Now that we seem to have reached a tipping point and will see far faster adoption of electric vehicles, a key question arises for insurers: Will they be safer or riskier?

In theory, they'll be safer, if only because the massive battery power will allow for a profusion of sensors and related safety devices. But we all know how theories can play out in practice. So, Cambridge Mobile Telematics took a hard look at performance to date and found that the answer is... a definite maybe. 

The factors involved are complex, and the stakes are high, so it's worth taking a look at the countervailing forces they've identified and at their analysis.

On the one hand, the CMT report found that the greater acceleration possible with electric motors is leading to much higher risks from fast acceleration and from cornering for electric vehicles (EVs). Tesla drivers, for instance, were found to have acceleration risks that were 3 1/2 to four times higher than drivers of vehicles with internal combustion engines (ICE). Tesla drivers had 76% higher risks from cornering.

Tesla drivers also speed 7% more than ICE vehicle drivers -- though hybrids speed 18% less, and compact EVs speed 24% less.

Those high-risk behaviors don't, in fact, translate into higher accident rates for Tesla drivers -- but do for those seeking extreme performance. People who own both a Tesla and an ICE vehicle were actually almost 50% less likely to crash their Tesla, on a per-mile basis. Drivers of Porsche EVs, though....

The report says: "Porsche drivers are 129% more likely to accelerate, 40% more likely to hard brake and 15% more likely to speed in their Porsche. Porsche drivers are also 55% more likely to crash while driving their Porsche," than while driving their ICE vehicle.

The issues get more confusing when you start looking at variables that could change considerably over time, especially as the performance and range of batteries improve. At the moment, "the average trip for a compact EV is 10% shorter in time and 26% shorter in distance than ICE vehicles," the report says. The shortening of trips is even more pronounced in cold weather, when EVs lose 10% to 20% of their range. But battery technology is improving rapidly. 

Gasoline prices are another wild card. With the surge over the past two years, the percentage of trips taken in a Tesla by those with both a Tesla and an ICE vehicle has risen from 63% of the total in January 2021 to 70% now. But the surge in prices has been subsiding -- and nobody can know where they go from here, unless you somehow have figured out what happens with global supply chains and with the Russian invasion of Ukraine. 

As driver behavior, safety features, battery performance, gasoline prices and other factors sort themselves out, EVs may well turn out to be less likely to crash than ICE vehicles -- or not.

In other words, stay tuned.

Cheers,

Paul

P.S. If you want some detail on why I believe we're reaching a tipping point in terms of EV purchases, here is what I wrote on the topic in February. The clean air incentives in the Inflation Reduction Act that the Senate passed over the weekend should speed the move toward EVs. 

It is no small task to insure coastal properties. Coastal home and business owners need adequate protection now as much as ever, especially as climate change leads to more severe and frequent storms. Unfortunately, the nature of risk – especially catastrophic risk from hurricanes – has become harder to predict using traditional underwriting tools.

Florida's new $150 million “My Safe Florida Home” legislation and the "predict and prevent" philosophy it embodies, mandate that insurers provide policyholders a detailed explanation for denying coverage and offer free inspections and up to $10,000 in matching grants for home improvement and hurricane retrofitting projects. This new legislation aims to build a better future for Floridian homeowners, but insurance companies need the right tools and technologies, such as a property intelligence platform equipped with geospatial imagery, predictive analytics, and artificial intelligence (AI).

Banks, insurance companies and employee benefits providers have taken massive leaps throughout the pandemic to better personalize their offerings and deliver additional value to plan members. But the work is not over.

Now the metaverse, a network of 3-D virtual worlds focused on strengthening social connections, is touted as a solution to many of the workplace woes that have characterized the pandemic era. (Zoom fatigue, anyone?) Metaverse evangelists claim that deeper interactions with technology through virtual and augmented reality will have a transformational impact on distributed workforces.

Insurers and banks – already stretched thin by the demands of digital transformation – are wondering if investment in this new tech will pay off.

While the metaverse is still largely theoretical, there could be several ways group and voluntary benefits providers could further capitalize on existing trends using this emerging technology. Let’s speculate!

Is the metaverse mostly hype?

Metaverse hype exploded following Mark Zuckerberg’s highly publicized announcement in October 2021 that Facebook’s name would change to Meta and that the company’s strategic direction would shift to building out the metaverse. 

However, the concept of a virtual 3-D space where people meet, play games and conduct business using virtual and augmented reality devices goes much further back. The term “metaverse” actually emerged in the early ‘90s, and Meta is by no means the only major player in the space. Tech giants Google, Apple and Microsoft have all invested heavily in metaverse development, with foundational research going back several years. Virtual properties are being snatched up for tens of millions of dollars.

Certainly, the metaverse has no shortage of skeptics, and for good reason. The hype has often been over the top. But there is something there. Savvy financial services giants like JP Morgan and American Express have already claimed expensive digital real estate in the metaverse. 

There is serious movement in this space that should not be dismissed by insurers -- even if the VR headsets do look a little silly.

How Can Employee Benefits Insurers Profit in the Metaverse?

Extending virtual health offerings

Since the pandemic, health insurers have experienced increased demand for virtual health and wellness services, such as telemedicine. By delivering these services through the metaverse, insurers could capitalize on trends while offering a more immersive experience.

Several technology providers, such as XRHealth, already offer metaverse-based virtual telemedicine services that user avatars can access at any time of the day.

Remote workers could also participate in mixed-reality wellness programs and fitness classes alongside their colleagues. This can promote healthy living and build rapport among distributed workforces.

Life and health insurers could integrate metaverse services into their benefits. Insureds would then interact more closely with the carrier’s brand. 

Avatars and gamification offer richer experiences

Carriers are already deploying AI-based technologies like digital assistants to deliver timely and contextual customer service and coaching. The metaverse presents an opportunity to extend these capabilities by connecting with a user’s avatar. 

Avatars represent an individual’s entire digital identity in a far richer way than we’ve seen previously. As people conduct more “in-person” activities using avatars, insurers will have more data than ever to develop personalization strategies. 

Carriers can leverage metaverse gamification to deliver rich experiences. For example, Cigna partnered with Microsoft to create a mixed-reality digital health tool called the "BioBall." The ball is a handheld, electronic orb that works with a virtual reality headset to navigate users through an interactive game while collecting their health metrics and flagging potential risks.

That’s a little more exciting than an online form!

Voluntary benefits tailored to the metaverse

Depending on how ubiquitous metaverse technology becomes, underwriters may very soon have troves of new data at their fingertips to more accurately and quickly evaluate a plan member’s risk. 

Using digital enrollment platforms, sales teams can recommend additional voluntary coverage based on a plan member’s activity level and other data points tied to their user avatar. This data may not be available through more traditional methods.

Additionally, popular voluntary benefits like identity theft protection will have a more important role to play in the metaverse. When a plan member’s identity can be tied to his or her digital avatar, that may create a bigger risk of identity theft.

New voluntary products tailored to distributed workforces “residing” in the metaverse may be commonplace in the next five years.

See Also: Beware the Metaverse

Responding to real-world, metaverse-related health risks

As plan members increase their usage of metaverse technology, they may be increasing their exposure to additional real-world risks. Metaverse distraction could be dangerous.

Some insurers are already responding. Seguro GO is an insurance policy designed to protect Pokémon GO players from accidents, thefts and injuries that may occur while players interact with the popular, augmented-reality game. 

Lengthy interactions within the metaverse may expose insureds to chronic health conditions tied to a sedentary lifestyle. With tools like digital coaches, insurers can establish a positive foothold in the metaverse and improve plan members’ physical wellbeing.

Insurers should also consider the mental-health risks users are exposing themselves to by protracted stays in the virtual world. We saw firsthand the mental health crisis that unfolded during the pandemic as screen time went through the roof. Will the metaverse really be any better?

Life and health insurers should pay close attention to how users interact with the metaverse and design wellness products and programs that encourage healthy digital habits, perhaps by leaning into the metaverse itself.

Will the metaverse disrupt employee benefits?

Inevitably, new insurtechs will exploit the metaverse, posing potential risks (and partnership opportunities) to more established players. At present, there is no way of knowing whether they will be successful in disrupting the industry, or even if the metaverse itself will be a success in the long run. Today, we are just speculating. 

The metaverse could have significant implications for traditional benefits distribution models. As carriers deploy customer-engagement strategies across the metaverse, it is likely that direct-to-consumer sales will increase further than they already have in recent years due to the more immersive brand experience offered within metaverse applications.

The metaverse won’t disrupt the fundamentals of the insurance business, but it will almost certainly change the relationship between insurers and insureds. As the technology matures, benefits carriers can offer more robust digital coaching, personalized benefits selection, collaborative digital wellness programs and new products to meet the coverage demands of metaverse denizens.   

Artificial reality, AI, gamification and other elements of the metaverse aren’t new. Whether you call it the metaverse or not, technology is changing rapidly, and it’s hard to predict where it will take us. Leaders of benefits insurers must help their companies stay on top of trends and positioned to take advantage of the metaverse and evolving tech.

Some of the largest insurance carriers no longer pay ransoms. The Office of Foreign Asset Control (OFAC) has deemed many hackers terrorists, making it illegal for insurance companies to pay their demands. The insurance industry faces pressure to shift its solution from ransom payment to incident recovery as more businesses request coverage. And while the war in Ukraine has caused a slowdown in ransomware attacks, experts predict these attacks will soon come back with a vengeance. It's time to think about solutions that benefit the insured and the insurance industry.

Hackers are getting smarter, and their targets are getting smaller. In Q4 of 2020, the median size of companies that incurred ransomware attacks was approximately 235 employees. In Q2 of 2022, the median company size dropped to 105 employees. Small and medium-sized organizations with limited cybersecurity resources are easier to breach and often rife with data that can be ransomed or sold on the black market. Also, hackers know that breaching organizations of this size receives less attention from law enforcement. Still, any company that uses mobile technology, engages with external partners or vendors, accepts credit cards or other forms of online payment or stores confidential customer, partner or other digital information is susceptible to a cyberattack and, therefore, in need of cybersecurity insurance and strong cybersecurity measures.

According to Sophos' report, "The State of Ransomware 2022," ransomware attacks are happening more often, doing more damage and growing more sophisticated. Last year, 66% of surveyed organizations were hit by ransomware -- an increase from 37% in 2020. Last year, businesses experienced 50% more cyber attack attempts each week compared with 2020. Equally alarmingly, the average ransom payment increased from $84,000 in Q4 of 2019 to over $800,000 in 2021. What's more, the increasing ubiquity of cybercrime leads to more claims, so, when a business is attacked, they run the very real risk of facing a longer recovery time as insurance companies and incident responders' resources are stretched thinner with rising demand.

It will take more than raising premiums and putting more limits on the businesses that can qualify for cybersecurity insurance to prevent increased claims and higher costs; insurers must insist their clients be aggressive about cybersecurity protection. This doesn't mean simply installing off-the-shelf cybersecurity products that don't fully protect businesses from sophisticated threats. Businesses may be at greater risk than they realize. The costs to recover and rebuild after a ransomware attack include more than hiring a qualified incident response team. Companies must also factor in downtime, lost data, customer service and exposing customers' confidential data, all of which could be devastating.

See Also: Why Hasn't Cyber Security Advanced?

Thankfully, there are 10 critical cybersecurity components insurance providers can recommend to reduce their customers' cyber risk levels, including confirming the implementation of:

  1.  Multi-Factor Authentication (MFA): Too often, companies rely solely on single authentication tactics like Touch ID. However, smart devices can recognize more than one thumbprint, and even fake fingerprints can successfully bypass sensors at least once nearly 80% of the time. While not an entirely bulletproof solution, MFA effectively creates additional hurdles for would-be attackers. Confirm that your customers practice MFA, even if it simply involves the additional authentication step of sending a one-time SMS to a trusted user's device to ensure they're a valid user.

  2.  Endpoint Detection and Response (EDR): EDR is an endpoint security solution that continuously monitors end-users' devices to detect and respond to cyber threats like ransomware and malware. Urge your customers to seek out EDR solutions that provide these four critical capabilities, according to Gartner: the ability to detect security incidents, contain the incident at the endpoint, investigate security incidents and provide remediation guidance.

  3.  Immutable Backups: Unlike conventional data backups, immutable backups are files that can't be modified in any way. In the event of a ransomware attack or other data loss event, your customers can rely on immutable backups to instantly restore their assets and maintain regulatory data compliance requirements -- without having to pay any ransom fees to get their (likely compromised) data back.

  4.  Managed Detection and Response (MDR): MDR is a cybersecurity service that combines technology offerings and (human) expertise to provide threat hunting, monitoring and response. By helping your customers engage with MDR services, you can support them in quickly identifying and limiting the impact of cyber threats, and they won't need to hire additional, costly security staff to do so.

  5.  Patch Management: Patch management involves identifying, acquiring, testing and installing software patches (or code changes) that are intended to fix bugs, add features or address security vulnerabilities. Many businesses forgo patching their systems, assuming doing so could disrupt critical application integrations. However, failing to patch a system's vulnerabilities creates an open door for hackers to enter and wreak havoc.

  6.  Employee Awareness and Training: A company's cybersecurity is only as strong as its weakest link, and all it takes is one employee -- even a well-intentioned one -- to cause that chain to break. Make sure your customer organizations have employee awareness and training programs in place that formalize and enforce cybersecurity best practices, such as the use of strong passwords, MFA and accessing sensitive files only from trusted devices, for instance.

  7.  Privileged Access Management (PAM): PAM is a security mechanism used to identify, authorize, manage and monitor privileged users across an entire organization. By ensuring your customers are using PAM tools, you can help them deliver secure, privileged access to critical assets while also satisfying key compliance requirements.

  8.  Data Encryption: One of the most effective data security methods, data encryption translates data into another form so only users with access to a secret key or password can read it. By ensuring your customers encrypt their data, you can help them protect their private information, sensitive data and the communication between their applications and servers.

  9.  Email Filtering: Email filtering services check all incoming and outgoing emails for spam, malware and suspicious links, and then organize these messages into respective categories and folders. Implementing email filtering is an easy and accessible cybersecurity best practice that can ensure risks like phishing emails and malware never appear in your customers' inboxes.

  10. Attack Surface Monitoring: Attack surface monitoring involves the continuous identification and monitoring of attack vectors across an organization's entire IT infrastructure. Most importantly, it's done from the perspective of an attacker. Make sure your customer organizations regularly perform vulnerability scans and penetration tests to verify their actual attack surface. It's dangerous for companies to assume they have all their attack surfaces covered!

Experts predict a new wave of cybercrime and increased attacks on smaller businesses. Now more than ever, your customers need help reducing cyber risk, and, as their insurance provider, you're in a unique position to provide trustworthy support today. Confirm that your customer organizations (as well as your own!) are implementing key cybersecurity best practices and receiving support from experienced cybersecurity professionals that offer state-of-the-art services. The prosperity and longevity of your and your customers' businesses depend on it!

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From the Editor: The Biggest Issue Facing Workers' Comp As tough as staffing is throughout the insurance industry, the world of workers' comp has things doubly bad -- it has to deal not only with its own staffing issues but those of its clients.  As Mark Walls, vice president of client engagement at Safety National, explains in this month's interview, many companies are having to ask employees to do more to cover for gaps in staffing. Companies are also being less rigorous about pre-employment physicals and may rush people into action. The risk of injury is rising as a result. At the same time, workers' comp carriers and third-party administrators are having to deal with their own shortages of adjusters, nurse care managers and so on, while dealing with caregivers that are struggling to line up enough doctors and nurses. The result? Not pretty. There's no easy answer, but carriers and TPAs are finding that they are more likely to attract and retain talent if they provide flexibility by letting people do more work at home.  Work-from-home creates its own set of problems, though, for all employers. I'll leave it to Mark in his interview to explain the subtleties, but suffice it to say here that it's important to designate an area for work in the home. Otherwise, any activity at any time in any spot in the house could be interpreted as being related to work, meaning that any injury could produce a claim.  The good news is that the long-term trends in workers' comp remain positive, as carriers and employers keep making work environments safer. Automation will also reduce risk.  But combined ratios have climbed from perhaps 85% to 100% or more, and any uptick in claims and expenses would send them even higher. So, the path ahead may be bumpy for a while. Cheers, Paul  Read the Full Interview READ MORE Threats, Openings for Workers' Comp What if external factors overwhelm the workers' comp system's century-old ability to balance the rights of employees and employers? Read More 'Scalable Compassion' in Workers’ Comp As much as claims representatives want to help individuals, there has been no feasible way to provide compassion at scale. Read More Smartest Idea for High-Hazard Businesses When an employee says they’re too tired to finish a physically demanding task and need to rest, that needs to be okay. Read More Case Study on Using AI in Workers' Comp Taking in extra data points and thinking in a different way has let us make better decisions about how to route claims, and more. Read More Identifying Fraud in Workers’ Comp One of the best tools for fraud prevention is to let employees know that false claims will not be tolerated and that penalties are stiff. Read More Should Workers’ Comp Be So Litigious? It’s time to dedicate resources on several fronts to get back to the original intent of the workers' compensation system. Read More FEATURED THOUGHT LEADERS Richard Victor Thomas Ash  David Fontain David Bacon  Stacey Gunn Greg Moore View all ITL FOCUS topics

Between 3% and 10% of total U.S. healthcare spending each year amounts to fraud, according to the National Healthcare Anti-Fraud Association. Facing potential multimillion-dollar losses, insurers invest significant resources into weeding out fraud and protecting their financial interests. 

Conducting a healthcare fraud investigation is a laborious process that can take weeks at the minimum. Most of that time is spent sifting through information from multiple sources, piecing it together like a jigsaw puzzle to reveal a complete picture of the provider-member relationship. Only a small proportion is dedicated to actual decision-making. 

But it doesn’t have to be that way. 

Dynamic problems vs. manual solutions 

Healthcare fraud is a dynamic problem, with fraudsters constantly changing their tactics to evade detection. Yet many insurers are tackling this growing issue using only manual tools – multiple databases, pivot tables and Excel spreadsheets – which are difficult to adapt to new scenarios. 

Unsurprisingly, this approach leaves insurers constantly playing catch-up with fraudsters, as investigators spend days manually looking into cases. In the current environment, some fraud is missed by insurers, while false positives are too frequent. Plus, when an insurance investigator does spot something fishy, they often lack the data to validate their findings. 

Evidence suggests that the pandemic and the rise of telehealth led to an increase in insurance fraud – as stringent telehealth rules were relaxed overnight, enabling providers to bill insurers for an enormous range of medical services provided virtually. 

So – in this increasingly digitized healthcare landscape – it’s clear that insurers must modernize their back office to protect against healthcare fraud. That’s where artificial intelligence (AI) and machine learning (ML) tools come in. These technologies enable insurers to create dynamic solutions to a dynamic problem. Here’s how. 

How AI Helps

AI enables insurers to optimize employee time. Rather than spending tens of days on research, caseworkers can rely on AI to analyze relevant, contextualized data and generate alerts accordingly. This both increases the efficiency of insurance investigators and improves customer experience. 

Consider the telehealth example. Imagine you’re an investigator working with rudimentary manual tools like pivot tables and Excel spreadsheets. You might spend hours assessing providers’ telehealth billing data before finding one that seems to be filing a suspiciously high number of claims. Bingo – you've spotted a fraudster! Not so fast. After embarking on a lengthy investigation, you find that this "discrepancy" can be attributed to the way the provider in question is coding a certain category of services. False alarm. 

Though the insurance investigator in this scenario acted completely reasonably, they inadvertently wasted significant resources chasing this dead-end lead. If this investigator had been equipped with AI-powered analytical tools, the same billing data could have been processed and interpreted in minutes – rather than hours. What’s more, AI would’ve produced significantly fewer false alarms, enabling the investigator to dedicate their time to following up on genuine leads – which means even more time saved.

AI’s second superpower is uncovering trends or patterns that simply aren’t easily recognized – thereby revealing hidden fraud. 

Let’s take the example of prescribing controlled substances via telehealth consultations. Rules governing this practice were significantly relaxed during COVID-19. As a result, this area of prescribing has become a fraud hotspot. This has both financial implications for insurers and potential public health ramifications – for example, if fraudulently prescribed opioids were to be sold on the streets.

AI is great at uncovering fraudulent prescriptions issued via telehealth. It can quickly spot suspicious prescribing patterns by aggregating all relevant data – including relationship analytics between members and providers. Has there been a spike in prescribing? Have these members been issued this prescription before? If not, why now?  

What’s more, AI can analyze huge quantities of publicly available data from the internet – forums, social media, Google reviews and more. For instance, if a physician was at a conference or on vacation during a particular week, how could they possibly have completed several dozen telehealth consultations each day?

See Also: Can AI Solve Health Insurance Fraud?

Striking the right balance 

This isn’t about replacing special investigations units with AI. Instead, it’s about enabling employees and AI tools to work together symbiotically. 

There’s no doubt that AI can analyze data in a fraction of the time it takes a human analyst – and with a much higher degree of accuracy. However, healthcare is – and will always be – a people business. Complex, life changing decisions made each day by insurers on the behalf of members and providers will always need human input. 

Let’s take the example of "fraud" perpetrated by healthcare providers. This falls broadly into two categories. The first category is composed of otherwise-well-meaning providers who might sometimes round up appointment times by 10 or 15 minutes and receive a higher insurance payout. The second category is made up of serious fraudsters, who consciously and consistently set out to defraud insurers. 

With AI and human employees working in tandem, insurers can easily uncover both groups. But they can also retain the freedom to handle the issue with compassion – punishing genuine fraudsters, while educating providers who’ve committed minor abuse.

That’s why – without a hint of irony – it seems obvious that AI is exactly what’s needed to bring humanity back into healthcare fraud investigations.

After almost 15 years of increasingly effective technological developments in the AI/ML arena, we are at a point where the algorithms and the trained models are well-known and the overall architecture of neural nets is well-understood. However, the quest for reasonable ROI from AI/ML projects continues, with data issues impeding wider adoption.

The current and still evolving technologies coined loosely under the term data-centric AI could help many industries tackle issues with data and make help make meaningful progress in reaping benefits of AI/ML sooner than later.

Insurance as an industry should take the lead in adopting data-centric AI technologies in providing better customer experience to the insureds.

Issues with data

Many of the following have been well-documented and are issues for which solutions have been emerging for some time: 

• Sovereignty and regional aspects
• Privacy and security
• Accuracy
• Bias and explainability, responsible and ethical AI/ML
• Interruption
• Unavailability of large data sets
• Bespoke model training

Adhering to aspects of data like sovereignty with all the current and emerging regulations requires insurers to essentially train AI/ML models in country or in some cases in the region where the models will be used to predict business insights. Because data is indicative in the context of regional and local market dynamics, AI/ML models should be trained locally for avoiding bias and making them explainable in the local context.

Another issue is that the pandemic caused major disruptions to business data, essentially weakening the efficacy of pre-trained models. This led many insurers to spend time and effort in re-training deployed models, as many of them built bespoke models in-house. 

See Also: The Data Journey Into the New Normal

What Is Data-Centric AI?

It is loosely defined as AI/ML that depends on data that is engineered to a) account for domain-specific nuances while also factoring in the regional/local context, b) handle regulatory aspects like the appropriate amount of anonymization, c) remove bias from data that is used for training, d) depend on smaller but relevant data sets when large data sets are not available and e) potentially use synthetic data that is generated by tools that try to maintain statistical similarity to real data.

This engineering of data goes beyond the traditional sourcing, cleaning and basic, algorithm-related tuning that happens today. Increasingly, tools to help visualize and engineer data are appearing in the market.

The data-centric AI and related tools are aimed at enabling business domain experts who can manage AI/ML initiatives without the need for a large team of data scientists and IT experts.  

What should the insurance industry do?

The insurance industry should look at data-centric advances in AI/ML and take the opportunity to lead in providing a better experience for insureds. Here are some suggestions for insurers as they embark on and in some cases re-look at their current AI/ML initiatives:

• Depend on foundational models
  • There is a growing movement in many industries to depend on pre-trained models and use them as foundational elements to improve efficiency in the context of a specific entity. This includes training specific areas that need improvement. This is as opposed to re-training the entire model. 
• Use smaller but relevant data sets
  • The insurance industry is rich with data; however, it Is not at the scale that consumer-facing entities collect data that enables them to train machine learning models for increasingly better accuracy. Moreover, there are questions for which the answers are not clear or are evolving. Who owns the data? Can the data be used for analysis? To what extent is the industry comfortable with anonymization technologies?

In this context, Insurers should start looking at engineering the small but relevant data sets that are easily available and can help improve accuracy of the models.

• Evaluate use of newer anonymization technologies
  • Technology that allows advanced analytics on encrypted data are maturing and should help insurers build business cases that involve their partner data.
• Build data engineering organizations – not just IT teams
  • AutoML technologies help move the skill gap in using AI/ML to the left, meaning knowledgeable business analysts should be able to do most of the tasks of a data scientist. AutoML technologies have traditionally not focused on allowing non-IT teams to engineer data, but tools for helping them do so for better model accuracy are emerging and are increasingly contributing to the data-centric AI movement.
• Use synthetic data selectively
  • Tools that generate synthetic data to supplement the smaller data sets that insurers depend on today are gaining traction. While many of the tools may not generate data that removes bias and do not necessarily maintain statistical integrity of the data that is required for effective models, they are a good start. At the outset, an easier way to start using synthetic data is to apply on a subset of the AI/ML system where there are issues with accuracy.
• Train models regionally
  • Increasing regulations on data in many countries may necessitate the training of AI/ML models in-country or in-region. This has the added benefit of reducing bias in training, probably will make it easier to explain the decisions put out by the algorithms and may be more accurate. However, in the past, scalability of training models locally and regionally has been an issue. With data-centric AI/ML tools augmented by AutoML tools, insurers should be able to set up a highly efficient business operation in training models locally.
• Build a framework and governance for responsible and ethical AI
  • The EU is leading the way in helping define a framework for responsible and ethical AI. Insurers should review their output and look at data-centric AI technologies as the foundational elements to define a bespoke framework for their business and set up governance to prevent and mitigate liabilities resulting from their use of AI in making business decisions.

Conclusion

It is imperative that insurance as an industry gets beyond the hype of the "I" in AI and becomes more pragmatic in their use of AI/ML technologies for generating business insights. The recent evolution of AutoML technologies helped shift the required skills to the left and reduced dependency on data scientists and IT teams. However, many of the issues with data necessitate the rethinking of the use of AI/ML in a data-centric way, helping business domain experts to engineer data and address many of the macro-issues and in the process, improve efficacy of trained models and keep them relevant in their continued use over a period of time.

The insurance industry is exploring ways to adopt technologies to minimize risks, provide personalized solutions, reduce costs and ensure compliance. Leveraging hyperautomation solutions is a game-changer.

With heightened customer expectations, hyperautomation technologies such as low code play a crucial role in delivering the speed and flexibility to resolve diverse customer requirements and provide personalized solutions. Because the technologies can seamlessly integrate legacy systems, they offer solutions at a nominal investment compared with traditional infrastructural upgrades that deliver similar results. 

Furthermore, hyperautomation technologies allow various departments to work together to improve processing speed and efficiency. Process improvements and changes can be implemented faster than ever, leading to higher flexibility and enhancing customer experience. 

High Levels of Accuracy

Automation in insurance and risk management allows multiple processes to be carried out simultaneously without risking errors while making processes, including onboarding, claims and verifications, error-free and fast-paced. Delivering solutions at speed while being accurate is crucial to elevating the customer experience and reducing operational costs. 

Introducing software robots to take over mundane and repetitive tasks enables the department to remain functional around the clock without increasing the workforce. Workflows for intelligent case routing can assign claims to the proper personnel easier. At the same time, carriers can bring efficiency into critical areas, such as document checking and compliance. The system can pull scattered and disparate datasets and store claims into one central repository. Thus, the system enhances reporting and expands access to critical information at the right time for verification.

Further, upgrading the digital workforce could seamlessly implement changes in processes. By harnessing the power of core automation, carriers can reduce fraud losses and cut operating costs while improving underwriting accuracy.

To deliver a connected experience throughout the insurance life cycle, it’s critical to enable teams to work together efficiently and make informed, data-driven decisions while remaining connected with clients. Because of disparate technology and lack of automated workflows, insurers face several preventable issues like gaps in transparency, manual process and poor customer experience. 

Hyperautomation brings together multiple technologies and their capabilities, including Robotic Process Automation (RPA), Artificial Intelligence (AI), API Integration, Machine Learning (ML), intelligent business management software (iBPMS) and Process Mining to augment and automate processes in ways more important than traditional automation.

Without hyperautomation, core policy and underwriting systems often require manual, repetitive tasks and result in a lengthy quote-to-bind process. This can hinder growth and result in customer dissatisfaction. With legacy systems, the underwriting process is time-consuming and at risk of human error due to its manual nature, resulting in inefficiencies and more liability. 

By bringing data together, integrating systems and automating workflows, you improve connectivity with unified claims, underwriting and agent-customer experience. Your staff has the technology they need to do their jobs more effectively and efficiently, empowering them to deliver the digital, connected and mobile insurance experience that today’s customers expect.

See Also: Insurers Turn to Automation

Seamless Adaptability

Fully optimized claims triage, straight-through processing and fraud management are the hallmarks of process automation. As the insurance industry launches business products and services rapidly, low-code platforms allow businesses to capitalize on the opportunity by empowering people with no previous software experience to build enterprise-grade applications effortlessly. Integrating a new feature, for example, to provide dynamic quoting and pricing usually takes months. Low-code provides feature-rich, pre-built templates and drag-and-drop elements so users can build robust applications and solutions faster.

Enhancing Customer Experience

The fast-growing insurance industry gives rise to ever-increasing amounts of data, and insurers face a considerable challenge in collecting and handling customer data. With automation and advanced data analysis technologies, businesses don't have to spend months on data preparation and model selection. Automation extracts, cleans and processes volumes of data—policy, claims, records—and provides meaningful information. 

Whereas legacy processing is fragmented, automation unifies data processing and provides 360-degree customer insights for effective decision-making. Automation empowers carriers to offer more targeted and personalized products. Automated insurance processing provides companies with better contextualization that allows for meeting individual customer needs, which is not possible with an outdated, one-size-fits-all model.

Customers can enjoy dynamic pricing and omnichannel customer experience. Ultimately, it all comes down to customer experience, and automation delivers the promise.

In the highly competitive insurance market, where the importance of convenience and efficiency is increasing, automation facilitates holistic, seamless claims processing. From noticing the loss to final claims settlement, automation platforms make the process easier for everyone. It can deploy rich solutions for claims and renewals triage, risk management, fraud identification and auto-retrieval of policy information. When automation is embedded in the process, instant claims reporting is possible through responsive solutions.

By automating the insurance landscape, carriers can remove high costs across the value chain, striking a balance between retaining customers and maintaining profitability. Embedding automation capabilities can reduce margins and help insurers reinvent their entire business with significant cost savings.

The clouds shifted rapidly as I drove back home after helping my colleagues set up for the Future of Insurance conference in Chicago recently. The sky looked ominous, but I kept on jamming out to XM radio. I thought we'd just have a normal thunderstorm, until I saw an electronic billboard on I-90/94 reading, “Tornado warning in effect, tune into local radio immediately for more information.”

It was hard not to panic. Has the tornado landed near me? Is my family okay? Is there damage to my home? Who else might be injured?

Luckily, I made it home, and everyone I knew and loved was safe. Ultimately, the tornado never appeared, but there was some wind and storm damage sprinkled throughout the city.

The next day, I thought about the lack of clear and accurate communication regarding the storm. And, given that nothing happened, I would probably believe the next warning a little less if I wasn’t in the industry. I also found it odd that I was in a vulnerable state and my insurance carrier had not communicated with me at all. 

According to a 2019 paper by Khaled & Mcheick, more than 3,800 natural disasters have affected more than 2 billion people in the past decade, resulting in 780,000-plus deaths and at least $960 billion in damages. The frequency and severity of these natural disasters will continue to increase because of climate change, and insurers have been enlisted as one of the first responders on that chilling front to deal with the aftermath of these devastating events. We must do more, not only for our financial viability as an industry, but also to deliver on our promise to policyholders to protect what they value most. 

Warning People Before an Event

We have seen numerous cases, such as the tsunami in Sri Lanka, Hurricane Maria in Puerto Rico and wildfires in the Western U.S., where warning in the days, hours or even minutes beforehand could have saved lives and protected assets. Carriers have invested in advanced weather monitoring services, improved coordination with national agencies to understand the potential trajectory of storms, more efficient identification of affected policyholders and more flexible readiness plans to deploy CAT-related claims employees and third party administrators. But they still rely on antiquated communications channels with numerous friction points, such as sending emails that have low open rates or routing policyholders to glitchy websites to obtain needed information.

Instead, carriers could leverage texting to connect with policyholders faster. Texting is accessible, personal and ubiquitous. Reports show that 97% of adults text daily, and 99% of texts are opened. We also found this to be true for policyholder interactions with insurance carriers. In our study about insureds and texting, we found that 84% of policyholders would save a carrier’s texting number to their contacts and text them about a service or a claim, if offered that option. 

Carriers could also offer dedicated short codes (e.g., text “STORM” to 999-999 to receive hurricane-related updates) for policyholders to opt into and save to their contacts during the bind process. Policyholders could automatically receive timely information about catastrophic events if carriers integrate the service into their CRMs and weather monitoring services. The texts could also direct the policyholder to local weather sites for safety information or the carrier’s website for preparedness instructions for loss mitigation and prevention. 

Helping People During an Event

Texting is also a highly effective communication tool during an event. According to Khaled & Mcheick, there are many benefits of SMS. It: 

• Is available on any global system for mobile, or GSM, network.
• Is one of the first services to be restored if outages occur.
• Use minimal network resources.
• Lets message be stored on mobile phones and sent later when a network becomes available.

Texting also offers a critical way to get information from disaster response agencies at the state and federal levels (e.g., FEMA) to policyholders during an event. Carriers can amplify communication and relay these agencies’ messages to policyholders and provide GPS pins for shelters to get people to safe locations. And carriers can enable these one-way notifications to be two-way communication channels so that they can collect preliminary information on the extent of damages and capacity plan for the post-event claims. 

See Also: The Best Tools for Disaster Preparation

Assisting with Post-Event Claims

After an event, speed is critical for dealing with a high volume of claims and reducing fraud. Carriers can deploy texting solutions to capture first notice of loss (FNOLs). The texts can route policyholders to eFNOL channels and also take short-form FNOLs for high-frequency, low-severity claims that are likely to be paid out quickly. Also, texts can prompt policyholders to collect critical data, such as images or videos that can feed the claims model for triaging and payment.

If a policyholder needs to mitigate any damages, carriers can coordinate third parties and the policyholder via group texting. Or, if carriers need to get policyholders to hotels, pay-by-text can provide the first payment. Additionally, when an adjuster is assigned to adjudicate that claim, they can schedule an initial phone call via text instead of leaving endless voicemails and playing phone tag, which is critical for productivity given the large load. 

The current catastrophe communication process is not good enough, given what we will likely face in the coming decade. Carriers need to offer a more empathic, simple and convenient communication channel. By transforming and modernizing this process, not only will we protect our financial strength but also what our policyholders love.

High-resolution data is rapidly changing the cargo insurance paradigm—and it’s about time.

While brokers thoughtfully deliver individualized care to each client, the limitations of traditional insurance mean they’re still selling them the same set of insurance products.

With high-res data, however, they are empowered to dynamically create tailor-made products that address the unique needs of each client—all while minimizing pricing fluctuations and delivering stable costs to balance sheets.

In short, the way they do business is changing for the better.

Traditional underwriters are flying blind

Think traditional insurance leverages historical data to underwrite risks? Think again.

For hundreds of years, our industry has operated on the data it can acquire from a few sheets of paper and historical claims data. That’s about as low-resolution as it gets—an unsettling thought when you consider underwriters often quote out coverage with million-dollar price tags. Intuition and personal experience have long influenced pricing, as well.

This approach is ripe for error.

Past performance is not a good indicator of future performance, and human intuition is fallible — experience varies wildly from underwriter to underwriter, after all. Let’s also not forget that underwriters are human; they can and will make mistakes. Perhaps the data they’ve gathered is incorrect or incomplete. Maybe the events of the previous year, such as catastrophic storms or pandemic-driven supply chain disruptions, don’t reflect today’s environment.

With so many unknowns, it’s understandable that underwriters must price insurance conservatively—they have to ensure the sustainability of their business.

In the end, this approach has contributed to the wild peaks and troughs we often see in conventional insurance pricing—extreme swings that leave clients frustrated and price some out of coverage altogether.

See Also: Shipping Industry Safety Keeps Improving

High-resolution data paints a more accurate picture of freight industry risks

While the insurance industry has been slow to digitalize, the freight industry has long embraced big data approaches to optimizing routes, reducing losses and improving driver safety. As a result, the industry is awash in data.

Leveraging embedded insurance to gain visibility into this data, an AI learning engine like Loadsure’s is generating more accurate, sustainable pricing with a more holistic view of the risks. Where traditional insurance priced policies against the quantities and types of shipments and losses in the previous year, it’s so much more useful when premiums are also informed by the shipment-level details of those loads, like routes, reefer temperatures and G-loads on cargo—all while simultaneously synthesizing the claims history to understand the correlations where losses occurred.

This is only the beginning.

Soon, location, weather and national crime database integrations will feed data into our AI learning engine, as well. This data may be meaningless when pricing traditional, annualized policies, but with transactional coverage it becomes both valuable and actionable. Automation and AI allow dynamic pricing models to be refreshed with new shipment and environmental data as often as need be. We can then bring together historical and real-time data and adjust on the fly. By catering to specific instances, brokers are empowered to deliver every customer a bespoke product.

Where traditional insurance could only see a shipment of glass, for example, high-res data can enable one tailored rate for a summer shipment of Gorilla Glass when roads are clear—and another for crystal chandeliers traveling a pockmarked highway during a winter storm. 

Pricing granular risk? That’s just the tip of the iceberg.

What’s particularly exciting is that high-res data will also enable active risk management. Delivering actionable reports and real-time alerts, freight SMBs that lack the benefit of in-house risk management teams will get the crucial insights they need to suppress losses. If a police report indicates a high incidence of cargo theft at a truck stop, for example, an automated alert can instruct the driver to bypass that location in favor of a stop that’s less likely to pose a threat.

In time, this actionable information will become even more important than the paper itself—and clients will increasingly come to see brokers as active partners in their risk mitigation. 

High-res data is the key to opening up new levels of service to brokers’ customers and delivering products as bespoke as the relationships they have already developed.