KEY TAKEAWAYS:

--AI allows, for instance, for exoskeletons that can let workers with spinal cord injuries walk again.

--AI also has the capability to quickly analyze imaging scans to diagnose injuries, potentially speeding up the recovery process.

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Artificial intelligence (AI) is not just an emerging form of technology. AI has already proved valuable to just about every industry, including the medical sector. AI can often analyze complex medical data and come to more informed decisions than humans, leading to improved accuracy of assessments and more effective treatment plans.

“The advances in medical research and technology are robust. We are seeing exponential growth of new research every day,” said Lisa Haug, assistant vice president of medical management at Safety National. “One example is the utilization of artificial intelligence and robotics to help with nerve stimulation for spinal cord injuries, which recent studies are showing has the potential to help [patients] regain movement. While these advances do not necessarily eliminate the need for 24/7 care, they are promoting more independence for the injured worker.”

How Is AI Used in Recovery?

The following are just a few examples of how AI can be used to aid in an injured worker’s recovery:

• Exoskeleton: This is a wearable robotic for patients recovering from spinal cord injury. It allows the injured worker to regain balance, coordination and strength – enabling many to learn how to walk again.
• Rehabilitation: AI can analyze historical data to make predictions about the best course of action for treating an injured worker and guide medical staff in developing effective rehabilitation plans.
• Digital Health: AI aids in early screening, detection and intervention, resulting in early treatment. An example includes home screening diagnostics where an individual uses a test kit at home to detect diabetes, flu, colon cancer, etc. The kits are usually tied to an app, enabling users to monitor results on their devices.
• Prosthetics: There are now computer-aided design and 3D printing of prosthetic parts, along with embedded AI systems that can interpret an injured worker’s movements to provide adaptive tissue and muscle assistance, leading to improved patient mobility.

See also: AI in a Post-Pandemic Future

The Benefits of Using AI

The benefits of using artificial intelligence in an injured worker’s recovery plan are vast, including:

• Quicker Diagnosis: AI has the capability to quickly analyze imaging scans to diagnose injuries, potentially speeding up the recovery process.
• Individualized Treatment Plans: AI can draw on vast databases of patient data to create individualized treatment plans that are tailored to a patient’s specific needs.
• Improved Accuracy of Medical Assessments: AI technology can analyze complex medical information and gain deeper insights than a human doctor, leading to improved accuracy of medical assessments.
• Smart Rehabilitation Plans: AI can study a patient’s progress and conclude which rehabilitation techniques are most effective, thus ensuring an optimal recovery plan.
• Real-Time Monitoring: AI can monitor a patient’s recovery progression in real time and provide immediate feedback, allowing physicians to make necessary adjustments to improve outcomes.

All these advances will help serve injured workers, though organizations will need to remain cognizant of advances and the associated costs. AI will continue to provide advances that provide deeper insight into the patient’s condition, which could help provide an improved path for care and, in some cases, return injured workers to their jobs more rapidly.

KEY TAKEAWAYS:

--Generative AI can optimize underwriter workflow by automating routine tasks around new business pricing, renewals, endorsements and cancellations.

--It could automate much of the traditionally arduous claims processing workflow, reducing the need for human intervention and ultimately cutting down on hours. 

--Generative AI can analyze large volumes of data and identify patterns or anomalies that may indicate fraudulent activity.

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To bolster innovation, insurers are turning to a technology that, in its short lifetime, has already created massive changes in business and the world. Generative AI, a type of artificial intelligence that can create content, rather than simply analyzing existing data, has been at the core of experiments in trying to optimize insurer processes, predict risk and develop customized policies for individual customers.

There are three key areas in which generative AI could transform insurers: underwriter workflow, claims processing and fraud detection. 

1. Underwriter workflow 

Generative AI can optimize underwriter workflow by automating routine tasks around new business pricing, renewals, endorsements and cancellations. This can save time and improve efficiency, allowing underwriters to focus on underwriting. For example, on new business, generative AI can analyze past quotes to assist in risk triage, scoring and classifying policies to assist underwriters in risk selection. This is particularly useful for lines of business where there are significant policy volumes such as financial lines and general aviation. 

With the explosion of data, accurately assessing risk now can mean analyzing vast amounts of information. Generative AI can evaluate this data in real time, allowing insurers to identify emerging risks as they enter the picture and tailor their policies accordingly. With this information in hand, they can work to develop customized policies reflecting the specific risks faced by individual customers in different regions. Looking at other types of non-natural risks, generative AI can analyze social media data and other sources of information to identify and predict the likelihood of incidents related to cybercrime, fraud or other emerging threats. 

2. Claims Processing 

For as long as it has been an established practice, claims processing has been a labor-intensive and time-consuming process, involving extensive paperwork, manual verification and often lengthy delays. Generative AI could be the end of this traditionally arduous process, as it helps insurers automate much of the claims processing workflow, reducing the need for human intervention and ultimately cutting down on hours. 

For example, using natural language processing, generative AI tools can understand and analyze claim forms, quickly identifying discrepancies and pinpointing gaps in information. As a result, the claims process speeds up, and the likelihood of errors and inaccuracies drops drastically. On top of this already welcome improvement, generative AI can be used to automate claims verification, using machine learning algorithms to identify potential fraud or other irregularities, helping insurers reduce the risk of fraudulent claims and ultimately improving the overall efficiency. 

See also: Google's $100B Mistake--and How to Avoid It

3. Fraud Detection

Fraudulent claims cost the industry billions of dollars each year. Generative AI can be a powerful tool in the fight against fraud, allowing insurers to identify and prevent fraudulent activity, instead of scrambling to ameliorate its effects after the fact. 

Generative AI can analyze large volumes of data and identify patterns or anomalies that may indicate fraudulent activity. This analysis can result in surfacing patterns of behavior consistent with fraudulent claims, such as multiple claims filed within a short time, injuries claims inconsistent with the reported incident or those filed from locations known to be associated with fraud. By identifying these patterns early on, insurers can block payments to claims highlighted as fraudulent, reducing financial losses and protecting customers from potential harm. 

As the market continues to evolve and generative AI tools become more sophisticated and powerful, insurers that embrace these technological developments will gain a competitive advantage and thrive in the face of disruption. However, it is important to recognize that there are also challenges associated with the adoption of generative AI in the insurance industry, including data privacy concerns, regulatory compliance and the need for skilled data analysts and other professionals to manage and interpret the data generated by these systems.

KEY TAKEAWAYS:

--Technology has made an unprecedented amount of data on natural disasters available to anyone who needs it.

--MGAs and technology companies will start building better disaster-related insurance products.

--They still need more and better tools so their efforts can scale.

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We’re four months into 2023, and already California has been hit with multiple flash floods, skiers were left snowless in the French mountains and earthquakes shattered cities in Turkey, Pakistan and the U.K. Natural disasters used to happen once every few years -- some were considered “once in a lifetime.” Today, they have become a part of our daily news agenda. 

Technological advancements have finally democratized access to vast amounts of data on these natural disasters. We’ve never been better prepared to understand what could happen tomorrow. 

Bridging the data gap with new tech 

Tech has a deflationary and democratizing effect. In the past, hurricane data and modeling was beholden to a supercomputer that certain specific people, governments and organizations were given access to. Today, three people in a startup can access this data through the cloud and generate high-performance complex calculations at scale. 

However, you can give a person paints and brushes, but they do not automatically become Picasso. Data is only as valuable as the way you use it. On one hand, the more relevant data you access, the better your risk modeling should become. On the other, if you still rely on legacy technology and processes like Excel spreadsheets, this cornucopia of data will instead create unyielding data pools that don’t produce improved insights. Pricing tomorrow’s risk cannot be done with yesterday’s tools. Only with modern tools and technology can businesses generate actionable insights that feed into complex issues like climate change. 

Data transparency and consent essential 

The sheer amount of natural disasters the world is enduring means that, whether for a traditional insurance behemoth or a scrappy insurtech startup, data is available to better predict the risk of these disasters happening tomorrow. But data has a bad reputation -- for too long, businesses have hidden data mining details in the small print of privacy agreements most never bother to read. Do you know how and why a business is storing your data? 

Data should be used to create better, fairer policies in insurance and to explain when policies change. You can’t argue against an increase in car insurance if there’s data showing you driving recklessly, for example. Data can deliver clarity and causality; it can explain risks and costs, but only when explicit consent is given to access such data. 

See also: Improving Communication During Disasters

Modeling for tomorrow 

We’re seeing a huge amount of investment in products like parametric insurance and other related modern forms of coverage to do with climate-related risks. When you look at the challenges faced by some of the biggest insurance companies in the world, you see that a lot of them are exposed to natural perils, like earthquakes and hurricanes, which means the providers of technology in that space are incredibly motivated to make things better. If clients are facing losses that your models are not allowing for, there is a huge disconnect, which will push incumbents to innovate. 

Of course, there’s always more to be done. It’s the nature of the innovation game. As we see more advancement in data science and analytical technology, we’ll start to see MGAs and other technology companies emerge that can actually build dedicated products for businesses around parametric and disaster-related insurance. In fact, we’re already seeing new entrants like Descartes do exactly that. These are companies that you wouldn’t have been able to build just 10 years ago, but advancements in tech and an increase in skilled analytical professionals have made it possible. A 100-person-strong data science team just wasn’t possible in the past. 

The democratization of data means insurers and reinsurers have the data to make better decisions, but not necessarily the tools to do so at pace and scale. It's incredibly important that catastrophe insurance evolves to support businesses -- it can’t be so expensive no one can afford it. That’s not a useful form of protection, yet catastrophe insurance is going to be critical in certain areas for companies to survive. That means as an industry, we need to leverage all the advances in technology to make more of the world insurable.

KEY TAKEAWAYS:

--The 2022 deals show a growing percentage of Tier 4 insurers and MGAs purchasing core systems, indicating how important digital transformation has become to compete in the P&C market.

--Nearly all deals were deployed in the cloud versus on-premise, signaling a need for modern technology to support core deployments. 

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It would be difficult to dispute how critical core systems are in the insurance business – they are the enablers of significant in-and-out interactions and help drive efficiency and profitability for an insurance company. Today, many insurers and MGAs are accelerating their transformational journeys by upgrading their policy, billing and claims systems. And new research suggests that demand for new core implementations is not slowing down. 

A new research report from SMA examines core systems deals completed in 2022 from 18 of the top solution providers in the market today. The transactions span insurers and MGAs and support personal, commercial and combined personal/commercial lines business. What is clear from the research is that the core systems buying is consistent, which, along with continued deployments across all segments over the past few years, indicates a strong market.  

However, a few trends are emerging. The 2022 deals show a growing percentage of Tier 4 insurers and MGAs purchasing core systems, indicating how important digital transformation has become to compete in the P&C market, with more smaller insurers deploying new capabilities. In addition, nearly all deals were deployed in the cloud versus on-premise, signaling a need for modern technology to support core deployments.  

As digital transformation continues to drive innovation, we will continue to see investments in core systems. But it is important for buyers to be mindful of business objectives when choosing solutions – the wrong fit can obstruct goals, but the right solution can enable a digital insurance enterprise to reach new heights.   

For more information on core systems deals and buying trends in 2022, read SMA's recently published research report, "2022 P&C Core Systems Purchasing Trends: Insurance Market Dynamics Shift Foundational Technology Needs.” 

KEY TAKEAWAYS:

--Automation applied to an inefficient operation can simply magnify the inefficiency. 

--You will surely be asked which customers will be affected most by automation, so be prepared.

--Key changes will be cultural, so understand at the outset how much change will be needed -- and tolerated.

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Streamlining and automation often get talked about in the same breath, but there’s a big difference.

Streamlining is essentially simplifying an existing process. That is typically done by removing some unnecessary workflows from the larger effort.

By comparison, automation is “cost cutting by tightening the corners and not cutting them,” as Haresh Sippy, chief founder of Tema India, has put it. In the context of insurance pricing, automation typically means connecting disparate systems and data flows more seamlessly. Rather than just simplifying existing processes, these connections bring some overall structure and governance to the workflow, enable scheduling and triggering of activity and allow for reports to monitor progress. 

Automation shouldn’t just be a matter of saving time – important as that often is – it should bring new sources of value to pricing.

Automation done responsibly

Automation allows for doing more with less -- but automation applied to an inefficient operation can simply magnify the inefficiency. 

Traditional machine learning models, for example, have to effectively "fail" to learn. But will they learn fast enough for certain pricing applications? Automation has to be appropriate to the pricing circumstances for which it is intended.

When looking at how to apply automation responsibly, the six standards recommended by Microsoft are a good starting point: accountability; transparency; fairness; reliability and safety; privacy and security; and inclusiveness.

Improvement must be relative to something relevant

Insurers approach the pricing cycle of Analyze – Decide – Deploy in a multitude of ways, so no two automation projects are going to be the same. 

For example, companies working with traditional, generalized, linear models could make significant improvements (up to 40% resource savings in our experience) by automating the simplifying, grouping and curve-fitting factors that could lead to more competitive or segmented pricing. A next step could be the automated tuning of factor parameters and interactions, leading to applications that assist the interpretation of results.

The key is to identify where automation can improve your pricing process and deliver the most value.

Automation may do more than just replace what previously would have been done manually. Machines may reveal pricing insights that wouldn’t typically have been uncovered. Often, automation can serve to triage the value of making rating updates, as we have seen recently with some companies automating the tracking of potential inflation effects on their books of business.

See also: Insurers Turn to Automation

Which customers will be most affected?

In just about every pricing automation project we’ve worked on where companies are, for example, using technology to integrate and update data from multiple systems to adjust their pricing and are aiming to get new pricing to market quicker, the question arises: “Which customers are going to be most affected, and by how much?” 

In the fairly safe knowledge that the question is coming, automate the response, particularly as impact analysis can be extremely time-consuming if done manually.

Another reason for being ready for the question is increasing interest from regulators in understanding how machine learning and automation are driving pricing decisions. 

Key challenges are often cultural

Automation doesn’t necessarily always sit easily with established pricing practices. It pays to determine what those most involved are prepared to let go and the acceptable levels of scrutiny and review of automated processes at the outset. 

There is likely to be a need to introduce new working practices, because breaks or barriers in an automation-enhanced workflow can limit the benefits of automation. For example, a company that aspires to automated delivery of pricing updates can face real problems if the hand-off from pricing/product teams to IT/rate deployment teams is overly manual and complex.

Check out Majesco’s latest research report to better understand the strategic priorities and investments needed to adapt to today’s market challenges and focus areas for new products, value-added services, channels and digital expectations.

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Sponsored by ITL Partner: Majesco

Similar to how iPhone technology transformed the way we communicate, generative artificial intelligence (AI) is creating new efficiencies for the insurance industry. Most recently, Oliver Wyman has been working with leaders to augment and reinvent significant areas of their business. Here we share our latest research, a primer on generative AI, how insurers can get started, risk considerations, and opportunities and key actions for industry reinvention. 

Read More

Sponsored by ITL Partner: Oliver Wyman

With inflation showing staying power, how can your firm best harness risk, economic disruption and prepare for a potential downturn? Today’s challenging economic environment offers firms the opportunity to Reset4Value — and drive strategic repositioning, smart cost decisions, and fund their organization for a better tomorrow.

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Sponsored by ITL Partner: Oliver Wyman

As the commercial insurance industry continues to evolve, insurers are under increasing pressure to streamline their operations, reduce costs and improve user experiences for agents AND insureds. The discipline of underwriting, involving assessing and pricing risk, is a critical function in this process, and the need for efficiency and thoroughness has never been greater.

One way insurers are addressing these challenges is by leveraging novel data, created by artificial intelligence (AI) to automate underwriting processes. By using machine learning algorithms to analyze vast amounts of data to predict the answers to underwriting questions, insurers can reduce the time and effort required to underwrite policies, while improving accuracy and consistency. It’s not too dissimilar from the way large language models (LLMs) like ChatGPT and Google’s Bard use vast amounts of unstructured web content to predict the next word in a conversational sequence.

Automation and data-driven decision-making also allow commercial underwriters to focus on more complex risks, while leaving routine and straightforward applications to be handled by machines. This not only increases efficiency but also allows underwriters to spend more time on high-value tasks that require their expertise and judgment. This is a MASSIVE win for underwriters who are forced into tactical, rather than strategic roles, manually processing applications rather than making value-added risk evaluations. In a low-complexity line of business like workers' compensation, a client of Planck was able to decrease processing time from hours to minutes and reduce submission errors by 29%, ultimately leading to a hit rate increase of 19%! 

Underwriting automation also improves risk management. Machine learning algorithms can identify patterns and trends that human underwriters may miss, allowing insurers to better predict and manage risk. This can lead to more accurate pricing, as well as fewer claims and losses for the insurer.

AI in underwriting also allows for more personalized risk assessments, which can lead to better pricing and coverage for clients. Insurers can use data from a variety of sources, including social media, satellite imagery and weather data, to gain a more complete picture of risk and tailor policies accordingly. This can help underwriters better understand the specific needs of each client, while also providing them with more targeted recommendations. Through a partnership with a top-three European carrier, Planck was able to identify that 75% of this carrier’s construction book of business was underinsured — leading to a 30% potential increase in revenue. 

See also: Insurers Boosting Their Use of AI

Underwriter augmentation of this sort is not a silver bullet, and it is not intended to replace underwriters entirely. Instead, if correctly deployed, AI serves to magnify underwriter abilities, enhance their effectiveness and ultimately make their jobs easier. 

Underwriting automation driven by AI-generated data can help make commercial underwriters more efficient, accurate and focused. By leveraging technology to streamline processes, insurers can improve customer service, reduce costs and better manage risk.

KEY TAKEAWAYS:

--The discussion has moved to how much analytical work and task automation are possible – and if it is feasible beyond simple repetitive tasks.

--Concerns have heightened about AI’s potential to replace humans and eliminate jobs.

--If it becomes impossible to tell if a person has created an image, email, letter or video, then the potential for fraud skyrockets, and determining liability in claims becomes more difficult.

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When I was asked in late 2022 and early 2023 about the implications of generative AI for insurance, my reply was always two-fold. First, I advised that it is absolutely a technology space to monitor closely, given the rapid advancement, broad application and unlimited potential. But secondly, I believed that the near-term use cases for P&C insurance were limited to more horizontal spaces and not so much to insurance-specific applications.

There is no question that even now there is value in automating and enhancing interactions. ChatGPT and similar tools are, at their root, designed for conversational AI – driving informed and automated chatbot-oriented interactions. Many types of communications can benefit from this technology – agency help desks, policyholder inquiries, claims status, internal conversations and many more. In addition, anywhere in the insurance enterprise where there is a need to summarize information, create digital material or extract data is now a possibility for AI to automate. In fact, this is more than a possibility – insurers are already deploying ChatGPT across many use cases.

Recently, I started cataloging all various interesting use cases of ChatGPT and, more broadly, generative AI across industries. However, I abandoned that effort as a hopeless task. There are many articles every day on how someone has used a generative AI tool to write code, pass exams, write papers, create art, images or videos, drive database queries, power conversations and more.

Within just a few months, I have seen the tone of these articles shift from the perspective that the AI output was amazing but not always accurate or high-quality to one where significant progress has been made. It was only a few months ago when it was sometimes easy to tell the difference between human- and AI-generated content; today, the task is far more difficult.

Now the dominant questions are not about whether the technologies are viable for real-world use cases. Rather, the discussion is about how much analytical work and task automation are possible – and if it is feasible beyond simple repetitive tasks (we already have RPA for that). The use cases are rapidly expanding into more complex, industry-specific areas.

This naturally heightens the concern about AI’s potential to replace humans and eliminate jobs. My fundamental view for many years has been that the AI family of technologies will augment humans and elevate the roles of industry professionals. Agents, underwriters, adjusters and others will focus on activities that require deep expertise, experience and empathy. I still believe that is true… but not as strongly as I did in the past.

See also: Google's $100B Mistake--and How to Avoid It

The other main question that arises is about the challenges of determining authorship. If it becomes impossible to tell if a person has created an image, email, letter or video, then the potential for fraud skyrockets, and determining liability in claims becomes more difficult.

The net of this blog is that generative AI in all its forms must be closely monitored by P&C insurers, and governments and the business world must develop the right regulatory/governing framework for AI. Experimentation with the technologies is mandatory. Now is not the time to sit on the sidelines and watch – things are moving too fast for that.