Surprising Lack of Innovation Plans Paul Carroll, Editor-in-Chief of ITL The 2020 Global Concerns Survey of insurance leaders by the International Insurance Society contains two major surprises. (I got an early look because ITL collaborated on this latest annual survey.) The smaller surprise is that COVID-19 ranks only second among the most important issues the executives identified. I had expected that the pandemic would be the top concern, given that 2.4 million people have died worldwide, that economies have been devastated and that insurers face exposure, especially given the recent decision by the U.K. Supreme Court that business-interruption insurance should cover pandemic-related claims. The bigger surprise is that, while innovation is the top concern, only 35% of respondents said they have an active, comprehensive plan — meaning that two-thirds do not... continue reading > SIX THINGS 4 Connectivity Trends to Watch in 2021 by Dave Acker In a business defined by relationships, connecting well on a virtual basis will be more than a change — it will be a requirement. Read More The Intersection of IoT and Ecosystems by Matteo Carbone Insurers can build a sort of digital twin of the customer, then tailor their offerings and improve the customer experience. Read More Let’s Do More Than Create Faster Horses by Tim Kershaw COVID-19 has accelerated adoption of e-trading and smashed paradigms. There is an opening for something fundamentally new. Read More How AI Can Transform Insurance Correspondence sponsored by Messagepoint Focusing on customer experience is a winning strategy as digital transformation efforts accelerate into 2021. Learn how AI-based tools are helping industries modernize their systems, optimize their content, and manage customer communications intelligently. Watch Now Closing the Protection Gap by Simon Young With climate risk on the rise and exposure growing, parametric insurance can plug the gaps left by traditional insurance. Read More Why CX Must Trump Efficiency by Renaud Million Companies talk about improving customer experience but focus too much on saving money. Customer process automation does both. Read More CISOs, Risk Managers: Better Together by Charles Pruzinsky In most large firms, risk managers buy cyber insurance--but are rarely expert in network security and may not fully understand the risk profile. Read More MORE FROM ITL February's Topic: Blockchain While the pandemic has greatly accelerated the digitization of the insurance industry — turning years into months — it has also shown us how very far we still have to go. As a rule of thumb, I’ve heard consultants say that 50% of the operating costs need to be driven out of the industry in the next five years. Blockchain has held out this promise for some time now. It’s lost a bit of its shine because it’s been identified as a hot technology of the year for so many years in a row. But it may be coming into its own, with some uses starting to move into production. Take Me There The Future of Blockchain Series Episode 3 Usage in Life & Annuities Having explored the possibilities for blockchain in personal lines and commercial lines in P&C, we conclude our webinar series on the technology by taking a look at two use cases in life and annuities that are close to moving into production.  Watch Now GET INVOLVED Write for Us Our authors are what set Insurance Thought Leadership apart. Get Started Partner with Us We’d love to talk to you about how we can improve your marketing ROI. Learn More SPREAD THE WORD Share Share Tweet SUBSCRIBE TO SIX THINGS

Is it myth or reality that remote work is going to halt innovation and collaboration in our workplaces?

We've heard lots of insurers express concern about the possibility, especially because they depend on collaboration to help their organizations build deeper and wider relationships with their agents and brokers to develop more business. Deciding whether the concern is a myth or a reality is tricky, because it's really up to the company and their organization.

Certainly, remote work can make innovation and collaboration more challenging. It removes easy access to that organic, unstructured “white space” where conversations naturally happen: grabbing a cup of coffee, passing in the hallway or chatting and building ideas after a meeting.

Remote work also makes innovation and collaboration even more important. Numerous studies have shown that companies that focused on innovation, both during and after a crisis, financially outperform the companies that do not, both during the crisis and far into the future.

So, it really is important that we do what we can to make sure the concern about remote work stays a myth. 

A silver lining is that the decades-long investments in digital transformation (which, frankly, have happened largely outside of the insurance space) have enabled us to remain connected rather than isolated. We have been able to use tools like Microsoft Teams, Zoom and Slack and online collaboration platforms like Miro to work together while we are apart. The added benefit is that we can tap into the best resources for the topic, project or relationship, regardless of location.

That said, it's not enough just to have the tools available. We also need to create an environment that encourages innovation. The fastest way to derail innovation efforts is to have a fear- or shame-based culture in which teams and employees are too afraid of making mistakes to offer new ideas. A courageous and specifically inclusive approach to ideation and doing business is really crucial – one where risk-taking (including the inevitable failures along the way to success) is rewarded. That's how we get the best ideas and bring them into action.  

Finally, we must make up for the gap in organic connection through a tried-and-true method of driving innovation – creating Networked Improvement Communities.

This approach is widespread outside the insurance space, but it's something we should deploy here for our benefit and that of our organizations, staff and customers. The objective is to create a community within your organization that is specifically dedicated to solving an identified problem. It can be outside the usual structures, teams and siloes. That community works independently on that problem but collaborates in sharing and building on one another’s solutions and ideas, driving innovation and creating deeper relationships across your organization. A great example is the global innovation effort in the scientific, medical and pharma community to develop a vaccine for COVID-19, which has resulted in the fastest vaccine to market by leaps and bounds.  

See also: Tapping Cloud’s Ability to Drive Innovation

Call to Action:

Here are three elements each company can use to ensure that remote work is NOT the end of innovation and collaboration in your organization:  

One: Assess your culture and eliminate any roadblocks to innovation. Reward risk-taking and curiosity. Make sure that you've got an inclusive environment where people are encouraged to challenge the status quo, try new ideas and speak up (even at the risk of failure) to make sure that the best solutions for the situation are sourced and selected. 

Two: Continue to use the many digital tools available for connection to make sure that we don't stay isolated, even in a remote work environment.

Three: Get explicit about creating Networked Improvement Communities to connect your organization’s employees and leaders across siloes to solve a specific problem. They can work independently but collaboratively to amplify solutions. That will create ripple effects, deepening those relationships beyond that specific project and allowing new ideas to form. (This step might be the most important in insurance.) 

If we take these steps, we can ensure that innovation and collaboration continue in workplaces in 2021. Those are the workplaces we all want to join.   

 

According to Willis Towers Watson, more than two-thirds of insurers credit predictive analytics with reducing issues and underwriting expenses, and 60% say the resulting data has helped increase sales and profitability.

That figure is expected to grow significantly over the next year, as the inherent value of predictive analytics in insurance is showing itself in myriad applications.

Predictive analytics tools can now collect data from a variety of sources – both internal and external – to better understand and predict the behavior of insureds. Property and casualty insurance companies are collecting data from telematics, agent interactions, customer interactions, smart homes and even social media to better understand and manage their relationships, claims and underwriting.

Another closely related tool is predictive modeling in insurance, such as using “what-if” modeling, which allows insurers to prepare for the underwriting workload, produce data for filings and evaluate the impact of a change on an insurer’s book of business. The COVID-19 crisis has shown insurers that the ability to predict change is invaluable, and “what-if” modeling is a great tool for carriers that know they need to make changes but want to ensure they are doing it accurately. The right predictive modeling in insurance software can help define and deliver rate changes and new products more efficiently.

Using the plethora of data now available, here are 11 ways predictive analytics in P&C insurance will change the game in 2021.

Pricing and Risk Selection

This isn’t exactly a new use for predictive analytics in insurance, but pricing and risk selection will see improvement thanks to better data insights in 2021. Given the increased variety and sophistication of data sources, information collected by insurers will be more actionable.

Why do these data sets help predictive analytics improve pricing and risk selection? Because they are largely composed of first-hand information. Data and feedback collected from social media, smart devices and interactions between claims specialists and customers is straight from the source. Data that isn’t harvested through outside channels (such as the typical demographic material used in the past, like criminal records, credit history, etc.) is more direct and can provide valuable insights for P&C insurers.

But just how much data are insurers collecting from IoT-enabled devices? Some reports estimate it’s approximately 10 megabytes of data per household, per day, and that figure is expected to increase.

Identifying Customers at Risk of Cancellation

Predictive analytics in P&C insurance is going to help carriers identify many customers who require unique attention – for example, those likely to cancel or lower coverage. More advanced data insights will help insurers identify customers who may be unhappy with their coverage or their carrier.

Having this knowledge in hand will put carriers ahead of the game and allow them to reach out and provide personalized attention to alleviate potential issues. Without predictive analytics, insurers could miss credible warning signs and lose valuable time that could be used to remedy any issues.

Identifying Risk of Fraud

P&C insurance companies are always battling various instances of fraud and oftentimes aren’t as successful as they would like. The Coalition of Insurance Fraud estimates that $80 billion is lost annually from fraudulent claims in the U.S. alone. Additionally, fraud makes up 5% to 10% of claims costs for insurers in the U.S. and Canada.

Using predictive analytics, carriers can identify and prevent fraud or retroactively pursue corrective measures. Many insurers turn to social media for signs of fraudulent behavior, using data gathered after a claim is settled to monitor insureds’ online activity for red flags.

Insurers are also relying on insurance predictive modeling for fraud detection. “Where humans fail, big data and predictive modeling can identify mismatches between the insured party, third parties involved in the claim (e.g. repair shops) and even the insured party’s social media accounts and online activity,” according to SmartDataCollective.

See also: What Predictive Analytics Is Reshaping

Triaging Claims

Customers are always looking for fast, personalized service. In the P&C insurance industry, that can sometimes present a challenge. But with good predictive analytics systems, carriers will be able to prioritize certain claims to save time, money and resources – not to mention retain business and increase customer satisfaction.

Predictive analytics tools can anticipate an insured’s needs, alleviating their concerns and improving their relationship with their carrier. It can also contribute to tighter management of budgets by employing forecasted data regarding claims, giving insurers a strategic advantage.

Focusing on Customer Loyalty

Brand loyalty is important, no matter the product, and now insurers can use predictive analytics to focus on the history and behavior of loyal customers and anticipate what their needs may be. How important is brand loyalty? About half of customers have left a company for a competitor that better suited their needs. Also, this data can help insurers modify their current process or products.

Identifying Outlier Claims

Predictive analytics in insurance can help identify claims that unexpectedly become high-cost losses — often referred to as outlier claims. With proper analytics tools, P&C insurers can review previous claims for similarities – and send alerts to claims specialists – automatically. Advanced notice of potential losses or related complications can help insurers cut down on these outlier claims.

Predictive analytics for outlier claims don’t have to come into play only after a claim has been filed, either; insurance companies can also use lessons learned from outlier claim data preemptively to create plans for handling similar claims in the future.

Transforming the Claims Process

With predictive analytics, insurers can use data to determine events, information or other factors that could affect the outcome of claims. This can streamline the process – which traditionally took weeks and even months – and help the claims department mitigate risks. This also allows insurers to analyze their claims processes based on historical data and make informed decisions to enhance efficiency.

Advancements in artificial intelligence and other analytical tools have also become increasingly important in the claims process and are transforming how carriers do business.

Data Management and Modeling

Data is one of the most valuable assets an insurer can have, and predictive analytics have been helping businesses make the most of that data. From forecasting customer behavior to supporting underwriting processes, predictive analytics and data have been working together to provide valuable insights to insurers for years now.

However, making the most of your data is only possible with excellent data management and modeling capabilities. If data is scattered across disparate systems and there isn’t a strategic plan in place, all of that data is wasted. With data management solutions, predictive analytics tools can build a robust customer profile, provide cross-sell and upsell opportunities or even forecast potential customer profitability. And with insurance data models, insurers can deliver on-demand services to their customers via the cloud, using the data-driven insights gathered from their data management platforms.

Identifying Potential Markets

Predictive analytics in insurance can help insurers identify and target potential markets. Data can reveal behavior patterns and common demographics and characteristics, so insurers know where to target their marketing efforts.

Because there are 3.2 billion people on social media around the world, these platforms have become increasingly important when it comes to identifying potential markets. The platforms also influenced customer service: about 60% of Americans say that social media has made it easier to obtain answers and resolve problems.

Gain a 360-Degree View of Customers

TechTarget defines the 360-degree view of a customer as “the idea that companies can get a complete view of customers by aggregating data from the various touch points that a customer may use to contact a company to purchase products and receive service and support.”

Using predictive analytics, insurers can quickly and accurately consolidate data and generate insights that paint a more complete picture of a customer. What are their buying habits? What is their risk profile? How apt are they to buy new or expanded coverage? Before predictive analytics, insurers could estimate or take guesses at these questions, but now they are able to accurately and effectively service customers, which ultimately results in happier customers and increased revenue.

See also: How Analytics Can Tame ‘Social Inflation’

Providing a Personalized Experience

Many consumers value a customized experience – even when it comes to shopping for insurance. Predictive analytics in insurance provides the capability to comb through IoT-enabled data to understand the needs, desires and advice of their customers.

More and more insurers will use predictive analytics to help forecast events and gain actionable insights into all aspects of their businesses. Doing so provides a competitive advantage that saves time, money and resources, while helping carriers more effectively plan for a future defined by change. After all, data is only a strategic asset when you can actually put it to work.

The 2020 Global Concerns Survey of insurance leaders by the International Insurance Society contains two major surprises. (I got an early look because ITL collaborated on this latest annual survey.)

The smaller surprise is that COVID-19 ranks only second among the most important issues the executives identified. I had expected that the pandemic would be the top concern, given that 2.4 million people have died worldwide, that economies have been devastated and that insurers face exposure, especially given the recent decision by the U.K. Supreme Court that business-interruption insurance should cover pandemic-related claims.

The bigger surprise is that, while innovation is the top concern, only 35% of respondents said they have an active, comprehensive plan -- meaning that two-thirds do not. A further 12% said their firms are preparing to implement a plan, but that still leaves more than half with little innovation activity.

"I believe the health crisis has actually highlighted the need for innovation." said Josh Landau, president of the IIS. "The pandemic has exposed areas of weakness in how companies connected with clients and staff and managed data."

Maybe I'm taking the lack of innovation planning personally, given how much we stress the need for digital transformation at Insurance Thought Leadership and how many pieces we've published that try to give companies a starting point for innovation efforts.

It's true that not all promises related to innovation have been borne out -- the peer-to-peer model didn't work, on-demand insurance has proved tricky, too many have claimed "transformation," etc. -- but I still see the industry as a good five to seven years into a wave of technology-driven innovation, and I'd think that just about every company would at least have a plan in place.

I suppose the good news is that those of you who have laid the groundwork for substantive innovation have stolen a march on those who have yet to get going. If you've begun reinventing and speeding up your claims processes, are already incorporating lots of unstructured data into your increasingly digital underwriting operations, are experimenting with chatbots, robotic process automation and other tools to take a whack at your operating costs, are exploring how to use technology to reimagine the customer experience from scratch... well, I predict you will be rewarded for your prescience.

In the meantime, we at ITL will redouble our efforts both to sell the industry on the need to emphasize innovation and to help people and companies get started.

Stay safe.

Paul

P.S. Here is a link to the press release on the study and to a white paper based on it. In addition to ITL, the Pacific Insurance Conference collaborated with IIS on the survey. All three entities are affiliates of The Institutes.

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

4 Connectivity Trends to Watch in 2021

In a business defined by relationships, connecting well on a virtual basis will be more than a change — it will be a requirement.

The Intersection of IoT and Ecosystems

Insurers can build a sort of digital twin of the customer, then tailor their offerings and improve the customer experience.

Let’s Do More Than Create Faster Horses

COVID-19 has accelerated adoption of e-trading and smashed paradigms. There is an opening for something fundamentally new.

Closing the Protection Gap

With climate risk on the rise and exposure growing, parametric insurance can plug the gaps left by traditional insurance.

Why CX Must Trump Efficiency

Companies talk about improving customer experience but focus too much on saving money. Customer process automation does both.

CISOs, Risk Managers: Better Together

In most large firms, risk managers buy cyber insurance--but are rarely expert in network security and may not fully understand the risk profile.

Not so long ago, many chief information security officers (CISO) and other information-security professionals were offended by suggestions that their organizations should buy cyber insurance. After all, CISOs reasoned, if they did their jobs well, insurance would be unnecessary.

Fast forward to 2021. There probably isn’t a single CISO who believes that their organization is immune to potentially devastating cyberattacks. Recent news of alleged Russian penetration of well-protected government agencies and major corporations is one more reminder that any and every organization is vulnerable. Still, many CISOs are skeptical of insurance's benefits and often are only tangentially involved in cyber insurance decisions.

CISOs are often concerned about perceived gaps in insurance coverage, about underwriting criteria that are misaligned with an organization's security policies and procedures and about the willingness of insurers to pay claims. Some concerns are valid. For example, if an organization’s hardware is damaged by a malware attack, not every policy provides “bricking coverage,” which pays to replace impaired equipment. However, many CISOs' concerns are based on now-outdated policy language and underwriting and claims practices. As cyber insurance has matured, underwriters are offering broader coverage with less burdensome underwriting requirements. Rather than avoiding claims, insurers are often trusted partners in responding to cyber events and managing their consequences.

Cyber insurance coverage may be more expansive now, but insurance buyers must still ensure that the protection they purchase is adequate and appropriate for their organization and its specific risk profile. In most large organizations, the risk manager buys cyber insurance. However, risk managers are rarely experts in network security and may not fully understand their organization's cyber risk profile and control environment. This may result in purchasing insurance that does not adequately cover significant exposures, while over-insuring low-priority or well-managed risks. To ensure that cyber insurance aligns with the organization's risk management needs, risk managers need to work with a broker who specializes in this type of coverage offering. Additionally, the risk manager and the broker need to include the CISO in the buying process. 

CISOs and risk managers have a common mission — to protect the assets of their organization. In many organizations, they haven’t effectively collaborated -- along with their broker and carrier partners -- to achieve their common goals. Even when insurance is recognized as an essential part of the overall cyber risk management strategy, organizational silos, the lack of a common risk vocabulary and differences in risk management frameworks can impede cooperation.

According to a SANS Institute report, Bridging the Insurance/Infosec Gap, "InfoSec and insurance professionals acknowledge they do not speak the same language when defining and quantifying risk, leading to different expectations, actions and justification for outcomes."

The SANS Institute does not offer a one-size-fits-all solution for closing the gap. Within an organization, successful coordination and cooperation depend on corporate culture, institutional obstacles and how motivated CISOs and risk managers are to cooperate on their common goal.

See also: How Risk Managers Must Adapt to COVID

A coordinated approach is more essential today than ever before. With so many employees working from home during the COVID-19 pandemic, using their personal networks and often their own equipment, IT departments and security professionals struggle to ensure network security. A survey of 250 CISOs by Resilience (named Arceo at the time of the study) found that cloud usage, personal devices usage and unvetted apps or platforms posed the most significant threats during this period of increased telework. 

With so many factors outside the direct control of IT and information-security professionals, insurance becomes essential. But cyber insurance policies can materially vary, and not all insurers offer enough of the right coverage to satisfy an organization's risk-transfer requirements. Once the corporate risk management and information-security functions are aligned, a broker can help navigate the universe of cyber insurance and help the client understand nuances in policy language to satisfy the organization's risk-transfer requirements.

The outcome is an integrated program where insurance from secure and knowledgeable carriers is fully aligned with the organization’s risk profile and information-security strategy.

There isn’t an insurance business in the land that isn’t talking about digital transformation. Whether talking about AI, robotics or platforms, the majority of the industry is confident it’s heading toward a brightly lit, digital future.

The motivation for transformation? We are told customers are demanding a better experience: an interaction that is quick, clean and gets the job done with minimal fuss.

But, for all the effort made, the customer experience in insurance is fundamentally the same as it was 10 or 15 years ago – it’s still based on call centers. I think that is because, while the stated driver for digital change may be the customer, its primary purpose has been to reduce costs.

That efficiency-first approach has resulted in many organizations looking to webforms to digitize their customer-facing processes.

Webforms do a decent job leveraging digitalization to automate the beginning of processes normally done manually. Yet any claim coming from a webform still requires the capable hands of an operations employee, who will perform the rest of the process and communicate the outcome to the customer. In addition, webforms can’t converse -- reducing them, essentially, to digital monologue. While customers want a quick, hassle-free experience, many want that done through conversation of some kind. Conversations are comforting, familiar and create a sense of engagement that a static form can never replicate. 

A true digital experience is one that takes all the benefits of a one-to-one conversation and automates it using conversational process automation (CPA). That is the world that webforms were trying to create but failed to produce because of the focus on efficiency.

Source

CPA leverages a chatbot conversational interface to deliver an efficient customer experience, thinking about the customer first while saving cost. It allows for the execution of high-value, customer-facing processes, integrated into insurance platforms and systems and complying with security and audit requirements.

CPA will, I believe, bring the digital change that so many seek. They can replicate the conversational style and effectiveness of a human call handler for the vast majority of recurrent insurance interactions – from quote and buy through to claim notification. 

CPA has the capacity to handle call volumes that only a very large, very expensive call center could match. Of course, there are limits to what CPA can currently do, but it is improving all the time -- getting smarter at predicting queries, reacting to something that doesn’t fit into the box and leading the customer through complex processes. Webforms, for all their value, can never do that. 

As we collect more and more data through CPA, performance becomes more accurate and, according to a report from IT advisory firm Gartner, by 2022 70% of white-collar workers will interact with conversational platforms on a daily basis. 

See also: Insurtechs’ Role in Transformation

The combination of process automation and superior customer experience will drive efficiencies. A recent report by McKinsey estimates that, in the claims process alone, automation could reduce the cost of that journey by as much as 30%.

For insurance to be part of that digital future and to reap its rewards, the industry has to have customer experience as its main motivator, replicating all the value that a one-to-one conversation brings and putting the customer in control of the experience while keeping costs to a minimum.

If we persist in letting costs saving alone drive transformation, we are going to end up with fancier, more expensive tools than webforms that will deliver marginal efficiency while continuing to leave customers frustrated. And that would be a failure of purpose and progress.

There’s a misnomer about internet leads, and it’s written all over Facebook and proclaimed by many agency owners, producers and industry gurus: “Internet Leads Suck!” Many of the big lead vendors add fuel to the fire with dubious pricing, odd delivery and questionable results. Is the contempt of web leads legitimate? How else can we actually grow our businesses?

My observation from interviewing hundreds of agents on the Insurance Dudes Podcast is that only the best of the best have effective processes to properly build a lead-closing machine — the majority, the naysayers, lack this systemization.

In addition, there’s a disconnect between agent expectations about various lead types’ performance expectations; many agents don’t even know what metrics they should track to effectively create a feasible cost per sale. 

This article, the first of three in this series, will shed some light on the proper tactics needed to support an effective strategy for developing an effective internet tele-funnel.

For the most part, agents who have not been successful with internet leads seem to point their finger in the wrong direction. Most agents, including me (for many years), blame the lead provider. A powerful shift occurs with the epiphany that the common denominator across success AND failure is the same: the lead vendors. 

Well, if some succeed, while others fail, with the very same lead vendors... the issue must not be the leads themselves, but the process by which the leads are worked.

Over the course of making over 13 million of dials, and seeing incredible results, I have seen that most agencies lack a systematized process to follow up on leads. 68% of the time (the first Alpha for you statisticians), your typical live internet lead will take between eight and 21 dials to close — this is the hard data. Using a data set of at least 90 days, these numbers consistently hold true. Because the bulk of leads closed require OVER EIGHT dials for new business to be won, it’s imperative that a highly organized and trackable process is in place. 

Understanding this need for dials, an agent must stay the course for at least a few months to know a true cost per sale. Considering that large companies will commit to a specific marketing budget for the long term, and only pivot once they have insight into performance, why is it that so many agents will eject after just a week or two? 

“Getting your toes wet” is not an option, as it will only lead to poor results. An agent must know the numbers, the spending required and the timeframe of the sales cycle to win with leads — and this framework holds true for any marketing.

Digging further into the 13 million-dial data set, we know that “good” leads have a first-day contact rate of about 15%. Intent, type, cost and everything don’t matter if the contact rate isn’t better than 15%. Let that sink in… to achieve a positive outcome for your tele-funnel’s entry point, the winning metric comes down to connecting on 15 out of 100 dials. This makes for a lot of down time for the people doing the dials, even with a fast (and fully TCPA compliant) dialer.  

See also: Despite COVID, Tech Investment Continues

Agents must break through and understand that the need to put the right players in the right positions is critical. In building your tele-funnel, dials are the highest-quantity activity, while requiring the lowest skill set. This knowledge is crucial to moving “leads that suck” from the first, second or third dial (the average times that average agents call leads) to making eight to 100 dials on a lead.

Once we had calculated the enormous number of daily dials required to reach our goal of $200,000-plus in premium per month, we knew mathematically that we needed 5,000 or more dials per day, as a team, just to hit all of our leads from today, yesterday and from the prior 88 days. 

We were in a race to move these leads — our agency’s latent equity — closer to a sale. We discovered that the more dials on a lead, the less it actually cost, because the potential to make contact, quote and close increased with each dial! 

With this realization, we sought to ensure we could guarantee making the dials we needed without burning out our agents and ensuring they were on the phone doing their most important activity, quoting, at least 10 new households per day. Plugging unlicensed, cheap labor into the top of the funnel also allowed us to continue to fill our pipeline with new prospects while freeing up agents’ days to follow up on unclosed quotes.  

After weeks and months of consistency, training and oversight, we were writing $5,000 to $20,000 or more a day. We had handled the first important piece of the equation: We’d created a systematized process to create predictable results. We had certainty that if we added X leads into the tele-funnel, it would result in Y sales. 

There have been ups and downs, but the word du jour is persistent-consistency. 

In the next article, I’ll take you into the metrics that need to be looked at, and the necessary baselines that need to be hit to ensure that your tele-funnel machine is functioning properly.

Imagine you were suddenly refused insurance coverage, or your premium increased 50% just because of your skin color. Imagine you were charged more just because of your gender. It can happen, because of biased algorithms.

While technology improves our lives in so many ways, can we entirely rely on it for insurance policy?

Algorithmic Bias

Algorithms will most likely have flaws. Algorithms are made by humans, after all. And they learn only from the data we feed them. So, we have to struggle to avoid algorithmic bias -- an unfair outcome based on factors such as race, gender and religious views.

It is highly unethical (and even illegal) to make decisions based on these factors in real life. So why allow algorithms to do so? 

Algorithmic Bias and Insurance Problems

In 2019, a bias problem surfaced in healthcare. An algorithm gave more attention and better treatment to white patients when there were black patients with the same illness. This is because the algorithm was using insurance data and predictions about which patients are more expensive to treat. If algorithms use biased data, we can expect the results to be biased.

It doesn't mean we need to stop using AI -- but, rather, that we must make an effort to improve it.

How Does Algorithmic Bias Affect People?

Millions of people of color were already affected by algorithmic bias. This bias mostly occurred in algorithms used by healthcare facilities. Algorithmic bias has also influenced social media.   

It is essential to keep working on this problem. In the U.S. alone, algorithms manage care for about 200 million people. It is difficult to work on this issue because health data is private and thus hard to access. But it's simply unacceptable that Black people had to be sicker than white people to get more serious help and would be charged more for the same treatment. 

How to Stop This AI Bias?

We have to find factors beyond insurance costs to use in calculating someone's medical fees. It's also imperative to continually test the model and to offer those affected a way of providing feedback. By acknowledging feedback every once in a while, we ensure that the model is working as it should. 

See also: How to Evaluate AI Solutions

We have to use data that reflects a broader population and not just one group of people -- if there is more data collected on white people, other races may be discriminated against.

One approach is "synthetic data," which is artificially generated and which a lot of data scientists believe is far less biased. There are three main types: data that has been fully generated, data that has partially been generated and data that was corrected from real data. Using synthetic data makes it much easier to analyze the given problem and come to a solution.  

Here is a comparison: 

If the database isn't big enough, the AI should be able to input more data into it and make it more diverse. And if the database does contain a large number of inputs, synthetic data can make it diverse and make sure that no one was excluded or mistreated. 

The good news is that generating data is less expensive. Real-life data requires a lot more work, such as collecting or measuring data, while synthetic data can rely on machine learning. Besides saving a lot of money, synthetic data also saves a lot of time. Collecting data can be a really long process.

For example, let's say we are operating with a facial recognition algorithm. If we show the algorithm more examples of white people than any other race, then the algorithm will work best with Caucasian samples. So we should make sure that enough data has been produced that all races are equally represented.

Synthetic data does have its limitations. There isn't a mechanism to verify if the data is accurate.

AI is obviously having a significant role in the insurance sector. By the end of 2021, hospitals will invest $6.6 billion in AI. But it's still essential to have human involvement to make sure the algorithmic bias doesn't have the last say. People are the ones that can focus on making algorithms work better and overcoming bias.

See also: How AI Can Vanquish Bias

Explainable AI

Because we can't entirely rely on synthetic data, a better solution may be something called "explainable AI." It is one of the most exciting topics in the world of machine learning right now.

Usually, when we have a certain algorithm doing something for us, we can't really see what's going on in the work with the data. So can we trust the process fully?

Wouldn't it be better if we understood what the model is doing? This is where explainable AI comes in. Not only do we get a prediction of what the outcome will be, but we also get an explanation of that prediction. With problems such as algorithmic bias, there is a need for transparency so we can see why we're getting a specific outcome. 

Suppose a company makes a model that decides which applications warrant an in-person interview. That model is trained to make decisions based on prior experiences. If, in the past, many women got rejected for the in-person interview, the model will most likely reject women in the future just because of that information.

Explainable AI could help. If a person could check the reasons for some of these decisions, the person might spot and fix the bias. 

Final words

We need to remember that humans make these algorithms and that, unfortunately, our society is still battling issues such as racism. So, we humans must put a lot of effort into making these algorithms unbiased.

The good news is that algorithms and data are easier to change than people.

Six years ago to the day, as I write this, I was a keynote speaker at a TINtech London Market conference with a brief to talk about e-trading and technology that asked: What are the new technologies that will affect people and process and stimulate innovation? Why is now the time to invest? What are the challenges to overcome? 

I start with this because it resonates strongly with what the InsTech London “E-trading platforms challenges, opportunities, and imperative” paper now sets out to address, several years later.  

Delve deeper, however, as this paper does, and you see that, while progress has not been made as swiftly in our sector as it has in others, the encouraging reality is that a lot has, in fact, changed and even more seems to be about to change in what the paper describes as a “truly digital world that many of the consumers and businesses we serve are already inhabiting.”

The paper is, of course, a must read. For those with less time, you can head straight to the conclusions. Those who prefer edification with their morning coffee and sit back and listen to the excellent podcast with Robin Merttens and Mark Geoghegan. As a poor fourth, here is my brief summary of highlights:

• The environment is more fertile today than ever before. “COVID-19 has acted as an accelerant of digital adoption and provided momentum for the adoption of e-trading, a belated burning platform,” and is smashing some long-held paradigms, not least about shoe leather and queues. “There is more recognition of the imperative for change; greater desire from within to deliver it.”
• There needs to be an intermediate stage between digitizing what the market has today (slips, quotes) to help drive adoption, and then evolve to become a truly digital ecosystem. The intermediate phase will be the foundation to push on from. The digital evangelists in our market are crucial, but it is naïve for us all to think we can step straight into a full-on digital ecosystem. Electronic case files (ECF) eliminated the claims paper but did not fundamentally change the process. ECF opened the servicing bandwidth -- previously, the speed of client service was directly attributable to the length of a claims broker’s arms! ECF gave the opportunity to make a significant change from old state, but it was an example of digitizing an existing process and not reimagining it in a digital landscape. Perhaps a case of “digital lipstick on a legacy pig,” to quote Robin.
• Straight-through processing is still something of a dream, and much data remains sub-optimal. The data standards are too narrow, and more open-source standards must come to the fore. Connectivity is a big issue. Application programming interface (API) adoption will gather momentum once the building blocks are in place. There is “much work to be done to achieve the levels of system-to-system connectivity we need. The industry is well short of where it needs to be on defining data and process standards and on pooling the required knowledge and resources to define them.”
• The stage is set for more innovative digital solutions to emerge. It is about being brave, and “the market cannot be a closed club if it is to succeed with connectivity.”
• The big brokers’ influence is key – “the king makers.” The fear of disintermediation is nowadays overblown, but, with a traditional model of "customers to the left and markets to the right," there is still a fundamental barrier. That is unsustainable and cannot be a continuing strategy, which is something more and more brokers now recognize. It is in and around data, analytics and insight where they can add real value far beyond the transaction. Algorithms will take over. They must, not least because customers like them. “If we fail this time round the most likely cause will be that the brokers sought to protect the status quo for a decade longer.”  
• If the big brokers decide to push on, it will happen, and Lloyd’s has a huge role to play, too. Blueprint Two sets up an environment where “private enterprise can be the way forward for the market to adopt, and across multiple platforms.”
• Consensus is a better way forward (the carrot) rather than imposition (the stick), but at some point compulsion must come. “A long-term free-for-all is unsustainable, and there will, as with any space race, be winners and losers over the medium term.”
• The customer’s voice in ‘the future of insurance’ is not loud enough. The market needs to be bringing the customer inside the tent constantly. If we just concentrate on making life easier for brokers and underwriters, customers will build the solutions that suit their needs independently of us. A great example is Insurwave, the genesis for which was the growing frustration from AP Moller Maersk about the inefficient handling of its huge cargo account. The endgame of a genuine ecosystem involves all market participants, which must include customers. “Our customers are increasingly strident in demanding it.”
• Traction has always been missing, but credible leadership is now starting to show up.
• RIP, analog! “The influence of the analog era workforce is waning, and, as a result, the cultural barriers to adoption are declining.”
• My personal plea: Allow the innovators and the technology specialists to now lead us forward. In doing this, we must not forget the other key members of the cast. In the past, we were hindered by a fundamental lack of actual brokers and underwriters being involved. Those that trade, brokers that broke and underwriters that underwrite. Gather the requirements at a trading floor level with active engagement from that community feeding directly into the innovators and technology partners. This is not to build consensus, but rather to be sure to surface the key issues and pain points that communities need technology to solve for. Imagine organizing a music festival without any reference to the bands headlining and their needs. Or designing a restaurant without involving the chef.  

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

In conclusion: We are in the risk business, yet we have been risk-averse. Let us take some risks now or, as Henry Ford would have it, we will forever just create faster horses. Or in our world, perhaps we will just end up with smarter slips. “Now we need to harness this collective will to get it done and take great care that we don’t jeopardize this historic level of enthusiasm,” and “we can’t build our future in an isolated echo chamber. It is a prerequisite that we understand what is going on in other industries and align our technology, products, and services with their requirements and interests.” Now really is the right time, and, to borrow from Macbeth, “if it were done, when ’tis done, then ’twere well it were done quickly.”

Traditional, end-to-end business models are breaking down in every industry, including insurance. In the digital era, it is increasingly difficult for any single firm to deliver the seamless experience that customers expect. More insurers are leveraging digital ecosystems to reinvent their products and services, providing better risk management, reduced claim cost and new sources of revenue.

However, rooted in legacy systems and siloed business structures, most insurers even lack the foundations to successfully execute insurance ecosystems. Insurance organizations will likely struggle in moving from traditional insurance offerings to tailored, ecosystem-driven customer experiences.

Nonetheless, insurers should have a plan for incorporating ecosystems into their business models. It’s time for all insurers to become insurtechs.

As opposed to the traditional business model, where insurers create and distribute end-to-end products and services, an ecosystem model is characterized by unified/digital platforms that incorporate third-party products and services and collaborate with segment-focused distribution partners. Carriers must either bundle value from others with their products (e.g., providing IoT-based real-time risk mitigation services) or provide value to a bundle that someone else is creating (e.g., insuring the performance delivered by an IoT service provider).

Based on research from the IoT Insurance Observatory — a think tank focused on North America and Europe with almost 60 members, including many of the largest insurance and reinsurance groups and prestigious tech players like ValueMomentum — the adoption of IoT requires a robust set of capabilities, as represented in the following figure. 

See also: The New IoT Wave: Small Commercial

Any insurance IoT program is a multi-year journey that requires overcoming functional silos, coordinating the different stakeholders and developing a collective intelligence. Insurers can achieve four kinds of goals:

1. Improving core insurance activities (assessing, managing and transferring risks) by using IoT products and services for continuous underwriting, claims management and risk reduction. This goal was investigated in depth in our previous article, “Chloe and Insurance: A Love Affair.” 
2. Providing positive externalities to society, a topic more and more relevant due to the current focus on ESG investments (environment, social and governance);
3. Generating knowledge about policyholders and their risks. This knowledge has allowed carriers to insure current risks in a different way, enable up- and cross-selling actions and insure new risks;
4. Improving customer experience by interacting with them more intimately and frequently, moving beyond the traditional risk transfer. Many players are selling additional services for a monthly fee; others have found new ways to sell insurance coverages thanks to IoT.

Partnerships are a key differentiator. Some insurers have recently announced bold initiatives to use an ecosystem to expand their reach. One such insurer is Nationwide, which recently disclosed its partnership portal, where it exposes its services and protection products – including auto usage-based insurance (UBI) and connected homeowner insurance – to partners. 

With more than half of insurers delivering on their core systems modernization projects in recent years, it’s time insurers leverage data coming from their core systems to grow their business. By integrating IoT devices data to the core system data and leveraging this data fusion, insurers will have the opportunity to build a more holistic view and understanding of their customers and their risks. Insurers will be able to build a sort of digital twin of the customer, then tailor their services and offerings and improve the customer experience. Insurers will also overcome business lines silos, enabling upselling and cross-selling. 

Many senior insurance executives acknowledge that the world will be more and more connected, but, even with ecosystems as a topic on the agenda, IoT has not been exploited in business processes in a meaningful way. To lead an IoT-based business transformation, a clear vision and a structured and well-communicated plan are necessary. 

Technology is one of the key enablers of this transformation. However, insurers will have to carefully investigate, determine, prioritize and experiment with a range of IoT business use cases to develop an IoT-based business model. Many insurers are exploring a range of scenarios beyond connected cars, including connected homes, health and lives, infrastructure, factories and transportation. A comprehensive approach to help insurers build out the required capabilities for IoT is below. This insurtech approach takes insurers from business model definition to vendor partner strategy, to platform implementation and finally to IoT insights across the insurance value chain.

A main challenge for insurers is building the technology architecture to aggregate, normalize and analyze data to make it available for the IoT platform. A big question for many is: How do I get started? An effective way to develop your architecture is by leveraging frameworks.

The framework below breaks down the broad portfolio of technology components, services and capabilities. The components are arranged in three layers – Edge, Fog and Cloud computing – addressing where data should be stored and processed for speed, cost and effectiveness, depending on the type of data and purpose of the data. 

The collection of managed and platform services shown in the framework, across Edge and Cloud computing layers, connects, monitors and controls IoT assets and the processes that generate data for insights and analytics. These services work together across multiple layers that include the IoT ecosystem — such as sensors, devices and industrial sensors — and connect to the computing infrastructure at Edge, Fog or Cloud, persistently or intermittently. 

Data collected by the IoT ecosystem is then processed and analyzed at the Cloud layer, along with enterprise data sets such as on customers, policies, claims and billing. All of this data forms the inputs to the digital twin, which can then be turned into actionable outcomes using the latest computing techniques. 

For insurers that are currently investing in IoT, and for many more that are considering doing so, this framework can help guide your approach and provide a strong architectural foundation.

See also: Global Trend Map No. 7: Internet of Things

As new waves of technology or sudden social shifts bring disruptions or opportunities to the industry — similar to telematics or digitalization — insurers must capture opportunities rapidly. Insurers that can reinvent themselves by leveraging data, including from the IoT, and form ecosystems will win.  

After all, the digital economy is a “made for me” economy, and the digital twins allow insurers to tailor insurance experiences. Customers will reward organizations that understand their needs and provide them personalized value. 

There are already examples of successful insurers – in different business lines and different geographies – that have effectively integrated IoT. Their stories mastering usage of IoT is an achievable target without investing hundreds of millions of dollars, but instead by leveraging the right partnerships.