Welcome to the new age of customers!  

Nearly every organization that sells its products and services in a B-to-C market goes through product, channel and service shifts brought about by consumer demand. Shifting business strategy to meet the customer is nothing new. Sometimes these changes are enacted through acquisitions. Sometimes they are brought about by greenfield business units. However, they come about, they are essential for long-term survival and growth. Consider some popular brands you know, and you can see the shifts in action.

Banana Republic was once a safari and travel clothing outfitter that even sold books. QuikTrip and Wawa were small-scale "convenience" grocers without fuel sales. Sony’s most popular product was once a Walkman tape player. KitchenAid was just a mixer company.

Every company is shifting as customers shift, and insurance is no different.

Insurers are in a transition between what they once were and what they may one day become. You may see this transformation happening in your own organization. What if one day your company generated significantly more profitable income from services, side offerings and partner products than it did from some of your core insurance products?

If you knew this could happen, how would it change your plans? The idea is not unprecedented. In a business world flush with new opportunities, executives commonly push their companies in the direction of customer demand and profit, not necessarily in the direction of the organization’s core competency.

That means it is always the right time to understand what the customer is thinking and needing and where customer demand seems to be growing.

Majesco recently released its annual consumer trends report, Enriching Customer Value, Digital Engagement, Financial Security and Loyalty by Rethinking Insurance. We assess the top-of-mind issues for today’s customers and look at how Gen Z and Millennials especially are looking for ways to achieve financial wellness — across all financial aspects of their lives, including P&C and L&AH products. How will technology-enabled products and value-added services add up to optimal insurance offerings for today and the future?

Using the report as a springboard, let’s look at what is changing year over year and where 2022 trends are pointing insurers for their 2023 strategies and beyond.

Gen Z and Millennials — “Serve the future me”

The future is all about the customer, and in insurance it is all about the customer’s future.

Insurance’s traditional products have always been pivotal in creating peace of mind, but new and expanding risks, market dynamics and evolving needs and expectations of customers require new ideas and approaches. Customers are seeking simple, holistic, direct experiences within a digitally immersive model. They are looking for real security over their lives and property, security that goes beyond traditional risk products and channels.

Insurers must give serious thought to offering value-added services that complement risk products and in some cases reduce or eliminate risk; providing multiple channel options, including new partnerships and embedded options and leveraging new data sources to create personalized pricing and underwriting. Beyond all this, insurers need to grasp how their company fits the future vision of a customer’s whole security picture. How much of this picture is theirs to paint?

Creating security in times of change

Resilience and financial well-being are essential to living in a world filled with risk. Customers need to be able to return to the status quo after an event – whether for assets like our businesses, homes or vehicles or for our own personal or employee health and well-being.

Customers are seeking help with managing all the complexities of their life and finances. They are expanding their view of financial wellness. They want confidence and security. Customers want an expanding focus on the prevention of losses, creating risk resilience and financial well-being.

See also: 3 Insights on Millennial Insureds

Top-of-mind issues

The tumultuous times experienced by many consumers are reflected in their responses regarding top-of-mind issues. In this year’s survey, we see that they are concerned about household finances, inflation, crime and planning/saving for retirement. (See Figure 1) The full list deserves a close review. Notice how many of the issues have a greater priority for Gen Z and Millennials than for Gen X and Boomers. Look closely at those issues garnering higher than 50% responses.

Financial well-being is about feeling secure and in control, managing your money effectively whether for the day-to-day, dealing with the unexpected (like risk and losses), or preparing for the future. This is why crime and inflation and household budget have become top issues for consumers. Uncertainty about the future will be a motivational driver for all consumer purchases in the short term.

Figure 1: Consumers’ top of mind issues

Both generations are concerned about cyber/data security/ID theft. Employment emerged as a crucial issue for Gen Z and Millennials, with a 23% stronger view in finding or keeping a job, 37% in how they would like to work and 21% in considering gig/contractor work options as compared with the older generation. This shift in employment expectations has a significant impact on employers in terms of group and voluntary benefits; cyber risk and worker safety – leading to a demand for different insurance products.

Likewise, environmental, social and sustainability-related areas are also of keen interest to the younger generation, with a 16% difference compared with Gen X and Boomers and 15% for increasing risks from severe weather. In response, some insurers are developing risk appetites based on net-zero and carbon reduction pathways, the introduction of sustainable insurance products and investments in funds that back or support insurance products.

Digital technology use trends

Gen Z and Millennials continue to outpace Gen X and Boomers in the use of digital technologies or digitally enabled businesses. Compared with last year, both generations were the same in usage, with the exception of a few key areas, as reflected in Figure 2. 

The strongest use is in the finance category, where digital payments reflect a gap of 27% to 28%, depending on the digital payment option. Despite the high usage levels, they are lower than last year’s levels, with a 25% decline in Zelle/Venmo for Gen Z and Millennials and a 15% drop for Gen X and Boomers. These declines do not align with the growth in Venmo usage, which processed $230 billion in total payment volume in 2021, a 44% increase year-on-year, and reported over 70 million users, mostly based in the U.S. This strong use highlights the need for insurers to offer alternative payment options.

Figure 2: Use of technologies and participation in trends, 2021-2022

The smart devices category showed moderate year-over-year usage increases for both generations. In particular, video security/detectors saw a 7% increase for Gen Z and Millennials and a 9% rise for Gen X and Boomers, reflecting a focus on protection, which aligns with the top-of-mind issue of crime. Both groups are willing to spend money that will improve their peace of mind.

Usage of fitness trackers increased by 4% for Gen Z and Millennials and 5% for Gen X and Boomers. This highlights their focus on health and wellness, another top-of-mind issue. The younger generation outpaced the older segment in usage by 14% overall.

Mobility saw an interesting increase of 5% by Gen X and Boomers for the usage of ridesharing services, while the younger generation saw a decline of 13%, bringing the two generations closer in overall usage of 23% to 28%. This aligns closely with industry usage statistics of 36% by Americans, which is double the usage since 2015. This continued increase highlights the shift in a broader focus on mobility options that insurance will need to meet. Can insurers become adept at insuring people on the move without their own vehicles?

Gen Z workers are more likely to have independent jobs or multiple jobs than older workers and are less likely to expect this period of financial insecurity to end, creating high levels of doubt about their eventual ability to either buy homes or retire. These views reflect a potential significant disruption in the “traditional lifecycle” of people and have significant implications for insurers in terms of insurance from group and voluntary benefits to home insurance.

Products and services demographic use trends

In looking at the holistic financial wellness aspect, the survey covered four areas: Finance, Insurance, Life/Health and Personal/Home, as reflected in Figure 3. The Finance and Personal/Home categories, as well as some types of insurance, reflected the strongest areas of focus for both generational groups, highlighting areas of opportunity for insurers.

Leading the financial wellness focus are bank account and auto insurance, with 85% to 92% usage by both generations. Following them are homeowners insurance, with 47% to 62%, health insurance through an employer, at 44% to 47%, and investments, with 40% to 47% usage.

Specifically, for homeowners insurance, Gen X and Boomers’ 15% differential reflects higher home ownership as compared with the younger generation.

This protection gap for both homeowners and renters insurance has existed for years and presents a challenge and opportunity for insurers to educate and engage consumers on their value by making them easier to purchase and use.

Figure 3: Products and services used

What stands out are the Personal/Home usage results. Amazon, video streaming and mobile phone usage of 64% to 93% reflect a strong alignment and loyalty to digital high-tech products and services. The usage and loyalty offer both a challenge and opportunity to insurers, in that usage has influenced consumers’ digital expectations for shopping, paying and customer service.

Amazon offers a pre-built audience for additional products or services that the company is beginning to enter. The recent opening of the Amazon Insurance Store in the U.K. and Amazon Clinic, a “virtual care storefront” in 32 states in the U.S., reflect how “big tech” is planning to enter the market through partnerships with other insurers. Likewise, mobile phone companies are increasingly looking at a broader relationship to “own the customer.” Verizon launched Family Money in 2021, a banking app and pre-paid debit card for Gen Z that allows parents to monitor their children’s spending and saving. Verizon’s launch follows its competitor, T-Mobile, which launched a digital banking platform in partnership with BankMobile in 2019.

See also: Tackling Turnover Amid the Great Resignation

Where does this leave insurers in providing a holistic financial picture?

As Millennials and Gen Z take the lead as the dominant buyers, the ability of insurers to capture, let alone retain them as customers will be severely challenged unless they develop a new approach. This newly dominant generation views and values things much differently. Their loyalty can be fleeting if nothing of value keeps them with a brand or company. They are not satisfied with traditional insurance processes, products and business models. They have grown up in a digital world. They expect and demand digital capabilities. They want new products that will align with their activities and behaviors. They want services, coverage and interactions that are available to them whenever they want them, and however they wish to engage.

At the same time, they need an education on how to properly see their own risk and how to “defend” themselves against the ever-increasing risks that threaten them each day. Can insurers begin serving their customers with expanded offerings, including digitally enabled preventive services and products that cover, not just their physical presence and property, but their financial and online presence, as well? Should insurers consider taking on an expanded set of banking and investment services or partner with someone who can provide them? Can insurers seamlessly provide for a healthier, more stable work and life environment that fits with today’s and tomorrow’s work and lifestyles?

For a deeper look at customer trends across all lines of insurance, be sure to read Majesco’s latest report, Enriching Customer Value, Digital Engagement, Financial Security and Loyalty by Rethinking Insurance.

For this month’s interview, ITL Editor-in-Chief Paul Carroll talked with Dan Swfit, the founder and CEO of “buyer experience” consultancy Numentum. Dan has extensive experience in helping people sell, especially through social media. He launched Sales Navigator for LinkedIn a decade ago, took a senior position with social media platform Sprinklr during its pre-IPO phase and founded Numentum nearly six years ago to help salespeople understand how buyers think and act.

ITL:

While your experience with training spans industries, when you zero in on insurance, what advice would you give agents and brokers?

Dan Swift:

I look at what the people in my life who are associated with insurance could be doing that they aren’t doing.

The folks we have our life insurance with, our long-term disability, our house and all that—none of them are on LinkedIn. So they don’t tell me anything about the organization they represent or their agency or their app. There’s very limited information about what they specialize in or about their experience. These people offer nothing to make me think they’re a standout. And there’s nothing about them as human beings.

I’d like to know something about the human beings I’m dealing with, not just send them money.

Even if agents are on LinkedIn, they aren’t very active. I want to be educated by them. I want to have the value of what I got from them reinforced all the time in my feed through a combination of stuff that the agency might have produced or that the carrier has. I saw some great stuff from the Hartford, designed specifically for small business owners like me, but it came through the corporate channel, and I just happened to see it in my emails. I would prefer to get that sort of thought leadership directly from the folks who sold me the insurance.

I chat all the time with other small business owners, and we talk about things like who we have insurance with, but I don’t recommend anyone. The folks I deal with have never done anything to make me say, “That’s my guy or that’s my gal.”

At Numentum, we train people on business acumen, and agents and brokers could do a lot just by showing they take an interest in their clients. Know that I have three young kids. Know what I’m about and who I am. When you come to me and try to upsell me, you won’t sound so transactional.

ITL:

That sounds spot-on for existing customers. What about leads on new ones? What would you recommend?

Swift:

Most agents and brokers specialize in a particular product line or aspect of the market or a specific industry, so tell me that in detail in your LinkedIn profile. When I look at your profile, I can then see your credibility and how you helped people just like me. [Here is Dan’s profile, in case you want to see how he puts his ideas into practice: https://www.linkedin.com/search/results/all/?keywords=dan%20swift]

When you reach out to someone on LinkedIn, don’t just connect and start selling. Connect, learn, nurture and then sell. Send me something that’s going to bring value to my life, maybe related to my being a small business owner. Then you’ve earned the right to ask for a conversation.

Customize the initial connection request. Don’t just hit the “connect” button. Most people have something in their profile or have posted something you could mention.

And use Sales Navigator. I haven’t worked for LinkedIn for years, so I get no credit for saying this, but, in this industry, there is literally no better tool. The tool lets you prospect based on industry type, size of organization, geography, job title and so on. You can also map your own professional network, which is why it's so important to connect on LinkedIn every day with anyone and everyone you have any interaction with – all your customers, all the people in your centers of influence, who you went to school with, play golf with, whatever. Because all the people in your network are then mapped to the companies and people you’re trying to reach.

When you ask for introductions, you can be specific. You don’t say, “Hey, Dan, can you introduce me to someone?” You say, “Hey, Dan, can you introduce me to Sam?”

ITL:

How about beyond LinkedIn? What sorts of other tools and techniques would you recommend?

Swift:

Facebook is the obvious one for connecting with customers. If you get to a certain place in a relationship, you earn the right to go off the professional network and on to a personal one, where you can learn about your customers as human beings.

Agents and brokers should use other social channels, too, for sure.

They could also be using video a lot more. I don’t just mean all the virtual conversations we’re having these days. I mean before and after those conversations.

Imagine you've got a conversation with a potential client you haven't met yet. Why not send a quick, 60-second video to the person? There are all kinds of tools that make this easy. You say, “I just wanted to put a face to a name for you before we speak on Thursday. Really looking forward to it. So-and-so speaks incredibly highly of you. Oh, and by the way, I've attached to this email some information that I thought you might find helpful as a pre-read before we speak.”

What a human way of showing up in that person's world.

Then you have the conversation, and the person might say they can’t make a decision without talking to their spouse. So you follow up with another quick video that can be shared with the spouse. You say, “I really enjoyed our chat about XYZ. These were the things we spoke about. This is what we thought made sense, and why. I look forward to speaking with you again on Tuesday. In the meantime, I’m always open for conversation.”

Imagine how much you’d stand out if you did that. People would think, Wow, this person really has it together. It’s a confidence build. It's a trust thing. It's an experience thing.

But people don’t do it.

ITL:

That sounds great. I can just imagine how I’d react if someone did that to me.

Any other particular tips?

Swift:

Despite all the technology and all the channels, I keep coming back to the need for relationships and to the simple technique of making LinkedIn part of your day-to-day. That doesn't mean living on it. It just means that any time you meet anyone, you connect on LinkedIn. Mark, the guy outside my house right now doing our landscaping, is a small business owner. Maybe when I look at someone I’d like to meet, I see that Mark did his landscaping.

“Can you introduce me?”

“Sure. Here’s his number. Just give him a call.”

ITL:

That’s perfect, but before I let you go, tell me a bit about Numentum, which is a new incarnation for you since we last talked. What does it mean to be a “buyer experience” consultancy?

Swift:

Most people we train have never bought something on behalf of a big corporation. How can they sell if they’ve never been on the purchasing side? So we teach them how buyers buy, including what issues they consider and how they get consensus. We teach salespeople how to engage with the buyer in the first place, how to educate and influence the buyer, how to take the person through a sales process so the buyer ends up saying, “I want to give you business,” versus feeling like they’re being sold to.

ITL:

I read an article the other day about how the CEO of Uber has begun driving occasionally – and had his eyes opened to issues he somehow never knew his drivers were experiencing. I’m sure that putting sellers in the buyer’s seat is thoroughly enlightening.

Thanks, Dan, for taking the time. It was great to catch up a bit.

About Dan Swift

In 2012, Dan joined LinkedIn to launch LinkedIn Sales Solutions and introduce LinkedIn Sales Navigator. Under his guidance, Navigator grew into a $1 billion+ product used by top B2B sales organizations. Afterward, Dan helped drive Sprinklr's growth to $500 million+ ARR and a $4 billion+ valuation. In 2018, he founded Numentum, a buyer experience consultancy that partners with companies like Vodafone Business, SAP, and RELX, boosting their digital buying strategies. Dan's influence also extends to advising high-growth firms, notably contributing to Accompany's $270 million acquisition by Cisco in 2018. An active member of the Forbes Business Development Council, Dan is a husband, father of three, former rugby player, and aspiring golfer.

I've had the pleasure of knowing Dan Swift for nearly a decade. He appeared on my radar when he made a splash as the director of social selling at LinkedIn, where he launched the Sales Navigator, which became a billion-dollar line of business. He kindly agreed to join my advisory board at Insurance Thought Leadership. 

Dan tapped into his inner entrepreneur even more when he left LinkedIn in 2015 and became a divisional vice president at Sprinklr, a social media management platform that later went public and currently carries a $3.6 billion market valuation. 

He struck out entirely on his own in early 2018, with a business he recently rebranded as Numentum. He calls it a "buyer experience" consultancy -- he helps salespeople put themselves in the shoes of buyers so they can see how to best fit into that process, and he has a growing roster of major clients. 

So, I was sure he'd have some useful advice for agents and brokers -- and he certainly did. 

The biggest surprise for me was something that now seems blindingly obvious but that had never occurred to me until Dan mentioned the idea. He suggests that, shortly before meeting a prospect for the first time, an agent or broker email a 60-second personal video. The ostensible goal would be to associate a face with the name of the agent, but think how much a video like that would surprise a prospect and how much that agent would stand out. 

Dan also suggests a very short follow-up video after that first meeting, summarizing the key points in a way that could be shared with the prospect's spouse -- while again making an impression.

He goes into detail about his video idea and lays out some other suggestions, too. I think you'll feel his enthusiasm bursting through his words even though you're just reading them, not hearing them on a video call, as I did during the interview.

I hope you'll read it.

Cheers,
Paul   

LEMONADE'S 'SYNTHETIC AGENT' NONSENSE

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INFLATION HITS HOME (INSURANCE)

Here's how insurers can adjust premiums to keep pace with inflation and ensure appropriate coverage while building customer relationships.   

THE KEY FOR AGENTS: LIFELONG LEARNING

Here are seven principles for a disciplined, strategic approach to gaining all the benefits that come from lifelong learning. 

HOW MILLENNIALS REVOLUTIONIZED LIFE INSURANCE

Millennials are revolutionizing the life insurance industry from the inside out, imposing their reach and influence on every aspect.

AGENCY/BROKER CONSOLIDATION

Rapid technological advancements, changing customer expectations and economic headwinds are reshaping distribution in insurance.

HOW TO GUIDE AFFLUENT CLIENTS

Here are four best practices to help wealthy clients understand their insurance issues and avoid claims losses.

The traditional method of lead allocation can burden one agent while depriving another of sufficient opportunities. To address this issue, sales engagement platforms have streamlined and improved lead allocation using a rule-based system. An ML-based system utilizes both the leads' and the agents' data fields to calculate a 'conversion propensity score' that finds the optimal matches for conversion. Implementing ML-based lead allocation can be a game-changer for carriers looking to sharpen their competitive edge and take the lead.

Generating quality leads through the website, social media, events, and mailer campaigns are something insurers are doing aggressively to win customers and gain market share. Alright, you get those leads - what next? You allocate it to the next available agent, via the trusted round-robin or a random allocation method. At least this is what would happen ten years ago. At best, an agent in the vicinity would be given that specific lead to chase. End of story.  Actually, the story never takes off.

With such an approach,

• There is a burden of leads on one agent, while another agent has insufficient opportunities
• A lead’s need may not be addressed enough for them to convert into a customer
• Managers need to constantly monitor this method making it unscalable

With technologies like sales engagement platforms, lead allocation has become more streamlined and intelligent. The application uses a rule-based allocation system to help allocate leads better, quicker. How does this work?

The application typically provides users (agent managers) an intuitive interface to build their parameters. For example, 

1. They could allocate new leads based on the lead source; from social media, from the website, or from a call center etc. and route it to a certain agent
2. Or, they could assign leads based on product - if it is health insurance, it can go to Jack, life insurance can go to Andy and so on
3. Agent managers could also allot their leads based on geographical locations, if in-person interactions is a huge factor in the lead journey
4. Or understand if the lead prefers in-person conversations, or not, in which case the lead could go to an agent in a different location but tenured in a specific type of insurance selling.
5. Beyond this, agent managers could assign leads to the first person who responds to the lead notification, the agent with the highest conversion success, or simply based on agent availability. 

With a combination of these parameters, based on the insurer’s requirement, a successful rule-based lead allocation system can be implemented. This method of allocating leads has significantly boosted lead allocation practices helping insurance organizations gain more conversions, with faster movement through the lead journey.

Happily ever after? ChatGPT says no, there’s more! 😈

With the world looking at AI and Generative AI applications as the next frontier, cutting edge sales engagement platforms are leveraging ML-based allocation methods to improve things further! 

ML-based rules allocation can bring in a superlative improvement in lead allocation efficiencies.

How does this work?

Here the rule-based allocation engine works in tandem with the ML-based allocation algorithm. So not only does the system comprehend lead attributes, it also recognizes the actions performed by the lead over time. As a starting point the lead passes through the rules-based allocation system that has been customized based on the parameters defined by the carrier. After filters on source, location, product need and more, the results are fed to the ML-based allocation system. 

Here’s where the magic happens.

An ML-based system uses both the leads’ and the agents’ data fields to calculate a ‘conversion propensity score’ - what is the percentage of success if lead A is paired with agent X? 

The match with the highest score also has the highest chance of conversion.

If it sounds like ‘matches made in heaven’ - it actually is!

The ML model keeps learning from each record and adjusts the algorithm to find best possible matches for conversion. 

ML-based allocation of leads assure 80% + accuracy in mapping the right lead to the agent - this can prove to be a game-changer for carriers seeking to sharpen their competitive advantage and take a lead. 

As insurance sales leaders seek ways to optimize operational efficiency in the distribution chain, lead allocation is an important area with a large scope for improvement. It is time to measure the,  

1. Leads being generated versus allotted
2. Lead allocation efficacy
3. Tools that can help in improving the efficacy  with a focus on using technology like AI and ML to optimize this further. 

Wait, there is room for a sequel too!🥁

The inherent ability of AI is to learn and improvise. With time, the algorithms gather the data presented to them, combine it with experience and are able to allocate leads with even more accuracy!  Insurance leaders can leverage this for better product positioning and faster conversions. 

KEY TAKEAWAYS:

--Insurers can access new data streams to identify new risks, such as to city infrastructure, including smart grids, intelligent transportation systems and IoT-enabled devices.

--New, sustainable and efficient transportation systems, such as electric vehicles, bike-sharing programs and autonomous vehicles, create unique risks that require coverage.

--Smart cities face increased cyber risks, and insurers should collaborate broadly with officials on new legal and liability issues.

--Insurers can encourage eco-friendly initiatives by offering incentives.

--They can also leverage telematics data collected from connected vehicles to offer usage-based insurance policies.

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Smart cities and insurance are intertwined in various ways. As urban areas around the world continue to embrace smart city technologies to improve efficiency, sustainability and quality of life, insurance plays a crucial role in mitigating risks and supporting the development and sustainability of these cities.

Urban areas are growing at lightning speed. Worldwide, there are now 37 cities with populations of over 10 million, and, by 2050, around 68% of the global population will live in urban areas.

As more people move to cities, rapid technological advancements have given rise to innovative solutions that enhance the quality of life and streamline daily tasks in urban areas.

What Are Smart Cities?

Smart city initiatives focus on integrating technology and data-driven solutions to improve urban infrastructure, transportation, energy efficiency and public services. The implementation of these innovations extends beyond individual homes to encompass the entire urban landscape, affecting insurance in several ways.

Here are some key aspects where smart cities and insurance intersect:

1. Risk Management and Data Analytics: Smart cities use advanced data analytics and real-time monitoring to identify potential risks and vulnerabilities in the urban infrastructure. Insurance companies can collaborate with smart city authorities to access and analyze this data, enabling them to develop customized insurance solutions for specific risks faced by the city. For example, insurers can offer policies tailored to address issues such as infrastructure failures, cyber-attacks or natural disasters.

2. Infrastructure Coverage: Smart cities often involve extensive and complex infrastructure networks, including smart grids, intelligent transportation systems and IoT-enabled devices. These connected systems may be susceptible to various risks, such as cyber threats, equipment failures or physical damage. Insurance providers can offer coverage to protect against these risks and provide financial assistance in the event of disruptions or accidents.

See also: Smart Cities, Smart Choices for Insurers

3. Mobility and Transportation: Smart cities often promote sustainable and efficient transportation systems, such as electric vehicles, bike-sharing programs and autonomous vehicles. Insurance companies can adapt their policies to address the unique risks associated with these emerging technologies, such as accidents involving autonomous vehicles or specialized insurance products for shared mobility services.

4. Cybersecurity: With increased reliance on digital technologies and IoT devices, smart cities become potential targets for cyber-attacks. Insurance companies can offer cybersecurity insurance policies to protect against data breaches, ransomware attacks and other cyber incidents that could disrupt city operations and services.

5. Liability and Regulation: As smart city technologies evolve, new legal and liability issues may arise. Insurance providers can work with city planners and policymakers to understand these emerging risks and help establish appropriate insurance requirements and regulations. This collaboration ensures that both public and private stakeholders are adequately protected.

6. Environmental and Climate Risks: Smart cities often incorporate sustainability measures to address environmental challenges. Insurance companies can encourage eco-friendly initiatives by offering insurance incentives for environmentally responsible practices, such as energy-efficient buildings or green infrastructure.

7. Telematics and Usage-Based Insurance: In the context of smart transportation, insurers can leverage telematics data collected from connected vehicles to offer usage-based insurance policies. These policies adjust premiums based on actual driving behavior, promoting safer driving practices and potentially reducing the number of accidents.

New York City's Connected Cars Program is a smart city project that uses connected vehicle technology and IoT sensors (e.g., smart street lights and cameras) to gather real-time data about where drivers made sharp turns or braked abruptly because of traffic congestion and poor road conditions.

Leveraging data from connected devices, innovative data-collecting technologies and other smart city technologies can help officials and insurers across many lines of business better understand urban risks, allowing them to analyze trends and patterns effectively.

The McKinsey Global Institute found that smart technology can help improve quality-of-life indicators in cities by 10% to 30% -- numbers that translate into lives saved, reduced crime, shorter commutes, a lower health burden and carbon emissions averted.

More urban areas are adopting smart city approaches, many of which are aimed at reducing vehicle accidents, improving public health, enhancing the quality of life and predicting and preventing risks. Over 225 municipalities in Canada have expressed interest in exploring smart city benefits.

Overall, the integration of insurance solutions into smart cities is vital for creating a resilient and sustainable urban environment. Collaboration between insurance providers, city planners and technology experts can lead to innovative approaches that address the unique risks and challenges of the urban landscape in the digital age.

Insurance companies are grappling with a range of challenges that require them to adapt and innovate to stay competitive in the ever-evolving industry. From emerging technologies to changing consumer expectations, insurers are facing a complex landscape that demands their attention. In this article, we will explore the top 10 challenges insurance companies are currently encountering and discuss potential strategies to overcome them.

1. Embracing Digital Transformation

One of the primary challenges for insurance companies in 2023 is embracing digital transformation. The rapid advancement of technology has revolutionized the way businesses operate, and insurance is no exception. To stay relevant, insurers must adopt digital strategies that streamline processes, enhance customer experience and enable data-driven decision-making.

2. Cybersecurity Risks

The increased reliance on technology raises the risk of cyber threats. Insurance companies deal with vast amounts of sensitive customer data, making them attractive targets for hackers. Protecting this data from breaches and ensuring robust cybersecurity measures is crucial for maintaining customer trust and avoiding costly legal consequences.

3. Regulatory Compliance

Insurance is a heavily regulated industry, and compliance requirements continue to evolve. Keeping up with these regulations and ensuring adherence can be a daunting task for insurance companies. Failure to comply with regulatory standards can result in fines, reputational damage and loss of business. Companies need to invest in systems and processes that facilitate compliance and regularly update their practices to meet changing requirements.

4. Customer Expectations and Experience

In the digital age, customer expectations are constantly evolving. Insurance companies need to deliver seamless, personalized experiences across multiple touchpoints to meet these expectations. From intuitive online portals to efficient claims processes, insurers must prioritize customer-centricity to retain existing customers and attract new ones.

5. Insurtech Disruption

The rise of insurtech startups is disrupting the traditional insurance landscape. These technology-driven companies leverage innovative solutions such as artificial intelligence, machine learning and blockchain to provide enhanced insurance experiences. Established insurance companies must embrace collaboration and innovation to stay competitive in the face of insurtech disruption.

6. Data Management and Analytics

Insurance companies generate vast amounts of data from various sources. Effectively managing and analyzing this data is a critical challenge. By leveraging advanced analytics tools, insurers can gain valuable insights into customer behavior, identify emerging risks and optimize their underwriting and claims processes.

See also: Top 5 Challenges Facing Agents in 2023

7. Talent Acquisition and Retention

The insurance industry is experiencing a talent shortage, particularly in areas such as data analytics and digital marketing. To tackle this challenge, companies need to invest in attracting top talent and developing strategies to retain skilled employees. Offering competitive compensation packages, providing opportunities for professional growth and fostering a positive work culture can help insurance companies overcome the talent acquisition and retention hurdle.

8. Changing Risk Landscape

The risk landscape is constantly evolving, driven by factors such as climate change, geopolitical events and emerging technologies. Insurance companies must stay ahead of these risks to provide comprehensive coverage to their customers. This requires research, risk assessment and product innovation.

9. Legacy Systems and Processes

Many insurance companies still rely on legacy systems and outdated processes that hinder agility and efficiency. These systems are often complex, expensive to maintain and incompatible with modern technologies. Implementing modernized systems and streamlining processes can help insurers overcome operational bottlenecks and drive innovation.

10. Competition and Market Saturation

The insurance market is highly competitive, with numerous companies vying for market share. In such a crowded space, standing out and attracting customers can be challenging. Insurance companies need to differentiate themselves by offering unique value propositions, leveraging technology and providing exceptional customer service.

Conclusion

Embracing digital transformation, prioritizing cybersecurity, adapting to changing customer expectations and leveraging data analytics are just some of the key strategies that insurers must employ to thrive in this evolving landscape. By addressing these challenges head-on, insurance companies can position themselves as industry leaders and secure their long-term success.

KEY TAKEAWAY:

--The main culprits are fraud and lawsuits, rapid inflation for building costs and a surge in extreme weather.

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Across the country, homeowners are facing heavier financial burdens when it comes to securing the coverage they need to protect their property and belongings. Rising homeowners insurance rates are making budgets tighter for millions of American households. And in certain parts of the U.S., the situation is rapidly approaching crisis mode (if it’s not already there). 

In recent weeks, hundreds of thousands of policy holders in Florida and California were left scrambling to find new insurance coverage due to insurance providers pulling out of both states. Farmers Insurance is the most recent insurer to stop offering coverage in Florida, joining dozens of others. Meanwhile, State Farm decided to stop writing new homeowners policies in California — at least for the time being. Louisiana has lost at least 20 insurance companies in recent months due to either insolvency or withdrawal.

There are many factors contributing to the exodus of insurance companies. Here, we break down some of the top reasons consumers are facing rate increases and difficulty getting coverage.

Insurance fraud and lawsuits

One reason insurance rates are rising is the ever-increasing likeliihood of fraud. According to the Coalition Against Insurance Fraud, the insurance industry suffered more than $300 billion in losses as a result of fraud in 2022.

Insurance fraud in Florida, in particular, grew rampant at the hands of roof replacement scam artists over the past few years. In many cases, these roof replacement schemes escalated to lawsuits, which resulted in even higher losses. 

According to a study by the Insurance Information Institute (III), 79% of home insurance lawsuits in the U.S. originate in Florida, despite insurers in the state only receiving 9% of the country’s insurance claims. The combination, along with other factors, has pushed the insurance market in the Sunshine State to the verge of collapse. 

See also: Fundamental Shift in Life Insurance?

Inflation

Insurance providers have also felt the sting of inflation for materials and labor. According to a survey by the National Association of Home Builders, average construction costs for a typical single-family home in 2022 were around $153 per square foot, a surge from $114 in 2019.

Natural disasters

Of course, you can’t discuss the home insurance crisis without examining the impact of extreme weather and natural disasters. Wildfires, hurricanes, winter storms and other types of severe storms can devastate homeowners and result in enormous financial losses for insurance companies.

According to the National Centers for Environmental Information, the U.S. has experienced 90 billion-dollar, weather-related disasters between 2018 and 2022 — an average of 18 per year. By comparison, in the 2010s, there were an average of 13 events per year, and just seven per year from 2000 to 2009. 

The bottom line

No single event is sending the home insurance industry into crisis. Instead, a combination of factors are putting homeowners insurance providers under extreme financial pressure, especially in certain parts of the country. And as insurers become insolvent or opt to pull out of certain states, it’s ultimately the homeowners in those regions who pay the price.

The insurance industry has historically been highly data-led. As computing capability has expanded, the ability of data science to turn traditional insurance problems from descriptive, backward-looking views to highly accurate, predictive insights has advanced.

Today, insurers continue to gain deeper insights captured more quickly than previously possible. For example, there has been fast-growing interest in using machine learning to improve claims operations via informed call routing decisions, or the ability to spot emerging problems early on and trigger the engagement of human intervention for remediation. 

In the turbulent markets that the U.K. personal lines industry currently faces, data science can, when combined with experienced decision makers, deliver a compelling advantage to ride the "perfect storm" more effectively. 

Yet, although insurers are increasingly using data to generate value, firms have so far done this with varying degrees of success. At the executive and senior leadership level, there is concern that significant investment in data science teams -- and the technology infrastructure required to deploy these methods -- are not delivering the practical, pragmatic business change or value they would like or expect.

The grace and favor once afforded to executives around data science as an “R&D” activity has passed, and the expectation of clear value from the investment is now being demanded. Close observation of the market has revealed five of the biggest drivers of underperforming data science teams:

1, Trading off accuracy and value creation

Insurers face potentially conflicting challenges between how data scientists have been trained to work and the actual needs of the business. Where model accuracy and predictiveness might be the ultimate focus for data scientists, many insurance leaders are keen to see swift and actionable insights that can result in material change and measurable value. They are also -- within limits -- more than prepared to compromise on predictiveness. 

The trade-off between model predictiveness and value continues to be a well-socialized issue. How leadership balances both requirements is not an easy problem to solve, and the time required to allow this challenge to find its natural equilibrium is not always palatable – or indeed practical or desirable.  

2. A lack of technical challenge

This is a situation that occurs with leadership who have not used advanced analytics techniques in their earlier careers -- for example, those who may have cut their teeth on GLMs and do not understand these new methods as deeply. Therefore, their ability to challenge model performance or outputs effectively is reduced. This can manifest as an inability to identify and therefore steer the team away from pitfalls and, as a result, the data science function failing to deliver sufficient commercial value. It can also present as a reluctance or slowness to apply these methods, due to fear or lack of understanding, that may affect future commercial prospects. 

See also: Healthcare Data: The Art and the Science

3. Naivete

There is a certain level of naivete in the approaches taken by data science teams, which stems from a lack of understanding of the very specific, niche problems faced by insurers. Model instability, for example, is where data science techniques are able to create an inherent variability (more so than with historical methods), which when deployed in an insurance context can lead to unintended and detrimental outcomes. What date scientists choose to model is sometimes misguided, so it is imperative that insurance specialists and data scientists work together, sharing goals to achieve the best outcomes for their business.

4. Managing massive model real estate

For organizations that have great data, the opportunity to model is enticing, and with well-built models the value is unquestionable. However, models need maintenance and attention as neglect risks leading to poor insight and decision making. So, with a large model real estate, it is easy for skilled pricing resource to spend a disproportionate amount of time on being glorified handle turners, rather than spending the time in generating material insights from models to create genuine business change and value. 

5. Insufficient governance and control

Data science teams can lose sight of appropriate governance. It is critical to bring together data scientists and subject matter experts to design systems that offer greater visibility of what models are doing, with more transparent governance that is sufficiently understood by the wider business and external stakeholders. The excuse of data science methods being opaque and uninterpretable is no longer an option, with the best having good control over the impact of their models. 

The U.K. insurance market is seeing an explosion in the use of data science, with both winners and losers. Bad data science is often clever people doing clever things with data, but they all too often fail to filter through the organization to drive real change and generate no commercial value. This results in poor return on investment, but more importantly a weight around the ankles of data science teams that results in reduced productivity and attrition.

Insurers that are pulling ahead of the pack are the ones thinking about how they can create the structure and culture to empower data science teams to deliver value. They also have a strategy and clear vision around team structure, what to model, deployment and maintenance, as well as having the technical expertise to ensure the implementation is robust and real business value is unlocked from data science, targeted at solving meaningful problems. Those who are successful in navigating these challenges are seeing significant tangible returns.

Way back in 2014, Wired magazine co-founder Kevin Kelly wrote, "The business plans of the next 10,000 startups are easy to forecast: Take X and add AI." Boy, was he right.

That prediction was far bolder than it looks in retrospect. For the preceding nearly 60 years, an AI revolution had been much promised but was always just over the horizon. Even proponents acknowledged that there was "an AI winter." 

But Kelly saw a convergence of new forms of computing power, plus big data and better algorithms, and declared the winter over.

And here we are: A form of AI, best-known through its incarnation in ChatGPT, has captured the world's imagination, and not only every startup but just about every established company is figuring out how to fit generative AI into its business plans. 

But if there's one thing I've learned over my many years of following technology -- beyond that Kevin Kelly is a smart fellow -- it's that caution is in order whenever a new technology is supposed to take the world by storm. Events rarely play out as expected, and mistakes get made in the rush for the gold.

So, I thought I'd share thoughts based on an insightful column I recently read on the 10 biggest mistakes companies make when trying to implement AI. The column doesn't focus on ChatGPT and its rivals, which I know is the topic du jour, but the broad lessons could save a lot of us a bunch of time, effort and money.

The column, by Bernard Marr, which I recommend reading in its entirety, lists these 10 as the biggest stumbles with AI that he's seen in his extensive experience:

• Lack of clear objectives
• Failure to adopt a change management strategy
• Overestimating AI capabilities
• Not testing and validating AI systems
• Ignoring ethics and privacy concerns
• Inadequate talent acquisition and development
• Neglecting data strategy
• Inadequate budget and resource allocation
• Treating AI as a one-time project
• Not considering scalability

I'd highlight these four: 1) lack of clear objectives; 2) failure to adopt a change management strategy; 3) overestimating AI capabilities; and 4) treating AI as a one-time project. 

Lack of clear objectives

From what I've observed, the biggest issue is that every company -- certainly, every public company -- is being peppered with questions about what its AI strategy is. Not having an AI plan would be like not having a website in 2000 during the first internet boom or not having an app in the 2010s, after Apple made smartphones ubiquitous. So, every company has some sort of AI strategy -- at least, a major AI project. 

But AI is often a technology in search of a problem, and that rarely works, no matter what technology is involved. Companies need to start, as usual, by defining a business problem to be solved. Then, if appropriate, AI can be applied. Just deciding to sprinkle some AI on a business unit or process rarely accomplishes anything, and can be distracting.

For me, two of Marr's other "top 10 problems" -- lack of data strategy and not considering scalability -- fit under this umbrella. A clear AI plan for, say, auto insurance claims needs to start by looking at how AI can streamline the process. But the plan also needs to envision from the get-go how the data gathered fits into the overall corporate data strategy -- such as by being fed into the underwriting process or, perhaps, being shared with car makers so they can improve safety or lower repair costs. In addition, the AI plan needs to map out how the initial work can be scaled. Otherwise, the AI work is more show than substance.

Failure to adopt a change management strategy

Everybody likes change -- except for the change part. And AI, done right, produces major changes in how people work. So, any AI strategy of any scope needs to prepare for the retraining that will have to be done and for resistance to appear. That means those driving the change need to communicate, communicate and communicate, then communicate some more. 

Executives will also need to model the new behavior. Don't expect others to use ChatGPT, for instance, if you don't.

I remember when IBM was selling enough email software in the early 1990s that the CEO decreed that paper memos were out and emails were in. The idea made a lot of sense. In Silicon Valley, the approach is known as eating your own dog food. You get a sense of what your customers are experiencing. But IBM executives -- who mostly didn't know how to type -- had their secretaries type memos as usual, then simply put them in email form. Subordinates weren't fooled, and the mandated move to email fizzled.  

Overestimating AI's capabilities

How easy is it to fall victim to this problem? So easy that even Kevin Kelly got caught, to an extent, in that brilliant article from 2014. He opened the piece caught up in the glow that AI achieved when IBM's Watson beat Ken Jennings at Jeopardy! in 2011 and reported at face value IBM's plans to "send Watson to medical school." But Watson, in its initial incarnation, turned out to be a one-trick pony. It was great at the sort of natural language processing that a contestant needs to do to decipher the clues on Jeopardy! but never came close to deciphering medicine. Kelly also predicted that Google would become so good at AI that, "by 2024, Google's main product will not be search but AI."

AI can be marvelous stuff, but it's really just smart computing. Yes, it can beat Jennings at Jeopardy!, overcome Garry Kasparov at chess and perform all sorts of other marvels in structured environments. But it isn't a better soccer coach than I am -- and I don't even coach soccer. 

It's crucial to focus not just on what AI can do but on what it can't. AI isn't magic.

Treating AI as a one-time project

AI is a funny beast. It isn't really a technology, at least not in the sense that, say, telematics or blockchain is. Historically, AI has always been whatever you could imagine as possible but couldn't quite do yet. When computer scientists conquered whatever the problem was, their work became plain, old computing, and AI was defined as some new aspiration. 

When I first had people start bragging to me about the potential of AI, some 35 years ago, the sorts of things we take for granted weren't even in the realm of possibility. Siri? Are you kidding me? Google Translate? Yeah, right. 

Now, while there's plenty of work being done to keep improving Siri, Google Translate and other such tools, AI has moved on to figuring out how to estimate car damage from photos a driver sends, how to price risk for a life insurance policy without requiring a doctor's appointment and the taking of fluids, etc.

Basically, AI is a treadmill. Once you get on -- as everyone should -- you can't get off. It never stops moving.

Marr's other four points are certainly important -- not testing and validating AI systems; ignoring ethics and privacy concerns; inadequate talent acquisition and development; and inadequate budget and resource allocation -- but I think of those as downstream issues that can be addressed if the strategic umbrella is right.

I came across a great quote the other day in a book about how much Abraham Lincoln did as president to lay the foundation in the U.S. for the development of science. Lincoln wrote: "We always hear of the successes of life & experiment, but scarcely ever of the failures. Were the failures published to the world as well as the successes much brain work & pain work--as well as money & time would be saved."

As usual, I'm with Honest Abe. I recommend we learn as much as we can from the failures to date on AI projects, to clear the way for the many successes that are possible.

Cheers,

Paul