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.

High-touch brands and industries have led consumers to expect that they can interact with brands whenever they want, however they want. These omni-channel expectations have become especially difficult for insurers, many of whom were already lagging other industries in terms of digital and self-service capabilities. 

Every brand touchpoint that insurance carriers offer can serve as both an opportunity and a risk—while positive interactions will build customer relationships and build trust, a subpar experience can have the opposite effect. Journey mapping is often the first step insurers take in engineering these experiences. 

Journey maps identify, visualize and describe every single brand touchpoint, including getting a quote, filing a claim, making a policy change and paying a bill. In a sense, they serve as a guide for marketers to build out an omni-channel experience. They can help identify gaps in the experience as well as find critical moments for consumers, whether rational or emotional. Successful journey mapping can help drive customer engagement, loyalty and trust, which boosts an insurer’s image and the bottom line. 

Making Journey Maps That Matter

Journey maps can also allow insurers to visualize user experiences over a particular time period. While customers may be the group that come to mind for most marketers, insurance organizations should also be mindful of the agent and employee experience. Some journey maps focus on all three groups at once. 

As with any new project, the best journey maps require structure. Organizations need to establish a clear framework, set of goals and defined scope for journey maps. This is especially important as journey maps can serve as a common point of reference for teams all across an insurer, including product teams, underwriting, operations, data science and marketing. Each department has a unique view of the customer journey and can identify gaps that other teams might not notice. 

Companies that have successfully generated an outside-in journey map standardized guiding principles across the organization, got feedback from distribution partners and had executive sponsors offering governance. Journey mapping is an iterative process, so the more involvement from varying departments across an insurer, the better. 

See also: COVID: Agents’ Chance to Rethink Insurance

What Goes on a Journey Map?

Not every journey map will be composed of the same elements, just as not every insurer has the same customer touchpoints. The format of a journey map depends on what business problems an insurer needs to address and which teams are involved. There are some commonalities, however. 

Most journey maps include some of the following features:

• The journey’s stages
• The steps of each stage 
• Actors and personas 
• Triggers 
• List of challenges and perceived obstacles 
• Brand touchpoints 
• Data and analytics requirements

The team in charge of designing a journey map analyzes every stage of the journey from a customer perspective. What is the customer thinking, feeling or experiencing along the way? Pinpointing specific moments of excitement or frustration for customers is pivotal if an insurer wants to generate an outside-in picture of their customer journey. 

Above all, journey mapping should be founded on the principles noted earlier. There should be cross-departmental governance to ensure engagement and a customer focus. Insurers should also be sure to establish a measurement framework with stated key performance indicators (KPIs) describing success along the customer journey.

KPIs commonly include metrics such as customer satisfaction survey scores, behavior metrics like time to completion and call-center volume relative to digital. The scope of the project should remain fixed on the customer journey to ensure the story line feels realistic and to keep progress on track. 

Journey mapping alone cannot mitigate the challenges of creating an engaging customer experience, but they are a powerful tool insurers can use as part of a larger strategy. Often, the collaboration involved in creating a journey map is just as important as the map itself.

To learn more about the components of a journey map and how to launch a successful journey mapping project, read Novarica’s full report, Customer Journey Mapping: Key Issues and Best Practices.

As captive insurance agencies grapple with increasing online competition and growing product commoditization, they are constantly looking for any competitive edge they can get.

One advantage many captive insurance carriers overlook is connected to what may seem like a disadvantage —  consumer preference for online research. When a consumer searches for products or services, the consumer typically starts with an online search, and the insurance industry is no exception. In fact, research from PWC found that 71% of consumers use some form of digital research before buying insurance (e.g., price comparison or social media). 

Where the local captive agent wins is in physical proximity to nearby consumers. On Google and other popular search engines, this is the most important local SEO (search engine optimization) ranking factor. Therefore, in a search-driven world, carriers with a large network of local agents near millions of potential insurance buyers have a major advantage over online-only carriers, which cannot rank in local search. 

How Local SEO Drives Leads for Local Agents

The first thing most consumers see when performing searches, after a few paid ads, is the top three organic results from nearby businesses: referred to as the Google 3-Pack. Local agents should make every effort to show up in the Google 3-Pack as this coveted real estate drives the most traffic and calls. In fact, a recent study of billions of search results found that 55% of Google search users click on one of the first three organic search results. 

This being the case, the savviest insurance carriers focus on growing their share of voice for the proximity discovery keywords that matter most - i.e., "best car insurance" or "affordable car insurance." More specifically, they are focused on Google, which accounts for  94% of the mobile search market in the U.S.

Factors That Drive Local SEO Ranking

To increase organic search rank for important keywords like "auto insurance" and improve how frequently local agents appear in the Google 3-Pack, carriers need to optimize for the five known controllable signals that determine local search rankings via a process called Proximity Search Optimization (PSO). Effective PSO requires managing the five known signals: 

1. Local Listing Accuracy — information accuracy and consistency across all networks not only improves proximity search rank, it also helps customers find businesses in the real world.
2. Local Profile Completeness — ensuring all applicable fields are filled out on each discovery network gives customers a more complete picture of your business, while improving proximity search rank.
3. Ratings and Reviews — maintaining high customer rating scores and being responsive to reviews not only affects proximity search rank, it also influences buyers’ decisions on which business to choose.
4. Local Facebook Publishing Activity — frequent posts containing important keywords can improve your proximity search rank and help brands drive local community connections.
5. On-Page Local SEO — alignment with Google’s assessment of webpage quality helps determine where those pages and the associated listings rank in local search.

See also: 5 Ways Tech Can Draw Young Talent

Best Practices to Win in Local Search

Recently, my company, MomentFeed, conducted an audit of the top six captive insurance carriers to see how they perform against the five known PSO signals. Given the industry’s marketing maturity, we were surprised to find unimpressive performance on these metrics. 

The good news: There is a huge opportunity for any captive insurance carrier willing to invest in a more comprehensive local search strategy. With even a small improvement, any carrier can dominate in local SEO. Here are some important tips for optimizing for proximity search: 

1. Make sure your listing data is accurate and up-to-date.

This may sound obvious, but with so many pandemic-driven office closures over the last few months, a surprising number of businesses across consumer industries have not kept their listings up to date. Local insurance offices are no exception. The average local insurance carrier was found to have inaccuracies on 55% of its data.

Best practice: Make sure to claim all listings and ensure local listing information is accurate and consistent across the internet. This includes summaries of the business, contact information, address, hours, photos and more. 

2. Make sure all applicable fields are filled out on each discovery network.

When analyzing Google and Facebook, two of the most prominent discovery networks, we found that captive insurance carriers failed to complete 3% to 15% of applicable fields, such as hours and photos. While this may not seem like much, even the slightest margin of error seems to knock carriers out of the Google 3-Pack. Both of the carriers that performed poorly in this category saw a lower number of locations appearing in the Google 3-Pack as compared with others in the study.

Best practice: Review all your Google My Business listings and Facebook pages, and ensure that all fields are filled out. 

3. Reply to most if not all of your reviews.

Over the last decade, online reviews have become a common part of the insurance buyer's journey. Now, reviews are also a major factor for online search ranking, as well; a poor response rate or lower star rating can bump a local agent out of the Google 3-Pack. This means less visibility for insurance agents not focused on this critical metric.

Best practice: Respond to all reviews, both positive and negative within 24 hours. Ideally, maintain a response rate higher than 90% and aim to achieve an average star rating of four or higher across all critical networks. 

4. Post to local social sites at least nine to 10 times a month.

Another way to ensure your agents are visible when local insurance buyers search is through local social media content. Agents who post more frequently to their social media pages are more likely to be successful at reaching local audiences.

Best practice: Local agents should create local social media pages on Facebook and Instagram and post at least nine to 10 times a month. These posts should include geo-tagging using location hashtags like #SantaMonica, #Seattle, #Boston, local photos (note - photos taken with a GPS-enabled device will embed the photo itself with geo-tagging information) and of course the named location tag. Agents should also include keywords that the agent is trying to rank for, i.e., “car insurance” or “affordable insurance.” 

See also: Are Insurers Ready for Voice Search?

5. Optimize your local agent website’s on-page signals in the categories graded by Google.

On-page signals refer to the characteristics of a location’s webpage that help search engines and consumers use the site. Three of the most important website categories that Google grades are: accessibility (ability of the page to be operated by literally anyone); best practices (overall code of health - security, page load time, load without errors, etc.); and SEO (factors that help search engines find and understand the page).

Best practice: You can run a website assessment on your pages through Lighthouse, a free Google plugin, to see how you are performing across the three categories. 

Now that the vast majority of consumers are going online to research insurance options, a comprehensive local SEO strategy is essential to make sure your local agents are showing up in the top three search results. The payoff for agents that get it right is substantial: improved  local marketing performance and a higher volume of interested and engaged prospects that can ultimately lead to more successful customer acquisition.

“It’s only a gimmick.” When I heard myself saying this a few years ago about voice-activated assistants such as Alexa and Google Home, I almost bit my tongue afterward. This was because such arguments were almost always a sure sign of the coming market maturity of a technology, and I was usually accustomed to hearing this from insurance company directors who were well-advanced in their years. That had been the case with PCs, modem-based internet, mobile internet, social media and attention hacking.  

However, despite the boom of useful and not-so-useful digitization initiatives, voice-activated technology is still in its early days. There are a whole series of voice skill products, for example by Aviva, LV and Travelers, but they were unable to appease the many critiques. Many people reacted as I did initially – seeing the products as gimmicks.

It is, of course, true that the number of daily users as well as the number of regularly used voice skills is still relatively low, although this was also true of smartphones 10 years ago. At that time, most people still used mobiles without a touchscreen. Many decision-makers also said that no customer would take out insurance using a mobile. They were wrong. Today, it's normal that insurance policies are taken out online via smartphones or with apps. Why therefore shouldn’t this development also repeat itself in voice technology?

Smartphones and voice-activated technologies do, after all, have one thing in common: They help make work processes easier and faster. It is simply quicker and more convenient to take out insurance with Getsafe or Lemonade by app or with DFV or Ergo using Alexa.

We think that the insurance industry also has to look at selling insurance products via digital language assistants, which is highlighted in the study “Digital Insurance 2018,” conducted by Adcubum. This revealed that almost one in five Germans under 35 years old could envisage taking out an insurance policy using a digital language assistant. Additionally, according to a Statista survey of 2018, the number of people using digital assistants has more than doubled in three years. An increasing number of industries are also discovering the strategic benefits of voice technology – for example, car manufacturers with their own language assistants. Encouraged by this development, insurance companies should incorporate Alexa into their information and contract completion process as quickly as possible.

Until now, very few companies in the sector really offer a fully digitized insurance completion and consultation process, for example with Alexa. These include the insurtech company Deutsche Familienversicherung (DFV). The Frankfurt-based business programmed a skill in 2018 that conducts the consultation dialogue and accesses it via an API interface on the actuarial calculation engine. The skill evaluates the questions and provides the relevant product content, including monthly payments, voice-activated and in real time. In this case, integration in Amazon Echo goes beyond insurance contract completion and includes contract updating and payment handling, reducing call center and back office costs.  

See also: 10 Tips for Moving Online in COVID World

Alongside information, branding and sales, voice skill developers are also working on applications for the use of language assistants in the office, which appeals to brokerage firms. Case workers only need say, “Alexa, please inform the assessor for the Meyer case,” and brokers only need say, “Check status of billing.” This is significantly easier than the tedious battle with painfully complex user interfaces.

Even in the early stages of voice-activated technology and assistant systems, those investing in their development can secure an edge over the competition. Wouldn’t it be good to be among those present at the start of this technology of the future and gain market share? Let us make the most of these opportunities and not miss out on them, as was the case with PC, the internet and social media.

The question of whether schools should reopen has launched a raging controversy around the country, with widely held opinions on both sides. 

Here, in my home state of New Jersey, which was in the epicenter of the pandemic back in April, Gov. Murphy announced that all schools should open with in-person classes capability, but quickly added the parent option for all-remote learning as part of local school flexibility for hybrid approaches. Despite the provision allowing parents to opt for on-line teaching only, several members in the state legislature and several, but not all, leading teacher unions want all schools to remain strictly on-line until at least Oct. 31.

New York’s Gov. Cuomo announced that all schools can reopen, but each school district must submit a reopening plan that includes mandatory masks, social distancing and testing protocols. All teachers are strongly recommended to get tested before school begins. If cases should surge in New York, the state plans will be revisited.    

At the same time, due to surging COVID-19 cases around the country, several other large school districts such as Chicago, Los Angeles, Philadelphia, San Francisco, Atlanta and Houston have now announced plans to keep an all-remote choice and have postponed plans for any-in person classes. In addition, there have been large teacher union protests around the country, such as at the statehouse in Michigan. The largest teacher union in Florida has sought an injunction to keep schools closed. Elsewhere around the country, teacher unions are considering safety strikes, and there are widespread reports of veteran teachers retiring or quitting outright. In Georgia, video of high school students with crowded hallways and no masks went viral, resulting in six students and three staff members now testing positive for COVID-19. This certainly was not the way to start the school year.

Governors and state education and health officials must make anguishing decisions in the next few weeks, or even days, to address understandable fears that may or may not hold up to the facts. For example, a recent joint study by the Public Health Agency of Sweden and the Finnish Institute of Health and Welfare found no measurable difference in the number of COVID-19 cases among children in Sweden, where schools remained open during the pandemic, and Finland, where the schools remained closed. In Japan, when schools reopened after the state of emergency was lifted in May, schools initially used a hybrid approach of alternating days, but now things are fairly back to normal, but with masks, social distancing, washing hands and daily temperature checks all in place.

Added support for opening schools safely includes the American Academy of Pediatrics and the National Academy of Science, Engineering and Medicine, who suggested school districts should prioritize opening schools safely, especially among K- 5 and special needs students. Dr. Anthony Fauci, widely considered to be among the top infection control experts in the world, recently said, “The unintended, ripple-effect, downstream consequences of keeping children out of school can be profound.”  

The Centers for Disease Control and Prevention (CDC) has also released Resources on Safely Reopening Schools, which emphasized the importance of a safe strategy. The CDC director said in a press release, “School closures have disrupted normal ways of life for you and your children, and they have had negative consequences on our youth.” However, the CDC said schools should employ strategies that best match local conditions that are both practical and workable with a motto of Communicate, Educate and Reinforce. The CDC goes on to say there needs to be an overall emphasis on the social, emotional and mental health needs of children, while carefully watching local community transmission rates. 

The CDC said that early reports and data appear to suggest that children are at a lower risk compared with adults from COVID-19, and if they do get sick they generally have less serious illness. The CDC reported that, as of July 21, children and adolescents have accounted for 6.6% of cases and fewer than 0.1% of deaths in the U.S. In addition, initial data suggests most exposed children were infected by a family member and unlikely to transmit to other students. However, the American Academy of Pediatrics just announced a 40% increase in COVID-19 cases among children the last two weeks of July, mainly due to the recent surge of cases in the West and South.

All these leading medical experts strongly note that schools will not be the same, and the only way to even consider reopening school is by using significant mitigation strategies such as: daily temperature checks of students, teachers and staff; daily symptom checks; requiring all sick students and staff to remain home; requiring masks, social distancing, hand hygiene, cleaning, disinfection and flexible hybrid reopening plans such as allowing parents the choice of all or partial online teaching. There is no shortage of other strategies such as ending lunch in the cafeteria, social distance requirements on school buses and the use of outdoor tents for classrooms when workable.

Various projections based on local school board surveys in New Jersey of how many parents will opt to keep their children home from school run between 20% and 70%. Such a hybrid approach will help to keep class size down to better allow for social distancing for those who do attend school. New Jersey announced that parents will have the choice for all online learning, and most schools are putting together various hybrid plans, such as alternating in school and online learning on a weekly basis. This hybrid plan was considered by many school officials as a game changer to allow schools to open more safely.

National surveys have reported one in five teachers do not plan to return to teaching and will quit or retire. This is beyond troubling, but at the same time completely understandable for teachers with underlying medical conditions that put them at much higher risk. However, I implore schools and teachers to be more creative and allow these high-risk teachers to teach the online classes only, until the infection rate in their area is low and while the world awaits a proven and safe COVID-19 vaccine that just may be available in early 2021 based on very promising research and clinical trials now fully underway. 

The CDC and all public health officials support mandating masks for teachers, administrators and staff and highly recommend that children in school must also be required to wear masks. This is going to be incredibly challenging, and the survey found nine out of 10 teachers agree because children will be children, and it will be difficult to constantly check that masks are on properly. The CDC highly recommends parents begin student mask training and mask endurance because many may be wearing masks now, but not for extended periods. A great idea to get children on board is to make their mask “cool.” That is, use stickers or other ways to make it fun to wear and emphasize that many superheroes wear masks. 

See also: What COVID and 43 Years Taught Me

The decision on when and how to reopen schools safely must be based on facts and local infection rates and other factors. A zero-risk world does not exist, but creative and hybrid strategies can greatly reduce risks for children and their families, along with the teachers, administrators and staff. I know some people will say that this is easy for me to say because they think I am not a teacher. In fact, I teach math to K-5 children, and I plan on returning to in-person teaching this fall when safeguards are all in place.

A very recent poll of parents here in New Jersey was conducted by Farleigh Dickinson University and showed how sharply divided parents currently are, with 46% open to returning their children if protective measure are in place and 42% planning on keeping their children at home until proven treatments and a safe and effective COVID-19 vaccine becomes available. 

What I found deeply troubling in this poll is the continuing apprehension of vaccinations by parents even if one is approved after scientific, controlled studies prove successful. The survey found 50% of parents believe that, when a proven COVID-19 vaccine becomes widely available, it should be mandatory for children before they are allowed to go back to school, while 44% believe it should be a “personal choice.” This is a perfect example of the damage caused by the anti-vaccination movement, which the World Health Organization declared in 2019 as one of the top public health threats around the world. 

Before parents and schools can even consider various safe return-to-school protocols, a major hidden consequence of COVID-19 fears must be addressed. Due to the pandemic, many parents are avoiding going to doctors' offices and not getting their children wellness exams and routine vaccinations. Parents, schools and state public health officials, including governors, need to address this potential nightmare through a public education campaign. The need to protect children from COVID-19 does not eliminate the need to immunize them against serious childhood diseases like the measles, mumps, chicken pox and whooping cough

The National Foundation of Infectious Diseases has reported a 50% decline in well-child visits since the beginning of the pandemic. In response to this alarming trend, the World Health Organization just said, “The avoidable suffering and death caused by children missing out on routine immunizations could be far greater than COVID-19 itself.”

The local strategies required to open schools must be based on facts and science and include the verification that children have met the mandated requirement of what should be routine vaccinations before returning to school. In addition, now would also be the perfect time to require the flu shot for children, teachers and staff. Each year, the flu kills tens of thousands of people in the U.S.  The last thing in the world a child, staff member or teacher needs is to have the flu and COVID-19. The combined illness could be devastating, at best, if not outright deadly. The American Academy of Pediatrics noted that anything that taxes your immune system, such as the flu, would put a person at much higher risk.

The decision of how and when to reopen schools is a multifaceted dilemma and should be based on facts, science and the local situation. Although the American Academy of Pediatrics and other medical experts supports reopening schools for the overall wellbeing and learning experience for the children, they stress that a one- size-fits-all strategy is not appropriate. A hybrid approach is the leading strategy currently, with a combination of on-line and in-class curriculum, in conjunction with an array of safe practices, including mandating routine vaccinations for children and the flu shot for all, except in very rare medical cases, such as children with an underlying autoimmune disease certified by a physician.

A hospital in Boston is now supplying a mobile van to come to people’s homes to provide children their needed vaccinations. Now, that is what I am talking about. One thing for sure is politics should play zero role in opening schools on either side of this incredibly critical issue.

Disasters – such as pandemics – have a way of revealing the need for change. That’s why digital transformation has advanced five years in about eight weeks. Businesses of all sizes have embraced digital sales and service, and, due to lockdowns, just about everyone has experienced the increased need for digital engagement, with expectations only rising. 

For P&C insurance, the pandemic has brought digital strategies and investments into sharp focus and revealed, in real time, which digital investments are delivering high value, and which are not. It has also exposed our gaps and elevated capabilities that were once perceived as less important to higher levels of significance. 

SMA’s latest market pulse research on the impact of COVID-19 and our ongoing advisory work with insurers have revealed that this pandemic has reshaped and shifted digital investment priorities. 

Three “must have” investments have emerged as focus areas this year: Digital Platforms, Digital Payments and Digital Communications.

Given the shift in focus, SMA has just published reports on these three critical areas and completed the Insights to Solution Series, which are virtual events showcasing strategic insights, panel discussions and virtual tours by leading solution providers across all three areas.

Digital Platforms. It is essential to accelerate the digital enablement of sales and service capabilities for policyholders and agents/brokers with a modern platform and connections to core systems. These platforms enable new servicing, sales automation and straight-through processing (STP) by leveraging transformational technologies like AI, bots, IoT and wearables. 

Digital Payments. There is a renewed urgency to transform inbound and outbound payments by moving away from physical paper checks and payments and creating digital experiences and virtual digital payment capabilities. Digital payments are crucial to delivering a highly tailored customer experience as well as improved operational performance.

Digital Communications. Digital interaction and delivery for communications with prospects, agents and policyholders are increasingly essential. This includes digital communication tools like chatbots, voice and business texting, and the tools to capture, create and manage forms, documents, correspondence and messages to support interactions.

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

2021 may paint an entirely different picture, but, for now, P&C insurer plans are full steam ahead with digital transformation investments.