As the COVID-19 pandemic continues, we’re learning a lot more about how insurance customers are being affected. We surveyed 6,000 U.S. consumers from May 22 to June 6, 2020 to get a snapshot of their attitudes about and experiences with insurance. Our survey uncovered the potential influence of COVID-19 on service preferences, loyalty to carriers and attitudes toward auto, homeowner, life, dental and vision coverage, as well as retirement.

As carriers navigate from their initial response to a longer-term strategy, they’ll likely need to adjust their approach based on shifts in consumer behavior. The takeaway? Financial stress correlates with dissatisfaction — but insurance carriers can improve customer satisfaction by introducing alternative pricing, bundling plans and, of course, upping their digital game.

Spotlight on consumers in this survey

• 55% do not feel financial stress right now
• 37% fear that the pandemic may cause financial impact to their retirement plan
• 15% anticipate they are likely to purchase life insurance due to COVID-19
• Source: PwC’s COVID-19 Consumer Insurance and Retirement Pulse Survey, June 2020: base of 6,000.

Consumers who faced challenges with their insurer

• 41% say they are likely or more likely to switch providers due to a lack of digital capabilities
• 15% identify lack of digital capabilities as the topmost challenge while interacting with insurers
• Source: PwC’s COVID-19 Consumer Insurance and Retirement Pulse Survey, June 2020: base of 657. Note: Percentage calculated as a subset of those who reported facing challenges with their insurer.

Budget strain tests customer loyalty in the insurance industry

As lockdowns took effect across the U.S., 45% of respondents reported financial stress. Job-security concerns — not only job loss, but also cuts in hours, pay and benefits — were the primary sources of stress. The most-cited concern (41%) was whether household income would return to previous levels.

See also: The Real Disruption of Insurance

More young households were under pressure: 73% of the 18- to 24-year-olds surveyed felt financial strain, compared with only 19% of those 65 and older. Three-quarters of respondents found federal relief helpful, at least in part. Among furloughed workers, 28% did not have a good understanding of their benefit status, including those in employer-sponsored life plans.

Tensions were running high in households with incomes of less than $50,000, where 59% felt they were under pressure. Within this group, 21% reported post-COVID-19 challenges in dealing with their insurers, half of whom were looking for more flexible billing and payment options. Four out of five (82%) in this group said they’d likely switch carriers as a result. Across insurance categories, 26% of auto and 18% of homeowner policyholders said new payment options should be standard practices as a result of COVID-19.

At all income levels, consumers indicated that they’re more willing to shop on price within the next 90 days, with levels ranging from 23% for respondents earning more than $200,000 to 34% for those in the $20,000 to $50,000 bracket. COVID-19 made price a top priority for 32% of consumers.

The top concern, among 25% of the auto policyholders, was that their decrease in driving would not be reflected in premiums. For life insurance policyholders, unsatisfactory coverage options or complex underwriting conditions were likely to prompt a potential change. Perhaps because they were mindful of the quick turn to remote staffing, consumers were less likely to drop their carrier based on the most common challenge: long call-center wait times.

Takeaways

Financial stress correlates with dissatisfaction, and it can lead customers to look for change. To avoid that, we recommend the following:

• Provide monthly billing options to help address policyholder cash-flow concerns.
• Explore forbearance or deferred payment options to help customers facing income disruption. Explain these accommodations and how customers can secure them. 
• Develop alternative pricing designed to help you retain customers. For example, consumers may be more willing to consider bundling policies or usage-based auto insurance programs, such as pay-as-you-drive GPS trackers and safe-driver telematics. Early adoption of COVID-19 rebates have contributed to high satisfaction among auto insurance customers and may have set expectations for price adjustments in other categories. 
• Consider ways to play in a bigger ecosystem — one that can meet an individual’s full financial wellness needs. Look beyond your own products and services. Consider offering more guidance and coaching to ease the worry of financial stress and help customers with the sometimes complicated trade-off decisions they need to make.

Life after COVID-19: Financial security needs grow stronger

Consumer concerns extend beyond immediate financial setbacks. COVID-19’s future impact is a top current concern: 40% of the stressed population said they’re anxious about both access to emergency funds and saving for college or other milestones, and 37% fear that the pandemic may affect their retirement plans. Many consumers worry that their workplace benefits will be cut or more coverage will be needed. Some said their experience with the pandemic has made them more open to considering life insurance (15% of respondents), supplemental health insurance (10%), disability insurance (9%) and critical-illness coverage (9%).

Takeaways

Annuities and cash-value life insurance products may be more attractive in a COVID-19 world, as older policyholders strive to preserve wealth and younger consumers seek assets to tap into in the event of a future crisis. However, the investment market volatility that makes these products attractive can also make it difficult for you to price them. We recommend you consider the following:

• Provide more conversion features without penalties that will enable customers to easily switch between products.
• Reexamine investment strategies to reduce risk and enhance payouts for longevity — creating options that pay out later — and retain cash value.
• Accelerate programs to simplify and expand access to life insurance, supplemental health, disability and critical-illness coverages.

Digital gets it done: Apps gain new acceptance in the insurance industry

Our survey indicates that as customers looked for alternate ways to update accounts, renew policies or resolve issues, online options came up short. Of those who expressed difficulties in dealing with their carriers during the crisis, 41% said they would be likely to switch providers due to a lack of digital capabilities.

When traditional channels shut down because of the pandemic, 19% of customers said they anticipated more interaction with their insurer through video chats with agents or chats via a website. They also planned to make more use of email and, especially for younger users, mobile apps.

Young consumers were most vocal about expecting digital options. Among the 18- to 24-year-olds surveyed, 53% said they were likely to use digital channels to engage with their insurers within the next 90 days, 49% were likely to purchase usage-based insurance and 49% were likely to shop around to save money on insurance.

See also: Why Traditional Insurance Won’t Work

Takeaways

Having digital capabilities has emerged as a differentiator as insurance customers are increasingly expecting to conduct business digitally. We recommend that you consider:

• Accelerating your digital development now that customers have broken old habits to embrace online and mobile channels.
• Leveraging technology to automate claims processing.
• Strengthening your self-service capabilities, which may enhance customer satisfaction and reduce operational load.

The pandemic’s early uncertainties have given way to both setbacks and a chance to operate differently. Insurers that examine customer pain points and life changes and accelerate their digital adoption have an opportunity to gain share and build loyalty.

We would like to acknowledge Anshu Goel and Susmitha Kakumani for their contributions to this article.

The insurance industry in the UAE is relatively young. The oldest insurance company in the country is less than 50 years old. The Insurance Authority, the regulatory body, was established as recently as 2007 to protect the interest of consumers.

The industry is going through rapid change. By the end of this decade, personalized insurance covers will replace the one-size-fits-all products currently available. Most of this change is a result of the consumer shift toward digital channels.

E-commerce in the UAE is booming. It is currently at more than $16 billion a year and is expected to grow 23% annually for the next couple of years. This shift has opened the doors to digital distribution for the insurance companies.

Consequently, sales through the digital channel on web platforms run by brokers, insurers and aggregators are growing by leaps and bounds. In the UAE, about five years ago, online car insurance sales accounted for less that 1% of the total motor insurance. Today, the channel contributes about 5% to 7% of the motor insurance market.

In countries such as the U.S. and India, online platforms have already become a preferred channel for purchasing both life insurance and general insurance products. According to a PWC report, 47% preferred buying insurance through one or another digital mode in India.

The COVID acceleration

The COVID-19 pandemic is an inflection point for the insurance industry in several ways. It has put life and health insurance front and center in the minds of people all over the world. The pandemic has made digitization an almost necessary condition for survival for the insurance industry. With restrictions on travel and the fears associated with even intercity mobility, the online sales channel has become paramount for insurers.

As the UAE marches toward digitization, there are some speed breakers. Despite the adoption of insurtech, there is still the need for some amount of manual paperwork during insurance purchases. For example, medical tests and policy issuance still require offline paperwork. To become truly digital, insurers need to invest more in technology.

Bumps on the road

While internet penetration in UAE is among the highest in the world, the UAE has an insurance penetration of just 1.9%; average global penetration is 6.1%. The insurance industry in the UAE is expected to grow at a compound annual growth rate of 4.2% between 2019 and 2024.

I believe that greater consumer awareness and tailored products could be the game-changers in the long term. In the shorter term, we need to accelerate digitization -- in particular, in the post-transaction phase to allow for instant issuance of the policy.

Quickly building and marketing a strong digital infrastructure is a challenge faced by many distributors. As the industry grapples with this challenge, we also need to re-engineer our operations so they can be run remotely, free from the limitations of confined office space.

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

What the future holds

Once the changes and innovations become widespread among insurers and distributors, consumers will start benefitting immensely. New, improved and custom-made insurance products to suit the various consumer life stages and financial goals would provide optimum protection against the uncertainties of life. The whole process of buying insurance would shift to digital mode -- from telemedical or video-based medicals examinations to digital fulfillment processes. Premiums will go down thanks to cost-efficient distribution channels.

As the industry moves toward an automated, technology-based marketplace, a plethora of opportunities will arise for progressive insurers and distributors to gain market share. The industry would have more data to assess and analyze individual risk factors, while distributors will have more efficient means to communicate with customers. The insurance industry in general will be able to provide a vastly superior consumer experience.

While the processing of inbound and outbound payments in insurance isn't exactly known for its speed, there are significant advancements being made by both insurers and solution providers to hasten delivery of payments and expand payment options.

A digital interaction actually happens at the speed of light – it is immediate and, being digitally based, can be controlled precisely. Insurers are now looking at sending invoices to their policyholders in the method of the policyholder’s choosing – email, SMS or even a digital wallet. The consumer can now start to change the method of payment in real time.

Consumers also want their money delivered at the speed of light, but outbound payments are still generally made by check.

Recently, SMA held an Insights to Solutions virtual event that focused on the transformation underway in the Digital Payments space. Of the insurers registered for the event, a resounding 83% stated that improved customer experience is a key motivator as they look to adopt digital payments. 67% of registrants envisioned digital payments as a key element of their digital strategy and road map.

Many of the changes needed to transform the outbound experience were also profiled at the event. The shift to digital payments requires an adaptation of people, processes and technology to make the experience successful for both the sender of the funds as well as the receiver.

At the event, we explored the impact of COVID-19 on payments. No one wants to send someone into the office just to print a check. 64% of the registrants were looking to reduce their reliance on paper checks, and 53% were seeking to reduce internal processing expenses.

The interest highlighted a focus on operational efficiency that includes offering the claimants options on an array of payment methods that are “best-fit” based on circumstances – the line of business, amount, etc.  

See also: How Claims Process Must Drive Change

Even for those who pride themselves on being high-touch, an interaction with a person does not always best serve a customer. At times, the speed and efficiency of a digital process – including immediate payments – are better. 

COVID-19 has accelerated many digital payments initiative, because advancements are needed now. There was always a need. Now there is also urgency.

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View this email in your browser | Forward to a colleague Elon Musk and Your Feedback Loop Paul Carroll, Editor-in-Chief of ITL Although I sometimes can’t decide whether Elon Musk is the business genius of our time or is two bricks shy of a load, he sure does get a lot of key principles right. The latest instance is a little-noticed announcement last week about how he is using Tesla’s auto insurance offering to create a feedback loop to help him make better cars. When an accident occurs, his designers learn immediately through the insurance arm what happened and can consider whether some modification to the car would reduce the damage or at least lower the cost of the repair. Customers will become less likely to wonder, “That fender-bender cost how much to fix?” Word-of-mouth on the cars will improve, leading to more sales, creating more data via the insurance arm, allowing for more design improvements and so on, pumping ever more money into Musk’s pockets. While emulating Musk won’t mean that you, too, can land spent rocket stages upright on floating platforms, insurers have a number of opportunities to create feedback loops and virtuous circles that could let them dominate part of the industry... continue reading > CLM CE Tracker Tracking and renewing adjuster licenses doesn’t have to be an administrative nightmare and time burden. Untangle it with CLM Tracker. Learn More SIX THINGS ‘Law of Computability’ Powers the Bionic Era by John Sviokla The bionic era automates symbolic work – perceiving and judging – and blends powerfully with the industrial era's automation of physical work. Read More COVID-19: Technology, Investment, Innovation by Stephen Applebaum There are two extremely different states existing within the insurance ecosystem: larger, well-funded participants and then all the rest. Read More Graph Theory, Network Analysis Aid Actuaries by Ankur Jain Graph and network analysis helps organizations gain a deep understanding of their data flows, process roadblocks and other trends and patterns. Read More Winning With Smart IoT in P&C Brett Jurgens, CEO and co-founder What if I told you that insurers could attract customers with smart home devices that generate interaction seven to 10 times A DAY? Learn More Voice Is the Future – Even for Insurance? by Robin Kiera Wouldn’t it be good to be among those present at the start of voice-activated assistants, a technology of the future, and gain market share? Read More The Most Underused Channel for Leads by Nick Hedges One advantage many captive insurance carriers overlook is tied to what may seem like a disadvantage— consumer preference for online research. Read More 3 ‘Must Have’ Digital Investments by Deb Smallwood Transformation has advanced five years in eight weeks, and P&C insurers need to keep up with digital platforms, payments and communications. Read More GET INVOLVED Write for Us Our authors are what set Insurance Thought Leadership apart. Get Started Partner with Us We’d love to talk to you about how we can improve your marketing ROI. Learn More SPREAD THE WORD Share Share Tweet SUBSCRIBE TO SIX THINGS

Managing business risk is a tricky thing. With an appetite too small, opportunity could be lost, but taking on too much risk could hurt profit and performance. 

Companies that are not thinking about risk are at risk!

Making the move to proactive risk management requires a culture shift, but 65% of organizations say they’re still operating with “reactive” or “basic” risk management response. Mature companies often take a strategic and calculated approach to risk management. Considering that risk = probability of occurrence x severity or consequence, mathematic analyses can help organizations avoid preventable pitfalls. Risk modeling using advanced statistical techniques has developed to align theoretical risk with real-world events and provides C-suite decision makers with quantifiable support needed to make data-informed decisions.

A Five-Step Approach to Data-Driven Risk Management

Where do smart companies start when they want to begin addressing risk? Data. 

To understand risk beyond “gut feelings” and anecdotal evidence, companies need to leverage the information that is available to them – especially in today’s data-saturated environment. These five steps can outline your path to data-informed risk management.

• Step 1: Collect your data. Often the most difficult step, identifying the right data to inform your analysis, is critical. We all know that “data is out there,” but not all data is created equal. For best results, explore different dataset options, take the time to understand how this data was collected and then clean data to ensure any risk analysis is both relevant and actionable.
• Step 2: Develop a risk model. Risk modeling allows teams to include contextually relevant predictors and relationships. If historical data exists for current risks, create an empirical model to articulate key predictors. If analysis focuses on emerging risks where no data exists, craft a theoretical risk model based on the relationships you do know.
• Step 3: Explore differing scenarios. There are probably a few risk scenarios that keep you up at night. Use your model to understand the likelihood and loss of these potential events. Estimate losses for each scenario in a metric that’s meaningful to your audience. Money? Time? Human capital?
• Step 4: Share your findings. Now it’s time to tell your story. This is where data geeks sometimes “lose their audience.” Your analysis is ineffective if decision makers do not understand the implications.  Share your findings in a way that is meaningful using relevant metrics, data visualization and scenario storytelling. In practice, this means avoiding abstract metrics in favor of direct impacts — such as potential revenue loss or downtime — and possibly using infographics to support cause and effect narratives. Connect the dots between risk and results with a relevant story that ends with actionable advice.
• Step 5: Enable action. As Theodore Roosevelt once implied, sharing a problem without proposing a solution is called whining. Once you’ve presented your model and your findings, you will likely have an understanding of the leading risk factors. Let these factors inform your recommendations for risk mitigation. This will help decision makers prioritize their resources for maximum impact. 

Sometimes, data isn’t enough

Not surprisingly however, data isn’t always enough to instigate change. As anyone who’s listened to the news lately knows, data can be manipulated and interpreted in different ways. Sometimes, we see what we want to see - it’s in our psychology - and the C-suite is not immune to this. To be human is to be biased. 

Therefore, communicating risk with data is a strong technique for neutralizing the effects of human biases, but one should be aware of common predispositions that often arise when people assess risk.  

See also: Claims and Effective Risk Management

To Be Human Is to Be Biased

The famous psychologist Daniel Kahneman highlighted the fallibility of human cognition in his work to discover inherent human biases. These biases evolved over millennia as coping mechanisms for the complex world around us, but today they sometimes impede our ability to reason. The challenge is that many of us are not aware of these biases and therefore unknowingly fall victim to their influence.

"We can be blind to the obvious, and we are also blind to our blindness." – Daniel Kahneman 

There are a few important biases to be aware of when presenting your risk analysis and recommendations. 

• Conservatism bias: People are comfortable with what they know, and we show preference toward existing information over new data. As a result, if new data emerges suggesting increased risk, an audience may resist this new information simply because it’s new. 
• The ostrich effect: No one likes bad news. When it comes to risk, people tend to ignore dangerous or negative information by “burying” their heads in the sand like an ostrich. But just ignoring the data doesn’t make the risk go away. A strong culture of risk management will help negate this effect. 
• Survivorship bias: Biases can work toward unsupported risk tolerance, as well. With survivorship bias, people only focus on “surviving” events and ignore non-surviving events (or those events that did not actually occur). For instance, a company’s safety data may show a lack of head injuries (surviving event), and decision makers may believe there is no need for hard hats. 

Communicating risk with data is an excellent start toward shifting your work culture to one of predictive risk management, but we cannot forget the human element. As you share your models, data and findings, remember to address potential biases of your audience… even if your audience is unaware of their own human susceptibility!

Although I sometimes can't decide whether Elon Musk is the business genius of our time or is two bricks shy of a load, he sure does get a lot of key principles right.

The latest instance is a little-noticed announcement last week about how he's using Tesla's auto insurance offering to create a feedback loop to help him make better cars. When an accident occurs, his designers learn immediately through the insurance arm what happened and can consider whether some modification to the car would reduce the damage or at least lower the cost of the repair. Customers will become less likely to gasp, "That fender-bender cost how much to fix?" Word-of-mouth on the cars will improve, leading to more sales, creating more data via the insurance arm, allowing for more design improvements and so on, pumping ever more money into Musk's pockets.

While emulating Musk won't mean that you, too, can send astronauts to the space station, insurers have a number of opportunities to create feedback loops and virtuous circles that could let them dominate a major part of the industry.

Companies have long tried to incorporate feedback, largely by having customer service reps' interactions interpreted in ways that can guide product design teams to fix problems or to understand customer needs that hadn't previously been articulated. Over the years, we've published articles about innovations such as improved speech-to-text translation, which let companies plumb customer calls for key phrases that could lead to insight. Just last week, we published a piece on how companies should track every time they say "no" to a customer, to see if a "yes" might be possible and lead to innovation in products or services.

What's different -- and what creates major opportunities for insurers -- are the newly available speed and specificity of insight, which can create such a tight feedback loop that the power increases over time and can lead to an insuperable advantage.

Technology companies dramatize the power of the right feedback loop. Facebook so dominates social media that it sees a huge percentage of the interactions and can mine them to see which ads work and which don't, which algorithms generate the most interactions in people's news feeds, etc. Facebook feeds its knowledge back into product design, and its competitive edge keeps growing (even though Washington is finally showing some antitrust concerns). The same holds true for Google's search engine: When Microsoft announced years ago that it was pouring unlimited resources into its Bing search engine, to take down Google's search engine, I was sure Google had nothing to fear even from a powerhouse like Microsoft. Google was seeing two-thirds of the searches, so it was learning and improving far faster than could Microsoft, which was seeing maybe one-fifth of the searches. Google Maps had the same sort of feedback edge over Apple Maps. Amazon, as the marketplace for so many goods, sees what works and what doesn't in exceptionally fine detail -- by color, by size, by time of year, by slight variation in price, etc. It's actually facing antitrust scrutiny because competitors claim Amazon uses the information to decide which products to start making on its own and enters markets with an unfair advantage.

Because insurance isn't nearly as digital as Facebook, Google or Amazon, the feedback loops will take longer to build, but they're still possible.

I'm especially optimistic about claims. As the process is being digitized, particularly with auto, there seems to me to be a great opportunity for some independent company to become good enough that it will achieve critical mass. At the moment, real progress is being made, as those involved in an accident take their own pictures, as artificial intelligence offers on-the-spot repair estimates and as coordination with the body shop at least begins digitally. But imagine what might happen if one competitor got its nose far enough ahead of others. That competitor would see so many of the claims that it could learn faster than others about just which pictures matter and how they should be taken, could finetune the AI to offer much more accurate estimates of damage (addressing what seems to be a source of many complaints at the moment) and could generally smooth the process between accident and repair by continually spotting and removing friction points. Then that company would become even more popular, giving it access to more feedback... and away we go.

Wouldn't you like to be that company?

Not every aspect of insurance lends itself to tight feedback loops. When you underwrite a batch of life insurance policies, for instance, you don't get your feedback for years or even decades on how accurate you were.

But just about anything that can be digitized allows for the kind of fast feedback that could produce a dominant information position.

Distribution is becoming digital enough in these pandemic times that at least pieces of the process could be optimized through instant feedback from agents, carriers and customers about where the pain points are. The opportunity is especially large with independent agents because, no matter how big a captive sales force is, it won't have the same scale as the universe of independents does, and information advantages are most powerful at scale (see, Facebook, Google and Amazon).

Business process outsourcing, buttressed by AI and robotic process automation, could be another opportunity for an information advantage from a tight feedback loop. That opportunity may be too immature still because, in general, if you've seen one business process at a company you've seen one business process at a company. There will likely need to be more more in common among processes before a company could swoop in and win the whole opportunity.

RiskGenius has always intrigued me as an information play, because of its AI that searches through policies to spot changes, compares clauses against similar ones in other policies, etc. If RiskGenius gets to critical mass in the number of policies in its systems....

There are surely other possible feedback loops, too, and if those of us in the insurance industry don't spot them then others surely will. Always remember what Amazon founder and CEO Jeff Bezos says: "Your profits are my opportunity."

Stay safe.

Paul

P.S. Here are the six articles I'd like to highlight from the past week:

‘Law of Computability’ Powers the Bionic Era

The bionic era automates symbolic work – perceiving and judging – and blends powerfully with the industrial era's automation of physical work.

COVID-19: Technology, Investment, Innovation

There are two extremely different states existing within the insurance ecosystem: larger, well-funded participants and then all the rest.

Graph Theory, Network Analysis Aid Actuaries

Graph and network analysis helps organizations gain a deep understanding of their data flows, process roadblocks and other trends and patterns.

Voice Is the Future – Even for Insurance?

Wouldn’t it be good to be among those present at the start of voice-activated assistants, a technology of the future, and gain market share?

The Most Underused Channel for Leads

One advantage many captive insurance carriers overlook is tied to what may seem like a disadvantage— consumer preference for online research.

3 ‘Must Have’ Digital Investments

Transformation has advanced five years in eight weeks, and P&C insurers need to keep up with digital platforms, payments and communications.

Most traditional insurers find it overwhelming to transform the innumerable sensitive actuarial processes needed for day-to-day functioning. This problem is amplified by most insurance actuaries spending most of their time on secondary activities, such as data reconciliation, rather than focusing on core actuarial tasks such as modeling, strategy development and root cause analysis. These secondary activities are usually low-value, repeatable and time-consuming tasks. 

It’s crucial to understand that, unlike other insurance processes, actuarial processes are complex and time-consuming and have a high number of touchpoints. Dynamic, frequently changing regulations can make these processes even more complicated.

For instance, the New York Department of Financial Services (NYDFS) published its Circular Letter Number 1 in 2019 on the use of big data in underwriting life insurance. The NYDFS states that “an insurer should not use external data sources, algorithms or predictive models in underwriting or rating unless the insurer has determined that the processes do not collect or utilize prohibited criteria and that the use of the external data sources, algorithms or predictive models are not unfairly discriminatory.”

This presents a need for full transparency to explain the variables computed and their effects, as well as a need for efficiency so that actuaries spend their time on analysis rather than data reconciliation. Other priorities will depend on the processes. For example, pricing and ALM modeling processes require greater flexibility and transparency, whereas valuation and economic projection models require more precision and prioritize governance over flexibility and transparency.

Irrespective of the modeling processes, legacy source systems, fragmented data, error-prone manual processes and a lack of data standardization lead to problems within actuarial organizations. Analyzing actuarial processes is quite complex due to the interdependencies and relationships of subtasks and files. With advancements in the field of artificial intelligence (AI) and machine learning (ML), copious amounts of data can be processed quite efficiently to identify hidden patterns. Network analysis is widely used in other domains to analyze different elements of a network. Within insurance, it can be applied for fraud detection and marketing. This paper describes an approach where network analysis is leveraged for actuarial process transformation. 

A Coming Science: Graphs and Network Analysis

Graph and network analysis helps organizations gain a deep understanding of their data flows, process roadblocks and other trends and patterns. The first step for graph and network analysis involves using tools to develop visual representations of data to better understand the data. The next step consists of acting on this data, typically by carefully analyzing graph network parameters such as centrality, traversal and cycles.

A graph is a data structure used to show pairwise relationships between entities. It consists of a set of vertices (V) and a set of edges (E). The vertices of a graph represent entities, such as persons, items and files, and the edges represent relationships among vertices. 

Graphs can be directed or undirected. An undirected graph (Figure 1) is where there is a symmetric relationship between nodes (A to B implies B to A), whereas a directed graph (Figure 2) is asymmetric. In the case of process improvements, the dependencies of one task or file with the others in the process need to be modeled. The relationship is asymmetric, and therefore should be modeled through a directed graph. 

See also: Big Changes Coming for Workers’ Comp

Network Analysis Basics and Process Improvements

Graphs provide a better way of dealing with the dependencies in the various data files, data systems and processes. Once any process is represented as a graph, there are multiple operations and analyses that can be performed. For instance, influencer nodes can be easily identified using centrality measures. Similarly, cycles, cliques and paths can be traced along the network to optimize flow. Network analysis helps assess the current state of processes to identify gaps or redundancies and determine which processes provide maximum value. 

Three key analyses are the most important in any process improvement framework:

1. Identifying process and data nodes that are crucial in the network 
2. Tracing from the input to the output in the processes to identify touchpoints
3. Identifying cyclical references and dependencies in the network and making the flow linear

1. Influential Nodes: Centrality

Centrality measures the influence of a node in a network. As a node’s influence can be viewed differently, the right choice of centrality measures will depend on the problem statement. 

• Degree Centrality: Degree centrality measures influence based on the number of incoming and outgoing connections of a node. For a directed network, this can be further broken down into in-degree centrality for incoming connections, and out-degree centrality for outgoing connections.
• Between-ness Centrality: Between-ness centrality measures the influence of a node over the information flow of a network. It assumes that the information flows through the shortest path and captures the number of times a particular node appears in that path. 

These different centrality measures can be used to derive insights about a network. While degree centrality defines strength as the number of neighbors, between-ness centrality defines strength as control over information passing between other neighbors through the node. Nodes that are high in both degrees are the influential nodes in the network. 

2. Graph traversal

Graph traversals are used to understand the flow within the network. They are used to search for nodes within a network by passing through each of the nodes of the graph. Traversals can be made to identify the shortest path or to search for connected vertices in a graph. The latter is of particular importance for making actuarial process improvements. Understanding the path of data throughout the process can help evaluate the process holistically and identify improvement opportunities.

3. Cliques and Cycles

A clique is a set of vertices in an undirected graph where every two distinct vertices are connected to each other. Cliques are used to find communities in a network and have varied applications in social network analysis, bioinformatics and other areas. For process improvement, cliques find an application in identifying local communities of processes and data. For directed graphs, finding cycles are of great importance in process improvement, as insights mined from investigating cyclical dependencies can be quite useful. 

Step Approach for an Actuarial Transformation Using Graph Theory

1. Understanding the Scope of Transformation

Understanding the scope of transformation is of key importance. The number of output touchpoints and files used by the organization is often significantly less than the number of files produced. Moreover, due to evolving regulations, actuarial processes can undergo changes. Some of the key questions to answer at this stage include: 

• Which processes are in the scope of the transformation?
• Will these processes undergo changes in the near future due to regulations (US GAAP LDTI/IFRS 17)? 
• Are all the tasks and files for the chosen process actually required, or is there a scope for rationalization?

2. Understanding Data Flow

Once the scope of the transformation is defined, data dependencies need to be traced. Excel links, database queries and existing data models need to be analyzed. In some cases, manually copying and pasting the data creates breaks in the data flow. In such cases, the analyst needs to fill in the gaps and create the end-to-end flow of the data. Some key aspects to consider at this stage are: 

• What are the data dependencies in the process?
• Are there breaks in the data flow due to manual adjustment?
• What are the inputs, outputs and intermediate files? 

3. Implementing the Network of Files

After mapping the data flow, the graph network can be constructed. The network can then be analyzed to identify potential opportunities, identify key files, make data flows linear and create the goal state for the process. The key analysis to perform at this stage are:

• Identifying important nodes in the network through degree measures
• Capturing redundant intermedia files in the system
• Capturing cyclical-references and patterns in the process

Based on the analysis of the network, bottlenecks and inconsistencies can be easily identified. This information can lead to process reengineering and end-to-end data-based process transformation. The results can be validated with business users, and changes can be made. The figures below show some of the patterns that can be captured using network graphs. The input, intermediate and output nodes are color-coded as blue, grey and red respectively.  

The Benefits of Actuarial Process Transformation Using Graph Theory

Due to the inherent complexity of actuarial processes, decomposing process and data flows can be difficult. While analyzing any actuarial sub-process at the lowest level of granularity, it is quite possible to discover multiple related files with lots of related calculations. Moreover, a major challenge quite common in actuarial processes is a lack of data documentation. Graph theory enables insurers to overcome these challenges: 

• Creating a Data Lineage From Source System to Output: Graph networks help improve the quality of data feeding into subsequent sub-processes. This benefits actuaries, as higher-quality data produces better models regardless of the techniques being employed
• Improved Visualization and Bottleneck Identification: Graph networks help visualize the relationship between various databases. The networks also help build a foundation for a data factory that not only creates a 360-degree view of useful information, enables data visualization and enables future self-service analytics. Moreover, several analyses can identify process bottlenecks that can be investigated further.
• Enabling Flexibility and Governance: On the surface, flexibility and governance may sound like competing priorities. Increased flexibility makes it difficult to control what is happening in the process and leads to increased security risks. However, graph theory helps manage governance by visualizing complicated data relationships and helps in maintaining data integrity. 
• Speed of Analysis: Traditionally, most of the time spent producing models is used to gather, clean and manipulate data. Graph theory helps in driving dependencies, enabling efficient processes and providing quicker results for a given problem. Graph theory can be used to rationalize non-value-adding files or processes, leading to streamlined and automated process flows. By linking the data elements from outputs to source systems, organizations can analyze processes in depth through back propagation. 

Case Example

A major life insurance player in the U.S. engaged EXL to examine its annuities valuation process and identify process improvement opportunities. There were multiple interfaces in the annuities valuation process, and many stakeholders were involved. Regulatory frameworks, a high number of touchpoints, actuarial judgment and manual adjustments made the annuities valuation process complex. Moreover, the client had multiple source systems from which data were pulled. Data came to the actuarial team through SQL servers, data warehouses, Excel, Access databases and flat files. As a result of the data fragmentation, a significant amount of effort was spent on data reconciliation, data validation and data pulls. While some aspects of these steps were automated, many of the processes were manually intensive, wasting actuarial bandwidth. 

EXL deployed a two-speed approach, tackling the problem from a short-term local optimization as well as from a long-term process improvement perspective. The local optimization approach focused on understanding the standard operating procedures for the individual tasks to automate the manual efforts. These optimizations generated quick wins but did not address the overall efficiency and improvement goals per se. 

See also: The Data Journey Into the New Normal

Knowing that there was a possibility of finding multiple tasks that can be rationalized, EXL prioritized and balanced the local and long-term improvements. This included speaking to multiple stakeholders to identify the regulatory GAAP processes for deferred annuities that needed to be focused on in the long term, and what the other processes could be addressed through local optimization. 

For the deferred annuities GAAP process, EXL leveraged network analysis to analyze the file dependencies. Each of the hundreds of process files and tasks were categorized into pure inputs, outputs and intermediates. These files were modeled as nodes in the network, while the data flows were modeled as edges. To capture the data linkages, a Visual Basic Macro (VBM)-based tool was deployed that automatically identified the Excel links and formulae to capture dependencies. Centrality measures were calculated for each of the files and then attached to the node attributes. The centrality measures showed important sub-processes and communities of files. For example, the topside sub-processes ingested more than 20 files and were high on degree centrality. Annual reporting sub-processes were high on degree centrality. 

The team also found 11 avoidable cyclical references for data flows. These data flows were made linear to create the goal process state. Moreover, it was also observed that some of the intermediate files were merely being used to stage the data. These files had basic data checks embedded but did not add a lot of value. These files were rationalized. Network analysis helped in providing an understanding of the data flows and creating the to-be state for process improvement. Moreover, the time required to analyze hundreds of tasks and files was reduced significantly. The team was able to identify an over 30% reduction in effort through a combination of automation and data-based solutions.

The news is filled with stories about advanced applications of technology, from the Internet of Things, satellite data, sensors and drones, to augmented reality, artificial intelligence, neural networks and machine learning. Each of these applications comes with its own body of research, corporate promoters and analyst predictions, making them seem unrelated. They aren’t. We are in a new era, which I call the Bionic Era, and it’s as powerful and pervasive as the industrial era before it. In the bionic era, we have a new blend of people and machines both doing and thinking together. Where the industrial era automated physical work, the bionic era automates symbolic work – thinking, perceiving and judging, blending with the advances of the industrial era in a dynamic and powerful way.

This bionic era changes the nature of economic returns and the very forms of capital that underpin our economy.

Modern life is so replete with the dance among people and our machines (cars, dishwashers, phones, etc.) that to observe their dynamic co- habitation is a bold assertion of the obvious. Yet, in the bionic era, which parts of life – especially which types of thinking and decision making are susceptible to computability – and when, is a tricky and important question to answer. Answering this query can give insight into questions like: Is my job safe? Will my company be put out of business? Which military powers will have the upper hand in the future?

The concept of computability can help us navigate.

What is computability?

A task is computable if it is highly digitized and there is a high level of knowledge about it, a relationship I refer to as the Law of Computability:

Law of Computability (LoC): Computability = Degree of Digitization of the Phenomenon * Level of Knowledge of the Phenomenon

I use the term "computability" instead of "automation" because with physical automation there is no digital description of the task. A washing machine automates the most labor- intensive part of doing laundry, but making it computable would require a digital twin of the washing machine to drive the operation and record details about all the elements that affect it, such as water, temperature, soap, agitation speed, the size of the motor, etc.

For a specific example of how the general phenomenon of the Law of Computability applies in the present day, considering the rapid advance of the self-driving car. Engineers believed they had a high degree of knowledge about the task of driving when Google set out to design a commercially viable self-driving vehicle, according to Chris Urmson, the first lead engineer for the Google car project. Engineers had deep understanding about the physics of car movement, location, function and behavior.

Early self-driving prototypes were also highly digitized, with multiple inputs including GPS, digital maps, onboard sensor equipment and massive amounts of computer processing power. Total cost for all that hardware: $250,000. Yet that combination of digitization and knowledge still resulted in a three-foot margin of error -- far too large for safety. Eventually, Urmson’s team realized that, while they understood how the car works and had extensive digital information about it, the environment through which the car drives is not equally digitized. Nor did they know as much as they needed to about it because environments are also always changing (one minute the cross-walk has a kid in it, the next it doesn’t).

Google engineers filled that gap by adding LiDAR laser surveying technology to the top of the car. The LiDAR spins around and collects over 1.5 million data points per second. By adding this tool to digitize the description of the environment in real time, Google was able to compute a dynamic, three-dimensional model through which the car drives. The addition of the LiDAR enabled engineers to deepen their knowledge of the driving environment to model pedestrians, cyclists, police officers, dogs, children and the millions and billions of situations and objects – living and otherwise — within it.

As driving becomes more computable, the change not only affects the sources of profit and power in the auto industry and related industries; it shifts the traditional definitions and buying behaviors of the entire sector and parallel sectors it touches, including insurance, repair shops, road construction firms, toll systems, parking, municipalities earning revenue from traffic tickets, public transportation, taxi and ride-sharing services and others. The folks at GM talk about having a market in transportation demand dynamically matched to transportation supply – including everything from owned automobiles, to shared bikes, to self-driving Uber vehicles.

How much do we have to “know” to render a task computable?

Different industries operate in wildly different contexts when it comes to how close we are to computing the underlying tasks that drive them. Think, predictive maintenance on a washing machine (high knowledge) vs. why cancer forms in any individual human body (low knowledge). The first task is largely computable; the second is not even close.

My friend Roger Bohn defined seven stages in his iconic knowledge framework. For the context of computability, we care about only three: Description, Correlation and Causation. The ability to describe a task is the baseline requirement to begin rendering it computable. When knowledge has deepened to the point of understanding correlation, we know enough about a task to understand the likely elements involved or affected. When knowledge has progressed to causation, we understand fully how it works.

See also: Will COVID-19 Be Digital Tipping Point?

In the context of the self-driving car, the LiDAR completed the necessary description. That allowed companies like Google, GM and Ford (not to leave out Tesla, which is following a parallel path with a different technological approach) to build fully working models and put them first on test tracks and then on real roads to run them through driving scenarios and gather data to analyze and deepen what we know. As Ray Kurzweil has pointed out, technology and knowledge have a positive feedback loop, so once a new technology is operating, learning and change happens faster. We see that with autonomous vehicles – the entire fleet learns together. The self-driving car is on the fast track now between description and correlation.

Computability Changes the Relationships Between Humans and Machines

Remembering that the bionic era is about a new mix of humans and machines, let’s explore how computability changes that blend. The vast digitization of the world is helping to simultaneously apply known techniques in new ways and find new knowledge to progress our understanding. For example, my dear friend John Henderson once tagged every single asset in the South Shore hospital – doctors, patients, ultrasound machines, etc. This created a digital library of all assets and their status. (In the language of the law of computability, it increased the level of digitization of the phenomenon.) With this new level of digitization in hand, his team was then able to apply existing operations research knowledge on scheduling, queuing theory, etc., to optimize the use of those assets – increasing throughput of the operating suites by 25%, which is a huge indicator of increased operational efficiency. In the language of the LoC, increased digitization unlocked the power of the knowledge of the phenomenon of interest.

Some pundits have said, if you can write down the function of your job, step by step, then it can be automated. I think that’s only partially right. For example, you can write down the task of caring for an Alzheimer’s patient with a step-by-step process, but we cannot yet render it computable, for many reasons. The robots cannot handle the complex and dynamic environment of person-to-person care. The roles of emotion, empathy and human understanding have a massive impact on wellness of patients, and we don’t yet know how and when humans might “feel” the same way about machines. So, that task is far from being computable – even though the programmatic articulation of steps can be done.

Using the Law of Computability: It’s all linking up or discovery

The twin challenges of any business are to understand how to digitize enough to use existing knowledge to create value, and how to create new, practical knowledge faster. For example, in a recent hurricane in Puerto Rico, a PwC team attached low-power WiFi sensors on tanks of diesel fuel running the generators powering the pumps that drove the water supply. This simple digitization enabled a whole new level of performance and confidence in the emergency water system. In a different example, Climate Corp., which sells crop insurance, used a combination of satellite, sensor and publicly available weather data to create a more accurate growth model for corn and other crops. They are so confident in their level of knowledge of the phenomenon of interest that they pay claims based on their model, without ever visiting the affected field. Again, the computability of the tasks changes the very nature and economics of the firm’s operations.

These questions apply not only to manufacturing sectors or service industries, like retail insurance, already far along their computability evolution, but also to knowledge-intensive industries that run on specialized human skills that many believe – sometimes falsely – are not imminently computable. Consider how this applies in digital retail. Before the digital age, customized shopping recommendations were so labor-intensive that they were only really provided by luxury brands offering dedicated, concierge-like services. Today, the data trail of browsing histories and digitally captured transaction details allow almost any digitally enabled retailer to develop a profile of a customer’s buying behavior, payment methods, shipping locations, etc. Amazon is the undisputed leader in this space in the West because the platform it has built for selling everything from books to vitamin supplements has allowed it to be both high scale and high scope — in other words, Amazon knows both a lot about you and a lot of different facts. It took many years and many billions of dollars spent to capture customers, build its business and technological platforms and develop robust analytics capabilities — in short, Amazon has higher fixed costs for making recommendations than, say, Walmart. But the marginal cost of making any given shopping recommendation is now at or near zero. And the transparent volume of customers and customer opinions on an infinite range of products drives still more traffic to its properties, as customers use Amazon not only to shop but to define their consideration set.

As this example also makes clear, the likely path for many knowledge-intensive industries is that the companies that create a dominant platform will thrive, and others will either barely hang on, or go out of business. The dominant platform(s) will have a more capital-intensive base, but excellent marginal and total economics, which will give them the capital necessary to continue to improve their technology to expand the distance between them and their next-closest competitor. They will also be able to skim the market on talent because technical expertise gravitates toward the leader.

How soon will computability change industry dynamics?

The law of computability helps answer what will be computable. The question of when, however, hinges not just on computability but on the competitive dynamics of a given industry and its sources of economic value. Technological progress is a dance between the possibilities of science and engineering, and the ambition of individual actors within businesses and government. Without the shock that came on Oct. 4, 1957, when the Soviet Union put Sputnik, the first human-made object, into orbit around the earth, John F. Kennedy would never have committed America to the moon project. There’s a Sputnik moment on computability coming in every company’s future. If one of the lead companies in an industry dives in and creates a solution, others will follow. The critical question for executives is, can you afford to be second?

See also: How Machine Learning Halts Data Breaches

Technologies are often over hyped early and under-appreciated later. When the iPhone was introduced on June 29, 2007, few people would have predicted the complete reconfiguration of where consumers spend their time, how people communicate and where people shop. In only 10 years, the entire consumer experience for billions of people radically shifted.

Some firms want to lead this revolution so they can be on the right end of the economic power curve that computability can deliver. Some, like Goldman Sachs, are already aiming to compute at least 10% of what their well-paid staff does today. GE is building digital twins of its industrial machines because it wants to drive productivity and gain market power. Others will need to respond if these business-to-business leaders have the same market power that the consumer companies like Facebook, Amazon, Alibaba and others have had in the consumer market. Those firms that can combine knowledge of their tasks and industries with deep digitization can lead the way in computability – and thereby garner competitive advantage that will be hard to overcome.

The COVID-19 pandemic has caused turmoil for insurance. To aid consumers as unemployment and uncertainty spiked, state regulators around the country issued emergency protection measures to extend grace periods for premium nonpayments, prohibit policy cancellations during states of emergency, extend premium repayment timelines and offer leniency to insured individuals hit by COVID-19, among other changes.

These emergency insurance industry regulations are complex — plus, protections vary widely by state and must be implemented rapidly. It’s no wonder insurers are having a tough time navigating insurance laws and regulations during the pandemic.

In late March, for instance, New York Gov. Andrew Cuomo passed Executive Order 202.13 to assist insurance policyholders experiencing coronavirus-related financial difficulties. The order stipulated that life, commercial property, home, auto, liability and other insurers extend grace periods for premium payment and repayment to affected individuals in the state. The order also temporarily prohibited some insurers from canceling, refusing renewal or offering conditional renewal on insurance.

These amendments to New York’s rules and regulations were more complicated than they seemed on the surface, which meant they raised legal issues in insurance businesses. For one, the accommodations only applied to individuals who faced economic hardship due to the pandemic. Individuals were required to provide insurers with written testimony — which added a considerable burden to insurers that had to collect, review and track this data. Some of those rules cast a long shadow. For instance, affected policyholders who were unable to pay their premiums have the option to repay what they owe in 12 monthly installments, which began in June 2020.

The emergency insurance industry regulation that was passed in New York is just one example of such orders. In every state, these changing insurance laws and regulations placed — and continue to place — a considerable burden on insurers.

The Department of Financial Services (DFS) has taken steps to lighten the load and help companies navigate the changes. While the uncertainty surrounding the pandemic persists, the DFS has loosened rules around notice obligations to allow insurers to email notices to policyholders, regardless of policyholder consent to email communication.

This provision, along with the DFS’s requirement that insurers post relevant information on their websites and maintain all records of communications with policyholders, was designed to communicate information to consumers rapidly and ensure records are complete and updated throughout the pandemic. In addition, the DFS provides sample correspondence for insurers to communicate COVID-related measures to policyholders.

See also: A Quarantine Dispatch on the Insurtech Trio

How to Be Compliant Amid Changing Rules and Regulations

Emergency insurance laws and regulations to protect consumers during the pandemic are necessary, but they make it difficult for insurers to reach strategic decisions and plan for what’s ahead. The future is cloudier than ever — no one knows how long emergency measures will remain in place or what the regulatory landscape or compliance requirements will look like in the near future.

Put another way, insurers are grappling with a plethora of hard questions. How will the new normal affect product features? How will risk assessment and financial underwriting be affected? What will be the long-term effect of wage loss for affinity customers in certain industries? How do insurers operationalize an increasingly complicated set of rules across multiple product lines, segments and regions? What happens if there is another surge of COVID-19 cases? The answers to these and other questions are neither clear nor simple.

Insurers face considerable questions when it comes to temporary insurance laws and regulations. Adaptation is a matter of survival. Here are a few steps insurance companies can take to handle the rapid changes and remain compliant:

• Dedicate a team focused on staying abreast of coronavirus-related regulatory changes, educating others in the company about these changes and building a strategy for compliance.
• Make business resiliency plans that take into account likely scenarios, including the emergency measures lasting in varying amounts of time.
• Repurpose staff members based on shifting organizational needs.
• Leverage external partnerships and partnerships between carriers and agencies/brokers — how can you support one another during this time?
• Appoint subject matter experts in account management, TPA management, underwriting and actuarial to handle related elements of the emergency regulations.

For many insurers, siloed legacy systems represent the biggest hurdle to meeting new insurance industry regulations while managing the resulting deluge of data. Switching to an integrated, comprehensive governance system that can better manage these changes and keep up with privacy and data management needs will not only help insurers weather the pandemic but will also help them make more informed, data-driven decisions for the future.

See also: How to Lead in the COVID-19 Crisis

COVID-19 has caused and will continue to cause headaches for insurers scrambling to keep up with new rules and regulations. But the pandemic also offers an opportunity to improve business systems and compliance practices. By making smart decisions now, insurers will be prepared for other changes that come.

This is a companion article to “Why Work-From-Home Threatens Innovation," published on Aug. 6.

As if our lives and the world in which we find ourselves aren’t confusing enough, for those of us working in the insurance industry ecosystem there are also less obvious threats that we should understand clearly.

There is a general perception that the insurance industry is doing surprisingly well in the face of a global pandemic. It’s true that the redeployment of thousands of employees from physical offices to work-from-home was accomplished very quickly and with minimal loss of productivity or gaps in customer service. It is also true that insurers, specifically auto insurers, have enjoyed an earnings windfall from the dramatic and sudden drop-off in vehicle use and the accompanying reduction in auto claims (even after premium reductions). So, one might also conclude that industry innovation and transformation continues apace – but that’s only partially correct. 

The Twin Realities

A closer examination of the evidence reveals that there are actually two extremely different states existing within the insurance ecosystem, essentially composed of the larger, high-profile, well-funded participants and then all the rest. The pervasive media coverage and general market buzz focused on acquisitions, IPOs, funding and consolidations involving the former group is obfuscating the deteriorating rate of progress among the more numerous and much smaller companies –  the very lifeblood of meaningful innovation and transformation, on which a very large number of Americans depend for their livelihoods. Compounding this dichotomy, and as explored more fully in my earlier piece, the longer-term costs of the new work-from-home model include increases in mental health issues and anxiety among this group. Overlaid on this is the large and growing talent and human resource drain from an industry now more focused than ever on cost reductions, primarily through staff cuts, hiring freezes and early retirement offers. Unfortunately, once the pandemic passes and workloads return to normal, this lost talent and expertise will not quickly or easily be repatriated. 

What the Latest Metrics Reveal

According to the Jacobson Group – the leading provider of talent to the insurance industry – in their July 2020 Pulse report, “As we enter the fifth month of the coronavirus pandemic, unemployment for insurance carriers and related activities rose to 4.6% in June – the highest unemployment rates the category has seen since 2013. The insurance industry historically lags behind the overall economy in terms of impact, and there are still predictions for a second wave of layoffs that will more directly impact white collar roles.”

In its August 2020 insurtech venture capital funding report, Crunchbase research reveals that, “from the beginning of 2020 through July 22, $2.6 billion had been raised for insurtech companies across 213 deals. That’s down from $4 billion across 315 deals during the same period in 2019,” a 35% year-over-year decrease in funding. 

See also: Why COVID-19 Must Accelerate Change

Willis Towers Watson states in its Q2 InsurTech Briefing that “we are in both pause mode and fast-forward mode. The strength and reliance on technology has never been greater, and yet poor market investment performances and focus on COVID-19-related priorities could see a downturn in technology investments from (re) insurance industry players over the next few years.”

Beyond these revealing investment activity metrics are the subjective observations from our own consulting practice. We are receiving a record number of personal outreaches and resumes from middle management up to executives from within the insurance industry. These inquiries reflect a large industry wide outflow of expertise and talent, which no amount of technology will replace anytime soon.

Signals Beyond Metrics

From the startup community itself, the outreaches for assistance with fund raising, go-to-market strategic advice and market entry are increasing weekly. Beyond the sudden challenge of raising early-stage capital, gaining access to insurers for presentations and discussions is a recurring theme. Hard-won POCs (proofs of concept), the lifeblood of startups, are being suspended or abandoned by carriers, presenting existential risks to these young companies. Consolidations between startups are on the increase, reflecting their need to create synergies, eliminate redundant overhead and conserve cash to survive.

To be sure, there are exceptions. Fueled by pandemic-driven demands, carriers are redoubling efforts to quickly implement telematics-enabled insurance programs, virtual and “touchless” claims inspection tools, AI-enabled photo and video estimating as well as fraud solutions along with digital claims payment capabilities. But the attention and energy required to investigate and adopt these valuable solutions is at the expense of the much greater number of worthy startups and innovations that have been put on hold.         

Other Looming Challenges and Risks

There are additional risks looming for insurers that will affect the broader ecosystem, and that will preoccupy insurers well beyond the end of the pandemic and continue to impair innovation and the health of the insurtech community for even longer. 

Business interruption lawsuits are mounting and will weigh on insurers of all sizes but particularly smaller, less-resourced carriers in defense costs, management distraction, negative public opinion and, in the worst-case scenario, costly settlements and possibly judgments.

Further, a tidal wave of deferred personal lines auto and property claims will surface once lockdowns are eased and will swamp claim departments whose staff has been depleted by layoffs and infrastructure reductions. While virtual and digital claims processes will help cover a portion of this claims volume, more complex or difficult claims will stretch already thin claims resources and expertise.

See also: What COVID and 43 Years Taught Me

And finally, even though new automotive technologies should gradually make transportation safer and reduce accident and claims frequency, those savings will be more than offset by rising complexity and costs associated with repairing these vehicles, driving auto severity to record heights.

The Future

However, I am not pessimistic about the future of insurance. In fact, I look forward to seeing and helping the industry regain and even accelerate the previous momentum gained by leveraging technology to fuel its inevitable and critical transformation. This quote serves us particularly well today – “opportunity and risk come in pairs” – Rwandan writer and blogger Bangambiki Habyarimana.

One of the surest ways to minimize risk is to recognize it and plan accordingly. Let the planning begin.