Much of North America is seeing lower interest rates across the board, which bodes well for consumers making large purchases but puts the insurance industry under intense scrutiny. Carriers with bond-heavy portfolios may see a decline in returns and, as a result, lower profit margins. Despite the insurance industry’s overall acceleration toward technology in 2020, carriers of all lines of business will need to move much more quickly – or risk falling even further behind their profit margin.

Insurers should cut unit costs, but not corners.

Insurers must cut costs; however, with more consumers requiring personalized attention from their insurance company, insurers must walk a fine line. Reducing expenses may be necessary, but insurance companies must be careful not to lose their existing customers in the process. Automation -- especially a newer form, called cognitive process automation (CPA) -- allows for reducing costs while still providing the service that customers require.

In some departments, such as underwriting and billing, insurance companies should prioritize responsivity for a more convenient customer experience. This can be done by using process automation to streamline communication between the carrier and the policyholder.

In other departments, such as claims, policyholders will appreciate careful and attentive human interaction. While responsiveness is still paramount, customers will have more trust in the company’s claim-handling process when they have access to a dedicated claims adjuster. 

Where resources are scarce, technology is a viable solution.

Even prior to the impact of COVID-19, carriers like Protective Insurance had begun implementing CPA, a more advanced version of robotic process automation (RPA).

Many carriers have at least discussed the features and capabilities of RPA. However, RPA and even intelligent process automation (IPA) products are primarily limited to structured data. 

CPA is the new disruptor in both the insurance and automation industries. Combining the repetitive abilities of traditional RPA with artificial intelligence (AI) and machine learning, CPA relies on bots that capture data and scan documents via optical character recognition (OCR) but that also do much more. The bots can fully automate entire underwriting and claims processes, from start to finish, with minimal human intervention.

See also: 20 Issues to Watch in 2021

As an example, policy underwriting has traditionally been considered a manual undertaking, but CPA has demonstrated that underwriting can be largely automated -- everything from policy submission to risk rating and underwriting to issuing declinations and binders. Using CPA, insurers can write more new business, streamline the renewal process and even detect cases of potential fraud with minimal human supervision.

Claims departments can significantly reduce the manpower needed for largely repetitive processes. Bots programmed with CPA can fully automate the first notice of loss (FNOL) process, fraud investigations, benefits calculations and even payments. In fact, time-consuming processes like claims communications can be automated up to 95%. 

Employees can instead focus on more engaging tasks and provide better service on edge cases.

Increased efficiency is more remunerative than reduced overhead.

In cutting expenses, the matter of efficiency is sometimes overlooked. If time is money, shouldn’t carriers condense time-consuming processes, as well?

Automation saves time and money. Whenever carriers optimize a process by implementing an automation solution, a precious resource has been created: time. 

As the policyholder mindset continues to grow in favor of more personalized experiences, cognitive automation allows insurance carriers to use their best asset – their human workforce – to focus on retention efforts, customer satisfaction and even cross-selling additional lines of business. At its heart, insurance is a people-focused business, and even tech-friendly consumers prefer personalized human interactions.

The takeaway

With lower interest rates threatening profit margins, insurance carriers must target cost reductions – and sooner rather than later. Companies can use new process automation tools, such as RPA and CPA, to cut redundant work often found in underwriting and claims departments. Insurers are then able to reprioritize the focus of their workforce on customer retention, or even scaling for growth.

Open insurance is about sharing vast and ever-growing volumes of structured data in a digital ecosystem to stimulate the creation of innovative insurance-related propositions for consumers. When customers are made the focal point of new digital business models, new opportunities continuously arise for cross-sectoral partnerships, platforms or collaborative efforts. 

This means that it is crucial for insurance companies to allow third parties (e.g. banks, fintech, aggregators, mobility providers, etc.) to access their data, products and services, and also for them to be present – and relevant – in their customers’ digital ecosystems. Like open banking, open insurance initiatives drive API-enabled access to insurance data, products and services. 

The Open Insurance Monitor presents how the insurance industry is developing towards open APIs

A rich API portfolio supports the best service provision toward customers and partners within third-party platforms. It is also important for insurers to offer a good developer experience to create the optimal environment for collaborative partnerships and innovation. INNOPAY, a consultancy, has launched the Open Insurance Monitor (OIM) to continuously measure and benchmark this functional scope of APIs and developer experience offered in the insurance landscape (see figure 1). OIM considers organizations around the world that publish insurance APIs via developer portals, including insurers, insurtechs and banks. 

Three key insights from the Open Insurance Monitor

1. Lack of focus on developer experience

The OIM reveals that insurers’ first efforts are mostly aimed at establishing a rich API portfolio with insurance-related functionality, with minimal focus on the developer experience.

The top left corner of Figure 2 shows several insurers leading the way as innovators of functionality. AXA offers a wide variety of functionality in most components of the insurance value chain and for multiple types of insurance products. These services include quoting and selling insurance, claims management and service-provider support during the execution of services to clients. Cover Genius also offers services in multiple components of the value chain, including services for product origination as well as claims management. Health insurer Humana provides a wide variety of API services such as enrollment in medical care programs, retrieval of medical information and supporting functionality for medical professionals during the execution of services. 

Analysis reveals that insurers are still only in the early stages in terms of creating the developer experience. Although most insurers have taken initial steps in providing API documentation, there is a strong focus on the technical aspect or specifications of APIs. The developer experience could often be further improved by increasing developer usability (e.g. tools, tutorials) and engaging with the community to spur collaboration and innovation.

See also: Designing a Digital Insurance Ecosystem

2. Banks are making inroads, too

Unsurprisingly, the banks included in the OIM offer a more advanced developer experience due to their open banking efforts and investments. Extending their API portfolios with insurance services would further boost their bancassurance models. 

The OIM identified a small group of banks that offer insurance services through APIs. This is the next wave of bancassurance and is an interesting revenue model for open banking. Thanks to their open banking capabilities, the banks included all have a solid basis in terms of API documentation and developer usability. Standard Chartered sets itself apart through features for community development such as regularly posting news articles and organizing hackathons and other types of events. OCBC emerges as a good all-round player in all components of developer experience, while Citi stands out in terms of developer usability by supporting fast onboarding and providing instruction guides for calling APIs, authentication and the sandbox environment. However, the scope of insurance-related functionality at these banks is still limited. If they decide to extend their API portfolios with related services, they will move toward becoming masters in openness, which will boost their bancassurance models.

3. Insurers are lagging behind in openness

Benchmarking against the masters in openness (e.g., National Bank of Greece and Deutsche Bank) reveals that insurers still have a long way to go in terms of openness. In fact, out of all the parties analyzed, only one is currently a master in openness: the U.S.-based insurance company Nationwide, thanks to offering a variety of insurance APIs plus enhancing the developer experience through clear documentation and good developer usability.

Nationwide’s extensive API portfolio currently consists of a variety of services for information retrieval, insurance quoting and issuing policies as well as APIs aimed at policy servicing. Portfolio extension could be achieved by including API services for managing claims and supporting service providers. Besides providing clear technical API documentation, Nationwide sets itself apart from other developer portals by emphasizing the business potential of its APIs through feature display and use cases, as well as offering good developer usability. 

No overall winner

As with open banking, there is currently no overall winner in the open insurance landscape based on the developer portal capability model, as depicted in Figure 3.

See also: 2021: The Great Reset in Insurance

For more details on the monitor and how to get access to digital ecosystems, please visit the website.

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.