader range of risk and automate decision-making.

More information on the space is available in Novarica’s latest report, Machine Vision in Insurance: Use Cases and Emerging Providers, which provides an overview of machine vision technology as well as prominent vendors.

The Need for Clarity and Realignment

At the 11th Annual Novarica Insurance Technology Research Council meeting, two keynotes laid out some fundamental issues for the industry to address.

Novarica Keynote: Key Insurance and Technology Trends for 2018 and Beyond

If I had to pick out a single dominant theme of my presentation on Novarica’s recent research and guidance to clients, it would be realignment. Rapid changes in technology capabilities are opening the door to realignment of the insurance value chain and product itself, as well as insurers’ technology stacks, and the management of technology organizations within insurers.

Realignment of the Value Chain and Product. We’ve been talking for the past few years about how advances in information technology make it easier than ever to analyze, package and transfer risk. Each of the traditional participants in the value chain between individuals or companies and the capital markets (i.e., distributors, primaries and reinsurers) is under immense pressure to prove added value and avoid disintermediation. We’re also seeing insurers start to leverage their risk management knowledge into products beyond loss reimbursement, with companies like Allstate commercializing their telematics capabilities and even selling their roadside assistance capabilities on a fee basis through partnerships.

See also: 9 Key Questions for Insurer IT Leaders  

Realignment of the Technology Stack. While insurers continue to strive for advantage in data and digital, and to build a solid foundation for agility and evolution by replacing legacy core systems, we’re starting to see two major changes. The first, which is more pronounced, is the incredible growth of cloud computing. Our research has shown a major shift in acceptance and embrace of cloud, and several meeting participants told us they plan to be 100% cloud-based within two years. The second, which is still at an earlier stage, is the embrace of microservices architectures, and the adoption of a capabilities-level architecture rather than an application-level architecture. This is something we’ll be watching closely in the next few years.

Realignment of the Technology Organization. All business units are more dependent on technology than ever before, and the widespread adoption of agile is helping to improve communications, relationships and collaboration between IT and other business units in many ways. But there’s still a fundamental disconnect in many companies between the way that IT evaluates its own performance and the way that other business units evaluate IT’s contribution to achieving the company’s goals. We published research this year on the benefits of using business KPIs and IT value metrics, to ensure shared understanding and the feeling of shared values between IT and other business units.

I closed with our nine questions for insurer IT leaders, all of which encourage re-evaluation of current practices and attitudes from an outside perspective. For example, instead of asking how to manage the threat of insurtech, ask what can be learned from these new entrants that are approaching the industry with a fresh point of view. Instead of asking how to win the war for talent, ask what is the value of working at your company? And instead of asking how to justify an IT investment, ask, how does the IT capability drive business results?

Guest Keynote: Scaling and Growing High-Performance Organizations

Chris Yeh has founded, invested in or advised more than 50 high-tech startups. He is the co-author, with LinkedIn founder Reid Hoffman, of The Alliance: Managing Talent in the Networked Age and the forthcoming Blitzscaling, based on a class he team-taught with Hoffman and others at Stanford. His presentation covered material from both works. If I had to pick out a single theme from his keynote, it would be clarity.

Blitzscaling, the ability to grow an enterprise quickly, requires a clear understanding of the goals and risks. It’s defined as: “The pursuit of rapid growth by prioritizing speed over efficiency in the face of uncertainty.” This is a conscious choice to do things in a particular way that might be viewed as “wrong” by other frameworks but makes perfect sense when viewed against the Blitzscaling opportunity.

The clarity of the strategic decision cuts through the noise of demands for efficiency. While insurers may not have many opportunities to Blitzscale, having this same level of clarity around goals to insulate them from traditional operational demands is critical to the ability to drive innovation.

See also: How Technology Drives a ‘New Normal’  

The Alliance framework for talent acquisition and management has a similar level of clarity to it. Companies and the people they need each have diverse objectives, some of which align and some of which do not. However, most talent strategies don’t acknowledge this, and are built on a level of disingenuity on both sides. By starting from a clear-eyed assumption that the employee is building a career that may involve leaving the company at some point, both parties can focus on creating mutual value and growth during the period of their alliance.

As one CIO commented to me later, “Chris talking about looking at your employees as having ‘tours of duty’ and how we as leaders need to look at how we help them ‘level up’ was very relevant to some actual personnel situations I’m dealing with.”