Tag Archives: Novarica

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.”

9 Key Questions for Insurer IT Leaders

Sometimes, finding the answer in confusing times is really a matter of knowing the right questions to ask. For example, the Novarica Nine Insurance and Technology Trends for 2018 and Beyond looks at three groups of three key issues:

  • External trends, like disaggregation of value, the insurtech bubble and drive for innovation leveraging emerging technology.
  • Technology capabilities in data and analytics, digital channels and core systems
  • IT management issues like security, talent and alignment and governance

In each of these nine areas, planning a successful strategy starts with asking the right questions. Some questions I encourage our insurer clients to ask themselves are:

What is our distinctive value to this stakeholder in transferring risk from individuals and businesses to the capital markets? (Disaggregation)

We’ve long talked about disintermediation of distributors, but, in the current environment, any intermediary between those looking to buy coverage and those looking to take risk are intermediaries. Insurers and reinsurers, as well as distributors, need to make sure they they’re providing a unique, differentiated value to justify their position in the value chain.

See also: Predictions From 6 Insurtech Leaders  

What’s different about our approach to the problem? (Insurtech)

There are many different opportunities to engage with insurtechs, but the most interesting thing carriers can learn from new market entrants is, what’s different about their approach? As the Zen saying goes, in the beginner’s mind, there are many possibilities. In the expert’s mind, there are very few. New entrants into the market can help insurers avoid overlooking new ways to solve customer problems.

What would this enable us to do? (Innovation and Emerging Tech)

Emerging technology is full of possibilities, from AI to RPA to IoT to drones. The important question is, what could we do if we had access to this technology and its capability? Could we price better? Gather information faster? Create better customer experience? Framing the right question can drive better strategies for exploring innovation and emerging technology.

If we knew, what would we do? (Data and Analytics)

Everyone wants more data and insights, but few have a plan to act on those insights. Without an idea of what could be done better if more insights were available, and a plan for how to do that, insights are worthless.

How could this be easier and faster? (Digital)

Insurers have different definitions of digital strategy, but it really all boils down to this question – how could this be easier and faster? Better UX? More pre-fill? More predictive analytics? Simplified products? Simplified processes? Yes to all. And then do it again.

How will this create a foundation for evolution? (Core)

Core systems are still the ultimate foundation for speed to market and ease of doing business, and are critical in enabling analytics and insights. Insurers should consider whether their core systems are really up to the challenge of creating a foundation for evolution in product and process, or whether they will be an anchor to the past.

What is our realistic goal? (Security)

Perfect security is attainable – unplug the internet and all terminals and lock the server in a dark room. Becausse that’s not realistic, insurers should do a realistic assessment of risks, costs and impact on business effectiveness.

Why should anyone work here? (Talent)

This is a challenging question to face. Insurers talk a lot about talent availability, but they talk less about what’s compelling about the opportunity to work for their companies. Is the pay stellar? Is the environment stimulating? Does the role have an opportunity to create an impact? If not, your problem may not be the supply of talent.

See also: Where Are All Our Thought Leaders?  

How does this IT capability drive business results? (Alignment)

IT leaders often have a hard time translating the capabilities they enable into business results. But how can anyone expect business executives to invest in something they don’t understand the value of? Not being able to draw a line between tech capabilities and business results is fatal. Communication and understanding are critical skills for IT leaders.

Insurers really need to examine whether they are providing a distinctive value and a positive experience in every area both internal and external. Some of these questions are not comfortable to ask, or to answer. But having a realistic set of answers is the first step in developing an effective strategic plan.