Insurers can help clients protect themselves - but preventive approaches aren't yet widely implemented, leaving the door open for unscrupulous hackers

Ransomware and business email compromise (BEC) attacks are soaring, and ransom demands have gone from an average of $10,000 to well north of $100,000 – demands sometimes reach the tens of millions of dollars. In this interview, we discuss what is causing the surge – and what businesses can do to protect themselves. 

This webinar will discuss:

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Presenters:

AI, machine learning, deep learning, natural language understanding, robotic process automation, intelligent process automation: Insurance has a lot of FUN (Frequently Used Neologisms).

The more terms are created and used, the less their meaning is clear, and this is exactly what is happening around AI. 

AI, or, better, machine intelligence, is a set of different technologies and techniques aimed at mimicking human behaviors. Not all are suitable to automate and optimize knowledge-based process such as claims or underwriting, where there are a lot of dense and complicated documents such as medical reports. So, how to scale and automate knowledge-based processes?

To define where to start, please consider:

• Strategic Plan: Each and every AI initiative must be 100% aligned with the company strategy. All initiatives should be part of an internal ecosystem, to share knowledge and lessons learned and to redefine priorities.
• Quick Wins: Begin where the use cases are not too complicated. Quick wins will fuel new wins and initiatives.
• Measure, Measure and Measure: Make sure there is clarity about criteria for success.
• The Right Skills: Pay attention to the teams and the right balance between outsourcing and internal staff, strengths and weaknesses. This is never a pure build-or-buy journey, but rather build-and-buy, where you can learn a lot from trusted advisers and providers.
• Do, Learn and Adapt: Agility and flexibility are extremely important when it comes to projects involving technology and innovation. Mitigate the risk by understanding immediately when it’s time to adapt.
• Organizational Impact: Do not underestimate the impact on the organization and the indirect variables that can influence the outcome of any initiative.
• Internal and External Visibility: Make sure the initiative is well-represented both internally and externally. Doing so you will help attract the right people.

See also: Crucial Technologies for P&C During COVID

Technology adoption is no longer a choice. It is a must-have. Carriers that will not act immediately will be left behind, victims of the AI divide.

When we think about how to prepare for a hard market, we immediately think of our end customers – the policyholders paying the premiums. While it’s true that we need to prepare them, the first step in doing so involves comprehensive training of our teams. It’s easy to forget that many of our employees have never lived through a hard market. They don’t know just how painful it’s going to be … for them and their customers.

Getting employees prepared and their messaging fully on point is like training for a battle. It requires conditioning, endurance and a tough dose of reality. Here are my top 10 tips:

1. Name the Training. When you put a name on something, you place a stake in the ground. People know it matters, and it’s going to involve a process. This isn’t a one-and-done. It counts. Call it whatever you want – Hard Market Bootcamp, Rate & Capacity Awareness or 2021 Reality Check – just give it a title and make it official.
2. Plan a Kickoff. Get everyone in your company on a call and introduce the topic. Show rate trends for your lines of business and have agents give firsthand accounts of situations they’re encountering. If you have veteran team members who were selling in the last hard market, have them tell the team what they remember about the last cycle. Share your historical retention rates and set a retention rate goal for the hard market. Use an infographic like this to paint the picture for your team.
3. Convey the Urgency. When customers are hit with rate increases, they shop, which means a massive amount of shopping is happening right now. How your team navigates the hard market will influence your success for the next decade. You can be the agency that uses it to capture new customers, or you can be the agency that loses customers and scrambles to stay even. Team members’ raises, bonuses and advancement opportunities will be affected by what they do right now. Do they know what’s at stake?
4. Bring the Numbers to Life. Use rate trends to illustrate the average financial impact on your customers. If a commercial auto client paid X to insure their fleet in 2019 and they have weathered two years of rate increases, how much premium will they pay at their 2021 renewal? How much will that affect their bottom line? 
5. Make Scarcity Real. It’s hard to envision something you’ve never experienced. We’ve all grown to expect an on-demand environment, where you buy anything, any time. Team members won’t comprehend that, in some cases, customers will simply not be able to buy the coverage they want and need. Capacity will not exist. Use the very timely and relatable metaphor of COVID to make it real. Just 18 months ago, we could not conceive of a time when you could not go to a restaurant or attend school in person. Lack of insurance capacity is like that. Access will be cut off in some cases.
6. Help Team Members Step Into Customers’ Shoes. It may seem obvious, but empathy is on the decline. Some people on your team won’t intuitively think about the hard market from your customers’ perspectives. Help them imagine it.
7. Give Them the Words. Don’t assume your employees know what to say. Strategically consider the best way to frame your hard market conversations and write down scripts for common scenarios that arise. Make sure everyone on your team is sticking to the script and communicating the same message. Have your account teams practice and role play.
8. Allow Time for Best Practice Sharing. Establish time each week for account teams to share what they encountered, talk about how they overcame challenges and support one another. This is a battle for coverage, and your team must be mentally focused and supported.
9. Challenge Your Team to Set Up Preemptive Communication Processes. The absolute worst thing we can do is surprise our customers. Our job is to help them navigate risk, and a big spike in expenses is a major risk. We need to let them know what to expect and let them know that we’re shopping the market and advocating on their behalf. If they know we’re on their side, they will be less likely to shop. Talk about the hardening market in your blog and in your customer newsletter. Start sending emails and letters six months in advance of renewal, letting clients know what’s happening in the market and what you’re doing about it. Call them 90 days in advance. Spending the extra time up front could raise your renewal rate a few percentage points.
10. Revise Your Game Plan Every Month. Hold a monthly training meeting for your entire team to compare notes, share best practices and review procedures. In every meeting, ask your front-line workers to share their experiences. Give key people notice that they will be asked to speak, so they come prepared. After a couple of months, people will know the drill and it will happen automatically, but you might need to engineer the experience the first couple of times. This should be collaborative and engaging – not a call where you talk and they listen. Track your monthly retention rates and compare them with the same month last year on each call, so your team has metrics to gauge their success.

See also: The Cost of Uncivil Discourse

As an industry, we always say that insurance is not a commodity, but in soft markets differentiation can be challenging. On the other hand, hard markets present an opportunity for the best insurance professionals to stand apart. This is our chance to demonstrate our value, make a difference and earn customers for life. Let’s do this!

Artificial intelligence (AI) and machine learning have come a long way, both in terms of adoption across the broader technology landscape and in the insurance industry specifically. That said, there is still much more territory to cover, helping integral employees like claims adjusters do their jobs better, faster and easier.

Data science is currently being used to uncover insights that claims representatives wouldn’t have found otherwise, which can be extremely valuable. Data science steps in to identify patterns within massive amounts of data that are too large for humans to comprehend on their own; machines can alert users to relevant, actionable insights that improve claim outcomes and facilitate operational efficiency.

Even at this basic level, organizations have to compile clean, complete datasets, which is easier said than done. They must ask sharp questions — questions formulated by knowing what the organization truly, explicitly wants to accomplish with AI and what users of AI systems are trying to find in existing data to get value. This means organizations have to know what problems they’re solving — no vague questions allowed. Additionally, companies must take a good look at the types of data they have access to, the quality of that data and how an AI system might improve it. Expect this process to continue to be refined as companies attain a greater understanding of AI and what it can do.

AI is already being applied to help modernize and automate many claims-related tasks, which to this point have been done largely on paper or scanned PDFs. Data science will push the insurance industry toward better digitization and improved methods of collecting and maintaining data. Insurtech will continue to mature, opening up numerous possibilities on what can be done with data.

Let’s look at some of the ways AI systems will evolve to move the insurance industry forward.

Models Will Undergo Continuous Monitoring to Eliminate Data Bias

AI will continue to advance as people become more attuned to issues of bias and explainability.

Organizations need to develop the means (or hire the right third-party vendor) to conduct continuous monitoring for bias that could creep into an AI system. When data scientists train a model, it can seem like it’s all going very well, but they might not realize the model is picking up on some bad signals, which later becomes a problem. When the environment inevitably changes, that problem gets laid bare. By putting some form of continuous monitoring in place with an idea of what to expect, a system can catch potential problems before they become an issue for customers.

Right now, people are just doing basic QA, but it won’t be long before we see them harness sophisticated tools that let them do more on an end-to-end development cycle. These tools will help data scientists look for bias in models when they’re first developing them, making models more accurate and therefore more valuable over time.

Domain Expertise Will Matter Even More

In creating these monitoring systems, they can become sensitive to disproportionate results. Therefore, organizations must introduce some kind of domain knowledge of what is expected to determine if results are valid based on real experience. A machine is never going to be able to do everything on its own. Organizations will have to say, for example, “We don’t expect many claims to head to litigation based on this type of injury in a particular demographic.” Yes, AI can drill down to that level of specificity. Data scientists will have to be ready to look for cases where things start to go askew. To do that, systems — and even the best off-the-shelf toolkits — have to be adapted to a domain problem.

Data scientists are generally aware of what technology options are available to them. They may not be aware of the myriad factors that go into a claim, however. So, at most companies, the issue becomes: Can the data scientists understand whether the technologies they know and have access to are appropriate for the specific problems they’re trying to solve? Generally, the challenge that organizations face when implementing data science solutions is the difference between what the technology offers and what the organization needs to learn.

Statistical methods, on which all of this is based, have their limitations. That’s why domain knowledge must be applied. I watched a conference presentation recently that perfectly illustrated this issue. The speaker said that if you train a deep learning system on a bunch of text and then you ask it the question, “What color are sheep?” it will tell you that sheep are black. The reason is that, even though we know as humans that most sheep are white, it’s not something we talk about. It is implicit in our knowledge. So, we can’t extract that kind of implicit knowledge from text, at least not without a lot of sophistication. There’s always going to have to be a human in the loop to correct these kinds of life biases to close that gap between what you can learn from data and what we actually know about the world. This happens by inviting domain expertise into the data science creation process.

We’re getting better and better at democratizing access to AI systems, but there will always be an art to implementing them — where the data scientists have to be close to the subject matter experts to understand the underlying data issues, what the outcome is supposed to be and what the motivations are for those outcomes.

Unstructured Data Will Become More Important

There is so much data at insurance companies’ disposal, but we have only tapped into a small percentage — and we’ve yet to cultivate some of the most significant assets. The integration and analysis of unstructured data will enable this to happen as it becomes more accessible.

Case in point: Natural language processing continues to mature. This means that, instead of pulling information from structured fields, like a yes/no surgery flag that could be interpreted pretty quickly by reading claim notes, adjusters could receive a more holistic view of the claim, going beyond the structured data and finding more and more signals that would have otherwise escaped the adjuster’s attention.

Images also provide all types of exciting and insightful unstructured data. The interpretation of scanned documents is a necessary part of claims. Advanced AI systems that can handle unstructured data would be able to read them and incorporate relevant data into outputs for evaluation. Theoretically, even further in the future, adjusters could look at pictures from car accidents to ascertain the next steps and cost estimates.

See also: Despite COVID, Tech Investment Continues

Systems that can interpret unstructured data also will be able to extract information in terms of drugs, treatments and comorbidities from medical records. In claim notes, sentiment analysis will seek out patterns from across many claims to identify the ones that yield the most negative interactions with claimants so that early interventions can occur to influence claim outcomes. We are just scratching the surface on unstructured data, but it won’t be long before it makes a profound impact on insurtech.

Feedback Loops Will Improve

Ideally, good machine learning systems involve feedback loops. Human interaction with the machine should always improve the machine’s performance in some way. New situations will perpetually arise, requiring a smooth and unobtrusive way for humans to interact with machines.

For example, claims adjusters may review data outputs and determine that possibly this sentiment wasn’t actually negative, or they might learn that they missed extracting a drug. By letting the machine know what happens on the “real world” side of things, machines learn and improve — and so do claims adjusters! To reach this level and to be able to continually improve data analysis and its applications, undergoing a continuous improvement loop, is where AI will ultimately shine. It empowers adjusters with rich, accurate knowledge, and, with each interaction, the adjuster can inject a bit more “humanness” into the machine for even better results the next time.

Companies are putting systems in place to do that today, but it will still take a while to achieve results in a meaningful way. Not a lot of organizations have reached this level of improvement at scale — except for perhaps the Googles of the world — but progress in the insurance industry is being made each day. AI systems, with increasing human input, are becoming more integral all the time. Within the next five to 10 years, expect AI to transform how claims are settled. It’s a fascinating time, and I for one look forward to this data-rich future!

As first published in Data Science Central.

We posed six questions to Tony Caldwell, a mentor to independent agencies who has written a number of articles for us on how agencies must adapt.

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Let's start with a general question that has puzzled me. The need for change by agents seems to be well-established by now, but many aren't adapting. Why is that? 

Business owners, in general, who have historically successful business models are typically reluctant to change. They may hope change won’t be necessary and, in some cases, plan to escape the need to change by retiring. With that said, however, I think the biggest reason agents are not adapting is because they don’t know how to do it, and so are overwhelmed by what they perceive as a difficult process. This isn’t unusual, but can be fatal.   

The biggest change facing agency owners is increased customer expectations for speed, timeliness and improved experience.  As they seek to meet customer expectations, agencies are now competing with innovators in every industry -- though many do not realize this.  As customers increasingly expect more, and others use technology to deliver services at lower cost, the danger for those who are late, or never adapt, may be that they have become boiled frogs. 

Once an agency decides it needs to adapt to the more digital world, and all the new dynamics that come with that, what's the first thing the agency should do? 

Find out the capabilities of the agency management system (AMS) and maximize those. AMS are very powerful and capable, and most agencies use only a fraction of their capabilities to analyze, manipulate and use data to improve retention and conduct account rounding and new business acquisition. There is no point in acquiring new technology or technical capability without maximizing what the agency is already investing in. Maximizing AMS capabilities will show the agency where their strengths, weaknesses and opportunities for improvement lie, and help them make better investment decisions on the next set of capabilities.    

What's the second? 

Invest in 24/7/365 capabilities for customer self-service. It’s indisputably clear that consumers want to be able to serve themselves when possible. Agencies are compared with every other business -- inside and outside insurance -- that gives consumers the ability to that. Consumers clearly want to have their needs and wants satisfied in real time, so agents need to provide those capabilities whether through technology, expanded agency personnel availability, carrier service centers or other third-party providers. Agents should consider the ideal customer experience from the customer’s perspective and then upgrade their capabilities to meet them.   

In articles you've written for us, you've mentioned some specific digital tools that agents should investigate, including RiskMatch. Could you offer agents a few starting points? 

In terms of customer service technology, AMS vendors like EZLynx offer the capability to put limited customer self-service on agency websites at low or no cost. Companies like Tarmika use new technology to make it easier for agents to rapidly market small business insurance. New industry entrants like Aureus Analytics are making it increasingly easy to unlock data to help agents improve retention, cross marketing and new business using artificial intelligence. Companies like Hubspot make it easy for agency websites to automate marketing, track and follow website visitors and accomplish other tasks to improve new business flow, hit rates, account rounding and retention. And technology like that provided by RiskMatch enables agencies to make better coverage placement decisions and maximize carrier contract opportunities, among other things.   

But let’s walk before we run. Agency owners have an opportunity to dramatically increase agency revenue by using their existing AMS to improve staff management; make better, more profitable, placement decisions; improve revenue through retention; and hit agency account size targets, among other things. Using a disciplined investment approach to acquiring and maximizing technology one capability at a time allows agencies to improve top and bottom lines along with value in a relatively short (12- to 24-month) period.  

You've also mentioned that agents aren't taking enough advantage of the tools provided by carriers. What, in particular, do you see agents missing out on? What happens when they don't adapt to what carriers are pushing them to do? 

One of the biggest is compensation. Carriers are increasingly offering agents incentives for specialized new business and providing retention bonuses. Yet agency owners allow employees to make placement and new business marketing decisions without regard to maximizing agency revenue. Worse, owners often fail to maximize traditional compensation like profit sharing because line staff, instead of management, are making placement decisions. Carriers often offer an amazing, and bewildering, array of training and marketing resources that aren’t taken advantage of by agencies. Yet, by analyzing their opportunities and weaknesses, agencies can easily develop plans to put these resources to work.   

Agencies should recognize that carriers face increasing cost challenges and that many are improving automation of their new business submission and underwriting. Carriers will be increasingly less tolerant of, and pay less to, agencies that don’t cooperate with their systems to improve hit rates. Agencies will be increasingly rewarded for giving carriers what they want without forcing them to weed through mindless submissions of what they don’t.  

What other threats are out there for agencies that don't adapt? You've written about private equity; could you explain that threat and list any others? 

The first threat agencies need to understand is that the market is shrinking. Direct sellers, virtual agencies, affinity marketers and non-traditional providers like Lemonade are all taking market share from traditional agents. Agencies must realize they cannot compete with algorithms on price and, instead, must focus their efforts and investment on improving the customer experience and relationship. In smaller agencies (under $500,000 in annual revenue), private passenger auto represents as much as 60% of agency revenues. It’s beyond obvious that this market will ultimately shrink, with technology reducing both physical damage and liability risks.   

Private equity-backed insurance agencies will also take market share from traditional agencies as they aggregate capability – human and technological – as well as make the necessary management and investment decisions to raise the bar in marketing, sales, operations and carrier management.   

In summary, the biggest threat is complacency. All threats have answers, including retreat, defeat, joining others and, most importantly, increasing competitive capability. As technology gets cheaper (which it always does) and easier to use, smaller agencies have increasingly capable competitive tools at their disposal. With a relentless drive for improvement in operations and management, agencies can adapt and continue to thrive.   

For insurance companies, there’s a constant influx of data from almost everywhere: customers, marketing teams, sales representatives, underwriting departments, HR and more. These massive amounts of data can be used to make your company better, or so you’ve been told. But harnessing business value from this data isn’t as easy as it might seem. It takes more than collecting data and building models for AI to help a business.

In the last few years, a technology has emerged that can harness AI across all departments of a business like never before, enabling massive, company-wide returns. However, the technology alone isn’t enough; there must be the right combination of technology, people and process.

Feature Stores for Machine Learning

Data scientists love to dive deep into different algorithm alternatives, but the most effective way to get better predictive signals is to get the right data. For example, in media personalization, companies often used the fact that a particular user visited a particular site (like a luxury shoe brand) as an important data point. But this is deceptive. Recency also matters. If a visit to a particular site has been within, say, the last 48 hours, you get significantly better conversion on ads. You have to get the right data points represented to get a model to perform!

Data points that inform models are known as features. These are usually transformed data attributes, which together form the feature vectors that are the input to machine learning algorithms. The process of turning raw data into features is called feature engineering, and is — in my opinion — the critical success factor for practical ML projects that deal with corporate structured data.

Not only is feature engineering essential for model accuracy, it’s also incredibly time-intensive for data scientists. Data preparation takes 80% of data scientists' time, which means they only have 20% left to actually build, test and implement models. This makes it incredibly difficult and expensive to build models at the volume that would be necessary to provide value for every department of an insurance company.

Technology leaders like Uber, Google and Airbnb have spent years and millions of dollars designing infrastructure that makes it possible to unleash the power of AI throughout a company. The solution they have all converged on is a feature store.

A feature store is a central repository that stores features, data lineage and metadata associated with all the machine learning models in a company. In essence, it is a single source of truth for all of the data science work within one organization. Being able to share and re-use features boosts data science productivity by cutting down duplicate work and making it easy for data engineers, data scientists and ML engineers to collaborate. Each machine learning model becomes cheaper and easier to produce. (If you want to learn more about why that is, there’s a more in-depth resource here.)

See also: 6 Implications of Big Data for Insurance

Integrate Diverse Skill Sets in Data Science Teams

Even though feature stores are incredibly powerful tools, they are ultimately still tools, which means how they’re used will influence how helpful they are. Even with a feature store bridging the gaps inside a company, a “siloed” data science structure makes it hard to truly integrate AI into the enterprise.

Traditionally, the people who can manage large volumes of data and “do the math” of machine learning are sitting in their silos. They are away from the action — where the application interacts with customers, suppliers and employees. They are one step removed from the business. 

But the AI or data science team is not equipped to get the job done independently. They simply do not have enough knowledge about the business or the applications that will deploy the models to lead to production applications that deliver business outcomes. The secret sauce to a successful AI implementation is diversity. Data scientists need to work side by side with people who know the business and the application from inception to completion. 

Culture of ML Experimentation

Machine learning projects need to include more than just subject matter experts and application developers as part of the data science and data engineering teams. To do ML well, you have to create a culture of experimentation within your data science team. 

Markets change, bad actors innovate, the climate changes, the competitors change and so much more. What was the perfect feature vector on go-live might produce noise two months later, or worse — tomorrow. You must realize that an ML project will not thrive with a hands-off approach; it is a process of continuous experimentation and continuous improvement. So the secret is to keep the diverse team intact, frequently evaluating the deployed models, and able to experiment with new features.

See also: Insurance Outlook for 2021

Conclusion

The technologies and organizational silos of the past weren’t made to embed AI into the fabric of organizations, and as a result, companies that aren’t innovating aren’t benefiting from the full power of AI.
To inject AI throughout a company, the goal needs to be the continuous improvement of business outcomes.

You can achieve this by optimizing the two bottlenecks of the operational process:. First, overcome the feature bottleneck of the ML lifecycle with a feature store. Second, overcome the organizational bottleneck of the technology lifecycle by distributing data experts in every department of your company. Your teams will finally be able to demonstrate a significant ROI from your AI.

As the largest generation in North America, millennials are a critical demographic for insurance carriers. Not only because this is a group with significant purchasing power, but also because their expectations around customer service and customer experience are changing the way many carriers operate. 

Millennial policyholders expect exemplary customer experiences and want digital ways to interact and pay bills. Many digital native insurance carriers are making it easier than ever to select and purchase a policy directly from a mobile device. But, for carriers that have been slower to innovate, gaining and retaining customers in this age group is becoming a challenge.

It's important for insurance carriers to realize, though, that technology can be an opportunity rather than a threat. Technology lets organizations personalize policyholder experiences, improve engagement levels and elevate the customer experience.

To help insurance organizations better understand how to leverage millennial policyholder preferences, Invoice Cloud recently conducted an online survey asking this age group questions about the types of policies they own, how they prefer to make payments and more. You can download the full report here, but here are three major takeaways from our research.

1) The needs of millennial policyholders are evolving

In 2019, millennials surpassed Baby Boomers as the largest living adult generation. Millennials spend trillions of dollars a year on goods and services – and insurance is no exception. 

According to the survey results, the most common policy among millennials is personal auto insurance (78%), followed by health insurance (76%). 

In total, 85% of survey respondents anticipate buying at least one new policy in the next five years. This means that the money spent on insurance by this already influential demographic will inevitably increase, and rapidly. 

The millennial generation spans individuals in their mid-20s to late 30s (with the eldest in this group turning 40 in 2021). This represents a sizeable range when it comes to consumer needs and life experiences. On the one hand, some millennials are starting at their first jobs, are striking out in the renter’s market and are no longer covered by their family’s insurance plans. On the other side of the age bracket, older millennials are buying property, starting families and purchasing more policies as their insurance needs grow. 

As the data shows, the gap between single millennials (whose insurance policies only cover themselves) and millennials who are starting families (whose insurance policies cover their partners and dependents) is gradually closing.

What this means for your organization: It’s clear that millennials are increasingly making up a large proportion of policyholders. They represent a group with a vast array of policy needs, and many are going through life transitions: moving from renter’s to homeowner’s insurance, or upgrading policies from covering an individual to covering a family. Carriers have to take millennial purchasing, payment and interaction preferences into consideration when deciding how to leverage technology and improve the overall customer experience. 

See also: 4 Firms That Understand Millennials

2) Millennials will switch carriers for a better experience, and have a history of doing so

When it comes to insurer loyalty, the survey results show that millennials will not hesitate to switch carriers if their current insurer isn’t providing a satisfactory customer experience. Forty-one percent of the millennial policyholders surveyed have switched insurance carriers within the last six to 12 months, and 76% have switched in the past five years.

We asked respondents to pinpoint why they left their former insurance provider. 

While 48% of respondents cited price as a primary driver, what’s more informative for insurers is that 22% switched for a better customer experience or expanded digital offerings and payment methods. 

What this means for your organization: Competitive pricing will always exist in the insurance space, and no carrier can accommodate the changing needs of every insured – it’s much more attainable for your organization to improve the policyholder experience and expand digital payment options. Try auditing your policyholder experience and payment offerings, taking time to evaluate what it’s like to pay premiums with your organization. This will give your organization the opportunity to address these issues and avoid spikes in customer churn. 

3) Digital experiences affect the purchase decision

When asked how they purchased their latest insurance policy, 41% of millennial respondents said they bought the policy on their mobile device, directly from the insurer. 

Purchasing policies and paying premiums aren’t where mobile preferences stop, either: 64% of respondents would rather communicate with an insurance carrier through a digital or mobile channel.

Even payment method preferences are evolving as millennials take up more of the insurance space. While credit cards and ACH are still common methods for making premium payments, 25% of millennials who said online or mobile payment options were “very” or “somewhat important” would rather pay their premiums via PayPal/Venmo or Apple Pay/Google Pay. This is another huge shift that’s unique to this rising demographic. 

What this means for your organization: Insurance organizations can no longer afford to ignore mobile payment channels and methods. Insurers must provide mobile channels for purchasing policies, communicating with insurers, paying premiums and more. Insurers must also ensure that these mobile channels are fully optimized by offering digital wallet options, pay-by-text functionality and a well-designed mobile interface for an outstanding user experience. 

See also: Six Things Newsletter | April 6, 2021

Start leveraging this data today

Clearly, millennial policyholders are an important market for every insurance organization, particularly as the needs of this generation continue to grow and evolve.

To learn more about important millennial insurance preferences, including how they choose insurance carriers, payment preferences and the most important factor when it comes to a great customer experience, download your free copy of Invoice Cloud’s research report, Keeping up with Millennial Policyholders.

The digital revolution in insurance, which began in distribution and then spread to claims, has now reached underwriting in a big way.

There are two consistent themes: 1) Advanced AI and ML technologies, paired with big data and sophisticated risk models, are fundamentally shifting the way underwriting is done. 2) Insurers are leveraging low-cost, cloud platforms that are built for scale and agility with new business models.

In this article, we will explore those two themes and show how digitization streamlines the underwriting process for a more efficient and sophisticated outcome. In our next article, we will explore how carriers are making the shift to next-generation underwriting, changes to user journeys and experience, and measuring ROI in these AI journeys. 

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The insurance industry relies heavily on actuarial models and benchmarks to analyze performance and predict future exposures. One of the core assumptions is that most components of the analysis mirror conditions similar to the past. However, the pandemic introduced several variables into the analysis that question the validity of those models in the future. 

The latest Out Front Ideas with Kimberly and Mark webinar brought together a panel of industry experts discussing how models have been affected and how risk managers need to adjust their future expectations to account for the pandemic’s impact. Our guests included:

• Tamika Burgos Puckett – risk manager corporate security, Zoom
• Richard Frese – principal and consulting actuary, Milliman
• Ron Schuler – head actuary, property and casualty broking, North America, head of collateral solution, Willis Towers Watson
• David Stills – senior vice president, carrier and risk practice, Sedgwick

Benchmarking Goals

Benchmarking is essential for any risk management program. Using threat and opportunity assessments and identifying key risk indicators help an organization assess any new risks. Organizations that have incorporated enterprise risk management programs use these identifiers in a holistic approach to establish business objectives.

Months before the first claim of the year, risk managers working with actuaries will need to make predictions about ultimate costs for the entire year. Significant business and financial decisions are made based on these predictions, including budgeting, service pricing and how much risk to take in other areas of the company. Predictability is critical for year-over-year improvement in these metrics.

While there are many vital aspects of benchmarking in casualty claims management, it is crucial to recognize the direct correlation between employees’ care and financial management metrics. With regard to workplace injuries, ensuring the delivery of timely and appropriate care and returning your employees to a pre-injury condition should remain at the top of the list. Staying focused on advocacy and timely care results in better outcomes for everyone. 

Benchmarking Challenges

The amount of data currently available seems endless, but is everyone interpreting it correctly? Are the risk managers’ goals aligned with executive goals or even the legal team’s metrics? Does everyone know what they are trying to achieve with their data collection? While collaboration is key to understanding benchmarks, making sure that everyone has similar goals is critical to exposing areas of opportunity. 

See also: Investment Mania: Understanding Why

Additionally, finding a good data source can be a primary challenge. Comparing similar risk profiles based on claims performance, including reporting, case reserves, severity and frequency, is critical to data accuracy. Other benchmarks like deductibles, premiums and collateral amounts can provide increasingly specific data to an organization. 

Regardless of how much and what type of data is gathered, be sure to make an action plan. If the effort is put into gathering all of this data, get interpretations from other experts and plan for making improvements.

COVID’s Impact

COVID-19 has made a considerable impact on analytics, but one of the most significant has been caused by presumption laws. These laws shifted the burden of proof, which completely changed the perspective in workers’ compensation. Employers, both insured and self-funded, anticipated more of a financial burden, resulting in duplication of benefits for some employees.

The pandemic also forced employers to focus on leading risk indicators like safety and preparedness, creating a more positive impact on benchmarks. As far as major disruptors, remote work had not been considered by many employers before COVID-19. New considerations like hardware, internet access, productivity expectations and, of course, cyber risk had to be made a priority to minimize business interruption.

While there are some similarities in overall impact, the effect of COVID-19 on benchmarks depends on the nature of the business and the physical location of employees. For example, retail will not be affected the same way that healthcare has been affected. Some states also had a greater impact than others because of differences in government shut-down orders. With claims consideration, pay close attention to the types of claims because exposures have shifted. While retail has seen a shift to online shopping, creating less foot traffic and reducing in-store exposures, increased distribution center activity could result in claims associated with overexertion or driver activity. Considerations also need to be made for the following:

• Unemployment rates causing difficulties with the return to work and potential for fraudulent claims due to financial strain.
• Potential surge in cases due to businesses reopening and restrictions being lifted.
• Comorbidities with COVID-19 long-haulers.
• Changes made to presumption laws. 
• Backlog of litigation cases.
• Prolonged hard market due to uncertainty in underwriting new policies.

While many risk profiles have changed because of workers’ compensation resources being reallocated and exposures moving to other jurisdictions, recognizing the difference in those jurisdictions and their influence on those risk profiles is critical in terms of cost and performance. 

Advice for Risk Managers

First and foremost, define your narrative. Be aware that every business is unique and has been affected differently by the pandemic. Risk managers should focus on claims frequency and how it has been affected by COVID-19 but understand that it could be starkly different from historical workers’ compensation claims. If possible, isolate the variable of COVID-19 claims, increase the frequency of analysis on metrics and test scenarios and assumptions. The finer the data can be stratified, the better. 

See also: 11 Keys to Predictive Analytics in 2021

Uncertainty is a risk manager’s worst nightmare, but properly using critical resources can lessen the fear. Lean on peers and colleagues for different perspectives and use subject matter experts, like brokers, insurers and actuaries, to prepare. Also, get senior-level management and the executive team involved to understand the full scope of the risks. Make sure financial leadership understands the uncertainty around the range of estimates on policy costs and read full policies and understand potential gaps in coverage. Remember to stay focused on the people — advocate for the injured workers and focus on getting them back to work.

To listen to the archive of our complete COVID Analytics & Benchmarks webinar, please visit https://www.outfrontideas.com/. Follow @outfrontideas on Twitter and Out Front Ideas with Kimberly and Mark on LinkedIn for more information about coming events and webinars.