Climate risk is yet again front-page news. Early estimates of total economic loss from Hurricane Helene and its aftermath amount to $145 billion to $160 billion. However, the early estimates of insured losses for the event are significantly lower, at $5 billion to $10 billion. If these estimates turn out to be accurate, this would make the ratio of insured loss to economic loss the smallest among U.S.-landfalling hurricanes in recent decades and point to a significant protection gap. 

With that context, ITL Editor-in-Chief Paul Carroll talked with Veronika Torarp, a partner with PwC, about how insurers can help improve climate resiliency.

Paul Carroll

To start, I'm curious how you are thinking about climate resiliency at PwC and how you're advising your clients to address the issue.

Veronika Torarp

Climate risk and climate resiliency are two of the most pressing issues facing our clients today. PwC was one of the lead sponsors of this year’s Climate Week in New York City, and it seems that the topic of adaptation and resiliency has moved to the forefront of conversations around climate. I think there's a great opportunity for insurers to be more involved in these dialogues, both in the U.S. and globally, about how we protect our communities in light of more severe weather.

We're advising our clients to engage more broadly with stakeholders, to work with communities and regulators to come up with practical solutions and start to mobilize action. There’s so much at stake, not just for insurers but for impacted communities, for businesses that operate in those communities, and for the people who live there.

Paul Carroll

I'm interested that you use the word “community” because talk about community-based solutions was one of the things that struck me at a recent climate event in Atlanta – which I just discovered you attended, too, even though we somehow didn’t meet during a two-day gathering of just 100 people or so. I had a home in Lake Tahoe for many years, and if I didn't take care of my manzanita, that put my neighbors’ homes at more danger of wildfire, and vice versa. So community-based approaches to mitigating risk make a lot of sense to me.

Veronika Torarp

If you take this broad topic of climate resiliency and bring it down to the community level, a lot of pain is felt by city managers and other community leaders. They see the effects when a storm comes through or damage is done, to the extent that insurance isn’t available. There are real economic hits. Beyond the immediate effects, your tax revenue base may be affected if residents lose their livelihood or move out of the area.

So this notion of community-based solutions is something we absolutely believe in. I know there are discussions emerging in the industry about community-based insurance programs, and they’re certainly worth exploring further.

I also think there's a lot more that could be done around predicting and preventing losses. For example, we could do more to help communities finance and put up steel roofs for homeowners to prevent hail damage. We could enforce better building codes, though that can take time to have a major effect. We could implement nature-based solutions to mitigate the impacts of storms and wildfires. There are a number of resiliency interventions that could be activated at the local level that could really help protect communities more effectively than they're being protected today.

Insurers can also develop new types of products and solutions. That could include parametric products that afford consumers some level of protection that's not a traditional, admitted policy. If you think more broadly, why can’t we create a resilient building certification, along the lines of the certifications that are now available for “green” buildings? A resiliency certification that’s codified would encourage people to build to the best standards both before and after damage occurs.

Paul Carroll

In a webinar you did recently, I was struck by something you said about municipalities that touches the other side of the balance sheet: the assets. You noted that many insurers invest in municipal bonds and could be affected as increasingly severe weather takes its toll on infrastructure and government properties.

Veronika Torarp

Yes, municipal bonds tend to be a large asset class for many insurers, and as we see more intense storms, wildfires, droughts, and floods impose higher costs on state and local governments, that's putting pressure on the spending side. At the same time, on the revenue side, storm damage and insurance risk can undermine the municipal tax base. These ultimately impact the ability of municipalities to service their bond payments and raise new capital. Moody's and other rating agencies have actually started to downgrade some municipal bonds as a result of looking at the climate risks. That could have a negative impact on insurers that invest in these bonds.

Paul Carroll

Another idea that popped for me in Atlanta was the idea of possibly insuring against extreme heat. Heat is obviously a factor in hurricanes, convective storms, hail, and a bunch of other things, but what do you think of possibly insuring against extreme heat’s more direct effects on people and property?

Veronika Torarp

I think there are lots of opportunities to innovate and afford consumers and businesses more protection. This is another area where there's an opportunity for prevention, through different ways to help with cooling and generally reduce exposures. That certainly will have implications on workers’ compensation and other lines, such as business interruption, when operations are affected by extreme heat.

The first step for insurers is understanding the risk exposure that more severe heat is introducing to the lines they're already writing and products they’re already offering their clients. Once you understand what that risk profile is, there's also an opportunity to think about if you can introduce specialized coverage or specific endorsements or specific products around heat exposures.

We’ve recently written a paper on the impacts of heat stressors and implications on mortality risk for life insurers, and there are certainly implications on accident and health and other P&C lines, as well.

Paul Carroll

To me, a tricky issue with climate is figuring out who pays the up-front costs of prevention, and making sure those payers collect the benefits. If I’m an insurer thinking of investing in mangroves as a nature-based approach to mitigating the damage that a major storm can do to a shoreline, the residents of the area certainly benefit, but how do I, the insurer, benefit enough to justify my investment? There can also be a timing issue, because some of these solutions, such as mangroves, will talk a long time to pay off. I know I’m asking a fuzzy question.

Veronika Torarp

Another way to ask the question, perhaps, is in terms of who pays for resiliency interventions, whether it's investing in nature-based solutions or investing in a steel roof. These go beyond what insurance companies can possibly pay for, so the challenge is how to arrange incentives that encourage these types of interventions. This is where some creativity is going to be required, and collaboration across stakeholders.

Communities can tap into federal funding that's available for some of these resiliency interventions today. More than anything, insurers can bring more clarity to what the types of interventions are that would have the biggest impact on loss mitigation, and ultimately, the cost of insurance. That's one of the reasons insurers need to be at the table. They have more insight and data than arguably anyone on what the leading causes of losses are and what can be done to prevent future losses, in terms of specific interventions.

But I will also say there are lots of resiliency interventions that don't necessarily have to be cost-prohibitive, such as clearing brush close to your property to reduce your wildfire exposure.

Paul Carroll

What other innovations should we talk about?

Veronika Torarp

There's a lot of emphasis on the incremental investment that we're going to need to address climate issues, and a really important point that sometimes gets lost is that we’re already making a lot of capital investments. Just as an example, I think the Army Corps of Engineers budget for next year is $9 billion. Are they spending that money based on standards that will result in more resilient bridges and other infrastructure? I don’t know. But we should be having conversations with them about the best standards to apply.

In the same way, after disaster strikes and homes get destroyed, we can make sure we rebuild those homes to a more resilient standard. We’re going to spend the money anyway. Let’s make sure we spend it judiciously with climate risk mitigation in mind.

The other point I’d make is that generative AI can have a transformational effect for society at large, and I get excited about how we can leverage it to generate analysis and advice that lead to better decisions, whether around resiliency investments or interventions.

We need to make more climate-savvy decisions about insurance and risk, and there's a lot of opportunity to explore what could be possible with new technology available to us.

Paul Carroll

We’ve covered a lot of territory, but do you have any final thoughts?

Veronika Torarp

I’d just emphasize that there is an opportunity for the insurance industry to lead here. At PwC, we recently published a paper on how insurers can lead on climate resiliency.

Historically, insurance has been a very cautious industry, but there’s a lot at stake, and insurers can have a significant voice in ensuring that we build more resilient communities going forward. I invite the industry to lean in and lead. There's a lot of opportunity.

Paul Carroll

That’s great. Thanks, Veronika.

About Veronika Torarp

Veronika is a partner in PwC Strategy&’s insurance practice. She has close to 20 years of experience consulting Fortune 500 companies on growth strategies in the U.S. and internationally. Veronika leads PwC’s sustainability strategy to the insurance sector and regularly provides forward-looking perspectives on the global insurance industry, most recently in “Climate risk and insurance: the case for resilience” and “Insurance 2025 and Beyond: Insurance reimagined: Spotlight on trust, convergence and transformation.” Before joining PwC, Veronika led market development and served on the climate change committee for Travelers Insurance. She received her MBA from IMD Business School in Switzerland and a B.S.B. from the Carlson School of Management at the University of Minnesota, where she serves on the advisory board for the Masters of Business Analytics. Veronika will be moderating a panel on the topic of climate resiliency at Triple I’s Joint Industry Forum, which will be taking place in Miami Nov. 19 and 20.

As much damage as Hurricane Helene has caused — and this one is really bad — it also sends a truly ominous warning about future storms because of something known as the "brown-ocean effect." 

Atlantic hurricanes lose force as they move inland, because they no longer have access to the warm water that provided their energy in the first place. But Helene stayed far stronger than normal as it moved north. 

The governor of Georgia said it was like a 250-mile-wide tornado as it moved through the state. Helene dumped more than 32 inches of rain on parts of western North Carolina and devastated the area. The state is some 450 miles north of the Gulf of Mexico and historically has just had to deal with the dying remnants of such major storms.  

Why didn't Helene fade as quickly as prior hurricanes have? Scientists say the reason may be the "brown-ocean effect."

And they worry that future storms may mimic Helene, meaning we may have to now worry about a hurricane alley reaching well up into Appalachia. 

The "brown-ocean effect" can occur when the ground the storm is covering is hot and already saturated by rain. The storm is thus traveling over a sort of brown ocean — made of dirt but still with an awful lot of warm water available to keep powering the winds and supplying the torrential rain. 

In the case of Helene, it caused so much rain in advance of the main body of the storm as it moved north that it may have created its own brown ocean that the storm could feed off.

We've already been seeing hurricanes intensify much more rapidly than the historical norm because ocean waters are so warm — Helene itself quickly went from a Category 1 hurricane to a Category 4 right before making landfall in Florida. Scientists now worry that these more intense hurricanes could, like Helene, reach much farther inland. 

A New York Times article on the possible role of the "brown-ocean effect" in Hurricane Helene cited earlier hurricanes where it was a factor:

"Past research has found that [the effect] helped keep Hurricane Ida in 2021, Tropical Storm Erin in 2007 and other tropical disturbances chugging along for longer. 'Instead of just winding down, they just maintain their intensity and in some cases perhaps intensify,' [Marshall Shepherd, the director of the University of Georgia’s atmospheric sciences program,] said."

He added: "'The fact that we’re just seeing these hurricanes penetrate farther inland as hurricanes, I think that is a harbinger of things perhaps to come in our state and even in the Appalachia region.'”

I dearly hope my fears are overblown. The region has already been hit so hard. As our colleagues at the Triple-I noted last week, Helene is "the fourth hurricane to make landfall in the U.S. this year and the fifth hurricane to slam storm-weary Florida since 2022. [This is] the third consecutive year Florida has incurred a major hurricane landfall. Additionally, this [is] the third hurricane to make landfall in the Big Bend over the past 13 months."

But there doesn't seem to be any respite in sight — in fact, more storms are forming in the Atlantic. And I think the "brown-ocean effort" could worsen their effects.

Stay safe.

Paul

P.S. There were quite a number of interesting pieces in the past few days related to extreme weather. Among them:

• Zillow says its for-sale listings "will now feature detailed climate risk information for five key categories — flood, wildfire, wind, heat and air quality — along with insurance recommendations."
• The Washington Post has a fascinating piece about a Tampa hospital that used something called AquaFence to set up a temporary floodwall and protect it from Helene. 
• Despite all the concerns about hurricanes and rising sea levels, the Washington Post reports that people keep moving to highly vulnerable areas such as Galveston. 
• In the same vein, the New York Times has a long, informative piece about people moving to areas that are increasingly susceptible to all manner of perils. 
• Finally, the Washington Post reports that the U.K., home of the Industrial Revolution, has shut down the last of its coal-burning power plants. No, that doesn't have anything directly to do with the hurricanes or other catastrophes we're currently facing, but I still count the development as a milestone in our attempts to limit climate change.

On July 2, 2024, Hurricane Beryl became the earliest Category 5 hurricane observed in the Atlantic Ocean on record, and only the second Category 5 hurricane to occur in July since 2005. Four hurricanes have now made landfall in the U.S. this year, including last week's devastating Hurricane Helene. 

Weather has become more unpredictable in recent years, and weather events are affecting a much larger geographical footprint than they have historically. In our industry, preparations for these events, be they hurricanes, rain, heat, hail, etc., can be as nuanced as the events themselves.

Carriers and repair shops usually plan months, sometimes years, in advance of these events, analyzing historical storm data and predictive models to forecast potential impacts. Based on these insights, carriers may adjust underwriting guidelines and secure reinsurance, while repair facilities may collaborate with suppliers to ensure a steady supply of parts to minimize repair delays and staff up to manage the increased volume.

A Look Back - Hurricane Ian

To grasp the true impact of a hurricane, let’s dive into the impact of 2022's Hurricane Ian. CCC's monthly comprehensive estimate volumes were relatively consistent and predictable – at least until the hurricane made landfall in Florida, Georgia, and South Carolina in late September. Comprehensive estimates jumped 3X for the three states and almost 7X for Florida, alone.

With the mass influx of claims comes the immediate need to effectively triage, assign, appraise, and move vehicles. One of the most noticeable shifts is in who performs the initial appraisal. Because of the higher proportion of potential total losses and the sheer volume of inspections that need to be completed, carriers increase their reliance on staff appraisers – many of whom are part of a catastrophe team – and independent appraisers (IAs).

After Hurricane Ian, there was also a significant increase in the time it took for vehicles needing repairs to start the repair process after initial claims were assigned to a shop.

This clear spike indicates a delay or bottleneck in the repair process, which could be due to various factors such as increased demand for repairs, limited shop capacity, supply chain issues, or resource shortages. Shops in affected areas may have also been affected by the storm, contributing to even more demand among fewer shops.

Perhaps more important is the continued downstream impact catastrophic events have in the months following on turnaround times for unrelated and other future claims as the ecosystem works through the backlog of vehicles.

A spike in claims creates a spike in the need for replacement parts. Following Hurricane Ian, Florida, Georgia, and South Carolina saw huge increases in demand within numerous parts groups, including liftgates, trunk lids, rear lamps, quarter panels, rear bumpers, and doors.

In Florida, which bore the brunt of Hurricane Ian-related damages, demand for parts increased almost 20X for trunk lids and liftgates; many other parts saw demand increases above 10X.

The severity of weather-related damages can also lead to an increase in the number of vehicles being declared total losses by insurance carriers, which can create assessment backlogs for shops and replacement vehicle shortages for insurers. 

CCC data shows that during Hurricane Ian’s peak impact days (Sept, 25-30, 2022), nearly 52% of vehicles were declared total losses for all three states, and 51% were declared total losses in Florida, alone. 

This is a stark contrast to the days leading up to the hurricane (Sept. 20-24), where the percentage of total loss claims hovered between 16% and 18%. After the hurricane subsided (Oct. 1-5), the percentage of total loss claims decreased to more typical levels, around 20%.

Commercial and recreational vehicles (CRVs) also saw an influx of total losses following Hurricane Ian. In Florida, CRV valuation volume jumped across virtually all categories, especially for marine (boats) of various sizes, heavy equipment, all-terrain vehicles, travel trailers, and motor homes.

The specialized nature of many of these CRV categories adds to the expertise, time, and cost needed to appropriately evaluate these claims. Costs and mobility vary widely for these units.

Total loss claims often necessitate coordination with salvage, towing, and rental services. As such, post-storm salvage operations become critical, as insurers and salvage companies must efficiently process a higher volume of totaled vehicles. This not only includes determining the value and condition of these vehicles but also coordinating their removal and sale, which can be a logistical challenge during periods of widespread disaster recovery.

See also: Preparing for a Rough Hurricane Season

Top Six Recommended Adaptations for Hurricane Season

1. Staff Up

As the 2024 hurricane season deepens, auto claims management teams must ensure they are adequately prepared to handle the potential surge in claims. This can include using third-party personnel and specialty vehicle valuation teams to supplement existing staff.

2. Form CAT Teams

CAT teams are composed of adjusters who specifically respond to catastrophe claims. As staffing challenges persist, carriers may sometimes resort to redirecting non-catastrophic adjusters to focus on major storm-related claims. This is where digital claims management tools can be most effective, enabling less experienced adjusters and increasing claims teams’ capacity to ensure they meet policyholders’ needs during peak times.

3. Use Claims Management Technologies

Claims management technologies can help reduce the need for carriers to put personnel physically on the ground and streamline repeatable processes so as not to burden their already overworked adjusters. This approach can vastly improve the employee experience while also ensuring storm-related claims are settled accurately and efficiently.

4. Prepare for Downstream Impacts

As economic challenges persist for consumers, the potential for vehicle damage to go unrepaired continues to rise, especially as motor vehicle costs rise. As of April, the consumer price index for motor vehicle insurance was up 23% year-over-year and up 42% since April 2022.

In efforts to decrease insurance costs, consumers may elect to drop first-party coverages (collision and comprehensive) on older vehicles and vehicles without lienholders. Another method to decrease insurance premiums is to increase deductibles.

CCC continues to see gradual shifts in claims toward higher deductibles. $1,000 comprehensive deductibles have increased by three points in the past 24 months (ending in March 2024), while less comprehensive claims include $100, $250, and $500 deductibles. 

If vehicles don't have adequate first-party coverage or have a high deductible that a policyholder is unable to meet, the likelihood of damages getting repaired surely decreases – especially superficial or aesthetic ones. As a result, the potential for unreported and unrepaired prior damage increases, which can complicate future claims and decrease the value of the vehicle.

Another growing concern is the impact of undiagnosed or unrepaired electrical and safety systems, which could be damaged during weather events, including hurricanes and severe convective storms.

See also: Climate and Catastrophe Risk Strategies

5. Assess Repair Inventory

Hurricanes and other severe storms may affect supply chains, potentially halting or delaying parts deliveries to shops, which can hurt productivity. While the unpredictable nature of weather events can make planning ahead difficult, there are two ways shops can prepare for this possibility:

• Review current parts inventory, assess stock levels, and identify critical components that are likely to be in high demand.
  • Create a shortlist of essential items that may become scarce in the aftermath of a catastrophe.
  • Order and stockpile parts in advance, reducing the risk of shortages and ensuring continued service to customers during and after hurricane season.
• Maintain strong relationships with suppliers to secure priority access to parts when supply chains are disrupted.
  • Communicate needs and plans with suppliers early on for more timely deliveries of essential parts to maintain smooth operations.

6. Communicate Clearly and Regularly

Given the unpredictability of storms, carriers need to engage with policyholders by maintaining open communication throughout the hurricane season and providing timely updates on storm forecasts, evacuation orders, and claims procedures to help mitigate losses and get people out of harm’s way.

Repair shops, which benefit from clear customer communication throughout the year, can consider leveraging existing digital communication tools, and even social media, to provide updates on repair timelines, temporary operation hours, rental car availability, alternative transportation routes, and more.

While major hurricanes garner significantly more attention, even smaller storms can critically strain auto claims and repair industry resources. Preparing with robust strategies and technologies is crucial to weathering the storm ahead, as is adjusting strategies based on new intelligence. 

Leveraging advanced AI-powered technologies that analyze historical data and produce predictive analytics can help businesses better forecast the severity and the probable impact areas of coming storms, allowing insurers and repair facilities to allocate resources more effectively and prepare for a surge in claims and repairs. Automated claims processing can also help speed initial assessment, helping to reduce bottlenecks and improve customer satisfaction.

As the development of artificial intelligence accelerates and its use becomes increasingly widespread, the insurance landscape must also begin to adapt to these changes. These transformations affect various aspects of the insurance business. Insurers recognize this and see the enormous potential in integrating AI with their systems to enhance claims processing, risk assessment, and pricing. According to the KPMG Global Tech Report 2023, 52% of respondents identified AI as the most critical technology for achieving their ambitions in the coming years. 

AI in pricing represents a breakthrough for the insurance industry. We are already witnessing insurance companies shifting to automated solutions, with more accurate risk assessment and increased profitability.

Let's take a closer look at how AI can affect insurance pricing.

See also: How AI Is Changing Insurance

Traditional Pricing 

Traditional insurance pricing methods are burdened with several limitations, such as being time-consuming and complex. This approach involves collecting essential demographic data and historical claims information and considering external factors. Insurers must then analyze this data using complex mathematical and statistical models to estimate risk and determine appropriate insurance rates.

In the traditional approach, insurers rely heavily on historical data, which can sometimes lead to outdated risk assessments. This method also tends to be more reactive than proactive, often adjusting rates only after significant market shifts or claims experiences. Moreover, the manual nature of traditional pricing can introduce human error, leading to inconsistencies and inaccuracies in risk evaluation.

Additionally, these methods require continuous updates to respond to changing conditions and new data, increasing the complexity of the process. Traditional pricing methods also lack the flexibility to effectively respond to rapid market changes.

Despite these limitations, traditional pricing approaches still form the foundation for many insurers. However, given these constraints and the growing potential of AI, traditional pricing methods will gradually be replaced, paving the way for more flexible and dynamic pricing models driven by AI.

Dynamic Pricing WAI and Connected Devices 

The integration of data with advanced AI algorithms represents an interesting step in the evolution of pricing. Smartphones, smartwatches, telematics, and IoT devices are crucial data sources that insurers can use for their pricing strategies. The rapid increase in connected devices and machine learning models enables continuous monitoring, providing a better understanding of customer risk profiles. Additionally, AI algorithms allow for real-time data analysis from various sources, leading to more precise and accurate risk assessments. This capability helps insurers tailor their offerings more effectively.

Examples:

• Telematics in Car Insurance: Telematics devices installed in vehicles collect data on driving patterns, such as speed, braking, and acceleration. Insurers can use this data to assess risk more accurately and offer personalized premiums. For instance, a driver who maintains safe driving habits may receive lower premiums compared with a driver with risky behaviors.
• Health Insurance: Wearable devices, like smartwatches, track physical activity, heart rate, and other health metrics. AI can analyze this data to provide a more accurate assessment of health risks, allowing insurers to offer tailored health plans and health management advice.
• Property Insurance: IoT devices, such as smart home sensors, can monitor environmental factors like temperature, humidity, and security breaches. AI can process this data with greater precision. This enhanced analytical capability allows insurers to assess risks more accurately and adjust property insurance pricing accordingly, providing a more tailored and responsive approach to risk management. 

Benefits

Data analysis using AI is becoming a crucial element of modern pricing strategies in insurance. 

See also: A Data Strategy for Successful AI Adoption

Increased Profitability for Insurers

 AI-driven models can analyze vast amounts of data quickly and accurately, identifying patterns that traditional methods might miss. This leads to better risk assessments and more competitive pricing strategies, which can enhance profitability.

Greater Customer Satisfaction

Collecting and analyzing data from connected devices allows insurers to create more personalized and precise offers, leading to higher customer satisfaction. Personalized policies that reflect individual behaviors and needs make customers feel understood, which can increase loyalty and retention. 

Adaptive Flexibility

AI enables insurers to respond quickly to changing market conditions. Dynamic pricing and real-time rate adjustments mean better risk management and maintaining competitiveness.

Operational Efficiency

Automating pricing processes with AI reduces operational costs and increases efficiency. Streamlined processes allow for faster data processing and decision-making, freeing up resources and reducing the potential for human error. Insurers can process claims and adjust policies more swiftly, enhancing overall productivity.

Implementing AI-Driven Pricing: Challenges 

Implementing AI in pricing brings numerous questions and challenges that must be addressed. Primarily, insurance companies need to prepare for significant financial and time investments related to modernizing IT infrastructure and implementing new technologies. According to a Gartner report, as many as 85% of AI projects fail due to a lack of appropriate data and infrastructure.

Challenges to consider

Appropriate Infrastructure 

Integrating various data sources requires advanced IT infrastructure, which is both costly and time-consuming. Companies will need to invest in modern data storage and processing systems capable of handling large and diverse data sets. This also means investing in scalable systems that can manage increasing amounts of data over time. 

Data Quality 

Ensuring data quality is critical. Lack of consistency or low-quality data can lead to inaccurate analysis and poor business decisions. Data must be clean, consistent, and well-organized to be useful for AI models. 

Security

The collection and analysis of vast amounts of customer data inherently involve privacy and security concerns. Insurers must protect sensitive customer information through measures like encryption, anonymization, detection, and prevention systems for data breaches. 

Regulatory Compliance 

While the pace of AI development often outstrips regulatory measures, the use of AI in pricing must comply with existing legal regulations. Insurers will need to continuously monitor regulatory changes to ensure full compliance with legal standards.

See also: The 10 Biggest Mistakes in AI Strategies

Transparency in Decision Making 

Another challenge is the transparency and interpretability of AI algorithms. AI models, especially those using machine learning, can be difficult to understand and explain, which may hinder their acceptance by both employees and customers. 

These challenges require strategic planning and consistent implementation to fully realize the potential of AI in pricing processes. Addressing these issues will help insurers use AI effectively while managing risks and maintaining trust with their customers.

Summary

AI in pricing offers unparalleled accuracy and speed in risk assessment and pricing decisions. The ability of AI to analyze vast amounts of data in real time allows insurers to make more precise pricing adjustments, reflecting the true risk profile of each customer. Furthermore, the speed at which AI can process and respond to new data ensures that insurers can adapt to market changes and emerging risks almost instantly. However, this transition also involves challenges that need to be considered. Despite these challenges, companies that successfully adopt these technologies will be better prepared for the future and more competitive in the market.

“Pivot” ranks right up there on the Mt. Rushmore of startup jargon – right alongside “bootstrap,” “scale,” and “agile.” 

With AI becoming mainstream over the last year, discussions of pivots have been sent into overdrive. We’ve seen one tech company after another pivot toward AI. Some are genuinely integrating AI into their products; others are using marketing plays to AI-wash their brands.

To be clear, AI is the real deal; it’s why I wrote my thesis on it in business school. It’s not just a wave of enthusiasm – AI is finally delivering on the promise that’s been decades in the making. Everyone wants in, builders and investors alike.

But a pivot, whether it’s toward AI or something else entirely, is never easy. You can’t just will it to success. And while there’s a lot to be excited about, many organizations are sprinting too drunkenly toward AI. Worse, many companies that are doing just fine without AI are making drastic, irreversible changes that could cost them dearly – for the sake of AI.

A good pivot demands a clear strategy and a perfect product market fit. Founders must make decisions based on a deep understanding of market dynamics, not a fear of missing out on the next hot thing. 

Trust me, I have been there.

See also: 7 Things Sailing Taught Me on Leadership

An aircraft carrier-style turn

My company, Sure, began as a direct-to-consumer (D2C) mobile insurance app before becoming a digital insurance infrastructure company, using our existing core technology. In hindsight, the shift was absolutely the right move, but success wasn’t guaranteed, and it wasn’t always clear if it would work.  

They key lesson I learned: Never pull the plug on something that is working to do something new until proving that you can successfully do the new thing. 

Our transformation didn’t happen abruptly, or as a result of one meeting or feedback from our board. It wasn’t a situation where we got up one morning and decided to do something totally new. It was an evolution based on skeptical thinking. 

We didn’t even utter the word “pivot” – we thought of it as an aircraft carrier-style turn. We kept our heads down on the existing business while we started to identify the first customers for our SaaS solution – and it worked. The first partnership that we ever secured as a SaaS infrastructure provider is still going strong to this day. In fact, it’s never been stronger.

Unfortunately, many startups make the fatal mistake of impulsively abandoning what they are currently doing successfully to prematurely pivot toward something new – and, well, they end up failing, oftentimes slowly and painfully.

What not to do

Don’t get me wrong. A pivot can succeed. It just requires careful execution and avoiding certain pitfalls. Here are some pits of sorrow and misery that will make your pivot fail and lead your company toward oblivion:

• Act impulsively: Jump on any trend, regardless of its long-term viability or relevance to your core strengths.
• Ignore customer feedback: Avoid engaging with customers or conducting thorough market research. Ignore data, trust feedback from one VC. 
• Rush the pivot, at all costs: Make abrupt changes without considering the impact on your existing customer base, team, and business partners that joined with the prior strategy.
• Abandon your strengths: Disregard what your company is currently successful at. Instead, pivot toward unfamiliar areas that you’ve spent a weekend thinking about.
• Stick to a rigid plan: Refuse to adapt your strategy as challenges arise. Remain stubbornly committed to an initial plan, no matter its flaws and lack of traction.
• Reinvent the wheel: Forego seeking advice from experienced peers or industry experts. Ignore what you are uniquely good at.
• Chase short-term gains: Focus solely on immediate results rather than long-term sustainability and growth. Try to hit that next milestone for a capital raise instead of focusing on building for the long-term.
• Avoid feedback: Neglect to refine your pivot strategy based on customer or market feedback. Don’t be skeptical. Your first idea wasn’t right, so the second one must be right!?
• Expect smooth sailing: Fail to anticipate and prepare for the inevitable difficulties and setbacks that come with major change or a whole new set of competitors.

See also: Leadership Lessons From Sports

What to do

Pivoting is nothing new in the startup world, especially in recent years. Startups have navigated through periods of rapid growth, abundant capital, and challenging economic climates. Seasoned founders understand that simply having a great product doesn’t guarantee success, and shifting focus can either make or break even the most innovative companies.

While Sure may have had a short lifespan as a D2C mobile app, the strategic hypotheses that we committed to early on helped us to grow into the complete digital insurance technology solution that we are today. That core technology, application programming interfaces (APIs), embedded insurance, and fundamental hypothesis about the market has never been more true than it is today. We chose to stay focused on our core strengths while we explored new markets, and the results speak for themselves. 

Pulling off the pivot isn’t easy, but if done well it can unlock bigger opportunities you never dreamt about at the beginning of your entrepreneurial journey – all while positioning your company for long-term success.

Insurance agencies constantly face operational challenges, from managing client renewals to processing claims and updating policies. These tasks can overwhelm internal staff, distracting them from core business responsibilities. With increasing customer demands and the need for faster, more accurate service, insurance agency owners are looking for ways to remain competitive and efficient. 

Many are turning to outsourcing, which can help streamline operations and deliver key benefits to insurance agencies.

See also: Does Generative AI Kill Process Outsourcing?

Operational Challenges in Insurance Agencies

Insurance agencies face challenges daily such as data entry, policy renewals, claims handling, and policy checking that require constant attention and precision. Agencies must also deal with issuing certificates of insurance, sending out renewal notices, and ensuring accurate policy documentation. These tasks often take significant time.

Moreover, the high volume of repetitive tasks can make it difficult for the internal team to focus on more strategic functions, such as client management and improving customer service. By offloading non-core tasks to outsourcing companies, agencies can boost productivity and improve overall operations.

Here are potential key benefits:

Improved Operational Efficiency

By leveraging insurance outsourcing services for administrative functions—such as policy renewals, claims processing, and certificate of insurance services—to specialized teams, insurance agencies can ensure these tasks are handled quickly and accurately. With experts managing back-office tasks, agencies can experience faster turnaround times and greater accuracy in service delivery.

Cost Reduction and Resource Optimization

Hiring and training full-time employees for non-core tasks can be expensive, especially when considering expenses such as salaries, benefits, and continuing training. By outsourcing, insurance agencies get help from experienced people without the long-term financial burden of maintaining in-house staff. This not only lowers operational costs but also optimizes resource allocation, allowing agencies to invest more in the growth of their agency.

See also: Why Hasn't Insurance Automated More?

Access to Skilled Expertise

Outsourcing partners bring industry-related expertise, providing insurance agencies with access to already trained personnel. These people are familiar with processes such as policy checking, claims management, and other critical tasks. 

Flexibility and Scalability

Whether an agency is experiencing a temporary rise in workload during busy seasons or needs to reduce support during slower periods, outsourcing provides the flexibility required to adapt quickly. Outsourcing also offers agencies an excellent way to scale operations without the challenges of hiring in-house staff.

Focus on Core Business Functions

Day-to-day responsibilities like client relationship management, sales, and business development are crucial to long-term success but often take a back seat when administrative responsibilities pile up. Outsourcing frees internal teams to concentrate on essential areas, improving customer satisfaction and driving growth.

Improving Client Satisfaction Through Outsourcing

By offloading time-consuming tasks like policy checking, certificate issuance, and claims processing to outsourcing teams, agencies can respond more quickly to client inquiries and process insurance documents with greater accuracy. 

How to Choose the Right Outsourcing Company

To begin, agencies must clearly define their current back-office needs. It’s important to choose an experienced provider and one that offers a range of services, including ones you might not need at the moment but might want in the future.

Additionally, it's important to evaluate the BPO company security measures and compliance, to safeguard client information.

See also: A Turning Point for Offshore Wind

Conclusion

Outsourcing provides insurance agencies with processes to streamline operations, boost efficiency, and improve client satisfaction. By delegating non-core tasks to an experienced outsourcing company, agencies can focus on their primary business objectives while reducing costs and maintaining exceptional service quality. 

Leveraging generative AI can transform the insurance sector by enabling dynamic risk modeling that evolves with real-time data inputs. 

Generative AI does this by integrating self-learning models that continuously refine themselves using multimodal data, including text, images, and sensor data. Insurance products can evolve alongside emerging risks, allowing for unprecedented precision in underwriting and claims management and fundamentally altering the competitive dynamics within the industry. 

Understanding Generative AI 

Generative AI models such as ChatGPT can produce content in various forms from data in which it has been trained. The content can be audio, image, or text. Generative AI models analyze information, look for patterns in it, and produce original content at a speed that far exceeds that of any human being. Generative AI processes data like the human brain does because it is based on neural networks.

Generative diffusion models are another prominent part of the generative AI industry. These models, such as DALL-E 2, Midjourney, and open-source Stable Diffusion, produce good-quality photos, video, and sound by reversing information loss because of noise intervention. Noise is a mathematically designed addition to an image. It incrementally blurs the data that is required to create a realistic image, and the model is able to generate the needed image without any data. 

Another paradigm of AI is predictive AI. It uses historical data to foretell outcomes accurately. For example, AI-driven risk assessment tools analyze and study historical data and help companies be insightful about future market trends. Predictive AI models are especially beneficial for businesses in the fintech industry, where forecasting and planning are strategic priorities.

See also: 3 Lessons Learned From Leveraging Gen AI

Enhancing Risk Modeling and Underwriting With AI

AI has improved the accuracy of many life insurance risk models and improved the underwriting process. 

Traditionally, life insurance risk models were based on historical data and estimates of mortality rates. The models could not adapt to capture the complex and changing nature of particular risk profiles. However, AI-driven predictive analytics can use a lot of data from various sources to detect subtle relationships that cannot be found by traditional methods.

Machine learning algorithms can learn from historical insurance data, and other sources like medical records and lifestyle information, then make highly accurate and specific risk assessments. Another branch of AI, deep learning, uses artificial neural networks that mimic the human brain's functioning. 

In insurance, deep learning can assess medical images and detect signs of diseases not observable in traditional medical records. When made a part of risk models, this information leads to a detailed assessment of a person's longevity. 

Also, the integration of real-time data from sources like wearable health trackers, fitness apps, and social media platforms presents a complete picture of risk profiles to underwriters. Advanced analytics and AI, such as predictive analytics, explore patterns that forecast future claims. 

The use of this technology also eliminates manual operations, thus lowering operational expenses and errors. A great example is the improved claims management process. AI-powered claims processing tools can quickly evaluate data from several sources. These can be police reports, medical records, and statements of witnesses.  

Thus, insurance firms are able to make well-informed decisions quicker.  

Hyper-Personalisation 

The use of generative AI presents huge opportunities for insurance and fintech domains primarily due to hyper-personalization. Financial products tailored to the needs of customers, such as personalized savings plans, investment strategies, and personalized premiums, enable organizations to provide a highly satisfying customer experience. 

Personalized customer engagement platforms use AI to manage  the questions of claimants and guide them through the entire claims process.

Additionally, generative AI improves an insurance firm's ability to assess credit risk. Thus, it boosts the ability to approve loans quickly. Such platforms use synthetic data to automate the evaluation of the financial historical and current data of the borrower. Thus, potential credit concerns and risks can be examined effectively. 

See also: 5 Ways Generative AI Will Transform Claims

Generative AI Integration With Existing Systems

Generative AI Integration is increasingly effective in data collection and processing. It is beneficial for sectors like fintech. When it is incorporated with existing systems, companies can find behavioral patterns that they cannot otherwise. 

This maximizes operational efficiency and helps pinpoint the activities of fraudsters. Here are key points for integrating GenAI.

• Identify where you want to integrate generative AI. For example, you may want to integrate it with real-time applications like risk assessment tools. 
• Set objectives or metrics for this implementation, such as customer engagement or cost reduction.
• Evaluate different models on the value of their output, scalability, and integration flow.
• The datasets prepared for training should be of high quality. Also, data governance practices impart greater accuracy.
• Integrate generative AI with your CRM, ERP, or marketing automation software as it gives the most advantage.
• Use strong security measures like encryption and access controls in the systems for data privacy, protection and transparency. 

Potential Challenges and Mitigation Strategies

Integrating generative AI in afintech business's ecosystem is not a straightforward process. It poses some challenges, which include the following:

Bias, Transparency, and Explainability in Decision-Making

The opacity of the AI models (also known as black box) and systems often conceal the reasoning behind their decisions. More important is cognitive bias, which introduces discrimination in the training data set. It begins with data generation and continues until the deployment of the AI system. 

Developers should embed fairness and transparency principles when structuring AI algorithms. The models should take into account fairness metrics and be able to give explanations for their decisions. The explanations should be clear to humans. 

This is particularly important in finance because unclear decisions in this high-stakes domain have severe consequences. Explainability can be introduced in many ways. Some major techniques are using interpretable models like decision trees, creating LIME, or Local Interpretable Model-agnostic Explanations, and using set standards. The standards should warrant AI systems to give clear reasons for their decisions. 

Model Interpretability

Interpretability in generative AI models is crucial for transparency. It also makes sure that senior insurance executives understand the outcomes produced by the model and validate the decisions. 

With time, AI implementation is subjected to regulatory standards. It makes interpretability essential for ensuring compliance with the regulations. It also enhances the trust and accountability of advanced technologies, which improves the rate of integration of generative AI across fintech and insurance domains. 

Innovative Approaches to Adaptive Insurance Solutions

AI algorithms that are trained properly analyze the risk profile comprehensively using factors beyond traditional demographics. The models re-evaluate existing customers and emerging risks whenever new information becomes available.

The following techniques and practices will aid in the development of insurance products that adjust to emerging risks. By doing so, they usher in many positive changes in the actuarial practices.

• Set clear objectives to achieve real-time insights regarding risk.
• When collecting data, make sure it is high in quality and relevant. 
• Train generative AI models using historical data so they can determine patterns that point to potential risks.
• Configure continuous data feed from integrated sources. It will keep your models updated in real time.
• Techniques like stream processing facilitate continuous ingestion and analysis of data streams from different sources in real time. 
• Train the model to automatically allocate risk scores to various processes and transactions based on the analyzed data.

However, to fully take advantage of the opportunities of generative AI, you need a thorough assessment of the business ecosystem by professionals who specialize in building and integrating generative AI. 

Well-thought-out investments in generative AI will help in the following ways:

• Enhanced product offerings
• Greater market reach 
• Efficient allocation of spending
• Operational intelligence 

Case Study

1. Metromile

Metromile, the mileage-based auto insurance company acquired by 2022, has successfully applied AI. It did so by developing ideal insurance products for its core audience to widen its market reach. It considered technologies that would evaluate potential risks, send personalized messages to users, and take on other important tasks. 

Metromile developed an interesting model of insurance whereby policyholders only pay per mile driven. It evaluates how a car is driven and tailors premiums based on this. Therefore, a policyholder who drives less will pay lower premiums. 

Metromile's unique approach and solution were widely praised. It resonated with drivers that had low monthly mileage and those in public transit or the remote workforce.

2. Appian Documentation Tool

Appian, founded in 1999, implemented AI in their Appian Documentation Tool for dynamic risk management. The platform uses AI for classifying documents and extracting important data automatically. This process also validates information.

The tool enables organizations to achieve increased visibility and maintain control over all their risk management activities. Additionally, the software's AI-based data fabric gives a single view of a firm's risk exposure. The risk reports produced by the tool are tailored to the needs of different stakeholders. The reports provide risk analytics in real time and support better decision-making.

Appian Documentation Tool has become a top AI-powered tool used by businesses in the banking sector. It has also been recognized by leading technological research and consulting firms like Gartner. 

3. MillerMo Employe Tool

MillerLabs took advantage of generative AI to enable insurance executives to automate routine tasks and fuel an environment of collaboration and innovation. The MillerMo Employe Tool uses the OpenAI GPT4-32k model and has a variety of functions. It serves as a personal assistant and facilitates proofreading and content production, data set analysis, and software development.

The tool automates administrative tasks for the company's employees so they can focus on the more significant aspects of their roles. 

Conclusion

Data analysis is very important for risk management in the insurance and fintech sectors. However, legacy systems compromise the ability to assess risk in today's ever-changing environment. 

Generative AI technology, which is evolving quickly, provides a great opportunity to enhance data analysis. With more accurate risk assessment, tailored policies, and seamless processes, AI improves customer experience. AI is, therefore, a strategic enabler that insurance and fintech firms have to adopt if they are to retain their competitiveness. 

Artificial intelligence (AI) has enhanced efficiency, accuracy, and customer satisfaction in claims management while reducing manpower and errors. In fact, AI-driven claim validation has improved accuracy to 99.99% and boosted operational efficiency by 60%.

By 2030, automation could replace over half of current claims activities — an indication that AI is not just a trend but will have a lasting impact in insurance.

However, successful implementation requires forward-thinking insurance leaders to take a balanced approach that recognizes both AI's potential and its limitations.

See also: How to Optimize Insurance Claims Management

How AI is improving claims management 

AI improves operational effectiveness, reduces costs, and improves accuracy and has an especially big impact on workers' compensation. Here’s how AI is reshaping claims in key areas:

1. Workers’ compensation (WC) claims — AI improves WC claims management by providing predictive insights and reducing costs. A study shows AI integration can reduce legal involvement in lost-time claims by 15%, saving 5% in claim costs, or $3.5 million annually for insurers handling $70 million in claims. For instance, AI can predict the life of a claim, estimate settlement amounts, suggest light-duty jobs, and allocate funds accordingly, reducing the need for consultants and minimizing human errors.
2. Fraud detection — AI can flag suspicious activities, such as claims reported on Monday mornings or at the end of the construction season, and alert human claims professionals to take a closer look. The AI enhances efficiency and reduces the resources needed for investigations.
3. Catastrophe (CAT) modeling — AI can predict and respond to natural disasters. Adjusters use AI for heat mapping to identify areas likely to be hit by storms, allowing insurers to prepare and advise clients on precautions to take. For example, AI predicted Hurricane Ida’s path, enabling insurers to effectively allocate resources and promptly support affected clients.
4. Budgeting — By analyzing past claims to predict future costs, AI can help companies plan for future claims and reduce financial surprises.

AI limitations in claims management

While AI has revolutionized claims management, it is not without its limitations and is not a foolproof method.

For one, AI’s effectiveness is highly dependent on the quality of data input, including claim amounts and quantities. An estimated 83% to 92% of all AI projects fail due to poor data quality. Ensuring the data is accurate and current remains a significant challenge.

AI also may struggle to make the right decisions in intricate situations. For example, claims involving complicated medical procedures, such as back surgeries, still require human oversight. AI might assist in processing but cannot fully replace human judgment. Insurance companies often have AI approvers — humans who review AI-generated decisions for accuracy and legality, particularly for complex claims. This human review is crucial.

Importantly, AI lacks the human empathy necessary for evaluating claims involving pain and suffering. While AI can calculate settlements based on medical expenses and wages, it cannot account for the emotional and psychological impacts of an accident. For instance, when determining compensation for someone who needs a knee replacement, AI won’t consider their co-morbidities, past athletic activities, or current job status. These nuanced considerations require a human touch.

See also: Making the Claims Process More Efficient

A balanced approach 

AI has become crucial in claims management, enhancing efficiency and accuracy. However, it will never fully replace human involvement. The future lies in combining AI’s strengths with human expertise to handle complex claims and provide empathetic service.

Successful insurance companies will leverage AI for operational efficiency while addressing its limitations with human oversight. Partnering with agile, AI-savvy risk management experts will be key to adapting to the evolving landscape.

AI will continue to shape claims management, but a balanced approach will ensure the highest standards and benefits for both insurers and clients.

The effects of alcohol and marijuana may be well-known, but they present a less understood side effect for the insurance industry. Although a claim involving one or both substances could seem straightforward, far too often carriers encounter complications during the process. The solution tends to veer from black-and-white into gray.

Case in point

Consider this true scenario. A policyholder drowned while kayaking in rough waters and was found three days later. The blood alcohol concentration (BAC) measured 0.12%, well above the national limit of 0.08%. Believing this contributed to the accident, the carrier denied the claim. Yet what seemed like a simple closed case morphed into one far more complex.

The beneficiary argued it was decomposition that led to the heightened BAC level. Indeed, photos of the death scene and the autopsy showed noticeable body decomposition. Research indicates that high blood alcohol levels may develop during decay of organic material and that a level as high as 0.2% does not necessarily indicate alcohol was consumed before death.

Also, blood alcohol levels during an autopsy are valid for up to 48 hours, assuming proper protocols are observed. Because the insured was discovered after 72 hours and the decomposition process was under way, the BAC was found to be inconclusive and the denial was overturned on appeal.

Alcohol vs. marijuana

The impact of alcohol on life and accidental death and dismemberment (AD&D) claims is more defined than that of marijuana. Alcohol has been legal in the U.S. since 1933 (post-prohibition), nearly as long as marijuana has been federally illegal (since 1937). This creates some stark differences between the two substances:

• Alcohol laws are consistent nationwide, as is a federally recognized intoxication level (0.08% BAC). The same does not hold true for marijuana. Regulations vary by state, and there is no standard intoxication level.
• Alcohol has an established testing protocol to determine impairment – blood alcohol concentration; marijuana does not. The level of tetrahydrocannabinol (THC), the active ingredient in cannabis, does not necessarily equate to impairment.
• A history of alcohol use does not affect the current BAC; the opposite is true for marijuana, where prior use can affect current THC levels.
• Alcohol’s metabolic process is understood, and the effects are predictable. In contrast, the metabolism process for marijuana is more complex and its effects are unpredictable. For example, THC can be stored in body fat for up to 28 days.

Regulations for marijuana are still under development, and it could be years before there is national clarity matching what exists for alcohol.

See also: Why Are So Many Dying on U.S. Roads?

Challenges with alcohol and marijuana claims

AD&D benefits tend to be approved when the loss is the direct and sole result of an accident with no contributing causes, such as alcohol or marijuana. Still, the industry faces a few obstacles that complicate the process.

Unclear policy language

Ambiguous policy language creates confusion for claim handling. The absence of clear language can lead to inconsistent decisions, and appeals can go in the claimant’s favor.

Inconsistent practices

Carriers across the industry have varying approaches to risk management. While some insurers strictly adhere to policy language, others may demonstrate flexibility. Even the number of resources used for claims differs by carrier.

Lack of direction

The claim evaluation process can be riddled with ambiguity, making the decision less straightforward. Vague policy language can contribute to uncertain claim direction.

Lack of understanding

Most policyholders and beneficiaries lack a true comprehension of policy provisions and claim processes, especially where exclusions apply.

Different state, different law

Unlike alcohol, no federal standards for marijuana exist. Depending on the state, cannabis can be illegal, legal for medical use, or legal recreationally.

Documentation is key

Obtaining the proper documentation is critical for reaching an accurate decision. Examples of records to consider include:

• Policy language
• Death certificate
• Coroner or medical examiner report
• Toxicology report
• Police report

The latter three can help determine policy exclusions for factors such as being under the influence.

Classifying marijuana

Because marijuana has been illegal in the U.S. for nearly 90 years, minimal medical science exists. Merely classifying marijuana presents a challenge. Because THC can cause multiple reactions to the central nervous system – excitement, impairment, depression – it does not fit into one drug category.

The Drug Enforcement Administration (DEA) has proposed reclassifying marijuana from a Schedule I drug – those without an accepted medical use and a high potential for abuse (e.g., heroin, LSD) – to a less regulated Schedule III drug (e.g., ketamine). If the change is made, it could create an avenue for expanded testing and established standards for law enforcement and the insurance industry. The road to reclassification is likely to be lengthy.

See also: Behavioral Science and Life Insurance

The state of marijuana

Because the legality of marijuana varies, claim decisions involving it require familiarity with the laws for the state in which the insured resided at the time of death.

Every state has laws addressing alcohol-impaired driving and drug-impaired driving, but the latter are more complex and difficult to enforce and vary substantially by state. One of two laws tends to apply when considering drug-impaired driving:

• Zero tolerance: It is illegal to drive with any measurable level of specified drugs in the body.
• Per se: It is illegal to drive with a level of specified drugs in the body that exceeds certain limits.

Conclusion

Alcohol- and marijuana-related claims exemplify the challenges inherent in claims adjudication. The complexities, especially for marijuana, create a significant challenge for carriers evaluating a claim’s validity.

Solid risk management protocols require a more detailed review of policy language, documentation, and state laws.

The impact of alcohol on claims is largely clear, but marijuana regulations vary by state and will continue to change. Insurers should consider broadening their exclusion language to address the use of marijuana and other drugs.

This is an ever-evolving process. The experts at RGA will continue to monitor its progress and provide future updates on alcohol, marijuana, and their complex impact on life and AD&D claims.

Driven by the need to improve operational efficiency, enhance policyholders’ experiences, and reduce cost-to-serve, more insurers are deploying automation. Automating repetitive tasks can streamline workflows and allow for data-driven decisions with greater accuracy and speed. However, many insurers struggle to scale these solutions to full implementation. In this blog, we will explore how to move from pilot projects to organization-wide adoption efficiently. 

See also: Why Hasn't Insurance Automated More?

Create a Vision

Executives need to start by identifying areas where intelligent automation will have the biggest impact and aligning plans with overall company goals. Instead of prioritizing short-term, tactical savings, this usually entails bigger, long-term objectives. For instance, automation in insurance claims processes reduces errors, fast-tracks settlements, and improves customer satisfaction.

Consider factors such as business process complexity, scalability, workforce readiness, stakeholder engagement, and current technology infrastructure to get the real picture. The goal is to make the business more intelligent, flexible, productive, and competitive.

Assemble the Stakeholders

Without obtaining buy-in from key stakeholders, it is nearly impossible to transform an intelligent automation plan into a living program. So, from the outset, involve people such as senior IT personnel, who can help with many essential issues, including security compliance, auditability, and the supporting infrastructure, configuration, and scalability. Their expertise is vital in ensuring that automated processes, such as claims handling or underwriting, are secure and compliant. Working with IT should be a continuous process to mitigate operational consequences.

Develop the Plan

To automate insurance processes, you should collect the right data during the process definition phase. This requires subject experts as well as a walk-through with the appropriate stakeholders to see how the automated processes will differ from their manual counterparts.

Development commences once the relevant business and process design authorities accept the suggested blueprint. Your strategy should ideally include: 

• Clear Objectives: Determine what success looks like to you, along with the metrics that will guide the implementation.
• Technology Selection: Select the right tools and technologies for automation. This may include robotic process automation (RPA), artificial intelligence (AI), machine learning (ML), etc. 
• Change Management: Prepare for the organizational changes that automation will bring. Provide for employee training to minimize resistance to change. 
• Pilot Testing: Test the waters with pilot projects before committing to a full-scale rollout.
• Scalability Planning: Make sure the insurance automation solution is flexible and accommodates changing business needs. 
• Continuous Improvement: Make provisions for regular reviews to assess performance, identify areas for optimization, and adapt to changing business needs or technological advancements.

Execute the Plan

Now that you've developed your strategy, it's time to put it into action. Begin with the pilot project, perhaps automating a specific claims process or policy renewal workflow, and closely track its progress. Collect performance data and obtain user comments. Address any challenges that develop as soon as possible and use this information to improve your strategy.

Once the pilot run is successful, start scaling the automation throughout the organization. This phase includes extending the solution to more processes, such as quote generation, underwriting, and policy management automation, and possibly different departments such as finance or legal. It is critical to maintain communication with all stakeholders, solve any new difficulties, and continuously improve your automation techniques based on feedback and performance statistics.

See also: Why Touchless Claims Is a Must-Have in 2024

Measure and Track

The right key performance indicators (KPIs) provide tangible goals to strive for and clear milestones to track the progress of your initiatives, including aspects like automation in insurance underwriting. The KPIs do more than just measure. They measure what counts. They produce insights that help create a compelling business case for implementing or enhancing automation. KPIs might include metrics like reductions in claims processing time or improvement in customer satisfaction scores. They assist in answering issues such as, "Are we on track to meet our objectives?" and, "Do we need to change course?" With these KPIs, insurance leaders make strategic decisions, drive a culture of automation, and make necessary changes to meet goals. 

Conclusion

Scaling automation projects is no small endeavor. However, by embracing automation, forward-looking insurers position themselves to streamline operations and improve customer experiences.