Emerging tech like generative AI poses new fraud risks for insurers. They must adapt with improved security, detection tools, and data management to stay ahead.

Content: Bad actors intending to commit fraud have always been innovators, finding new ways to defraud insurers and honest policyholders and often staying one step ahead of investigators. Advances in technology, like AI and generative AI, will bring new risks to the insurance industry as criminals search for weaknesses they can use to their advantage.

As technology shifts toward a more collaborative open system approach through the use of open AI programs and other generative AI applications, insurers will need to understand these risks and be proactive to prevent breaches and fraud attempts. A recent Aon report found AI will become a top-twenty risk in the next three years, highlighting the need for the industry to focus on the risks associated with it.

Whenever new technologies are introduced, bad actors search for ways to exploit them. Ring cameras have been hacked, with horrifying examples of hackers spying on people, making death threats, or scaring children through the cameras. A Jeep was hacked as an example of how software updates can be exploited, with the “carjackers” taking complete control of the vehicle as it was being driven. The various smart devices that now fill the homes of many people are often vulnerabilities, including smart TVs, lightbulbs, and thermostats.

The risks generative AI poses are dynamic and will continue changing alongside the technology, which means the industry must try to keep pace with bad actors.

New Risks, New Opportunities From Generative AI

While AI and generative AI use is still in the early stages, the insurance industry cannot ignore the emerging risks that accompany the opportunities. Some of the risks include:

• Data privacy and security concerns.
• Inherent bias built into generative AI applications.
• Ensuring compliance with legal and regulatory requirements.
• Potential over-reliance on AI and generative AI.
• Attacks by hackers on vulnerabilities within generative AI programs.
• Data poisoning when bad actors introduce bad information into AI databases.

FRISS recently released its 2024 Fraud Report examining global beliefs about fraud and actions taken to detect and prevent fraud in the insurance industry. Looking at emerging issues like fraud in AI and other technology, the survey examined how respondents found and prevented application and claims fraud and the tools they use to help fight fraud.

The majority of respondents (59.8%) would like to see their organizations implement automated fraud detection tools. Respondents believe implementing these automated tools combined with increased fraud awareness training and better collaboration between departments would help their organizations better fight fraud.

To fight these risks associated with generative AI programs, insurers can implement tools and strategies designed to detect, prevent, and control fraud.

Ways Insurers Can Help Manage Generative AI Fraud

Insurers will need to stay ahead of trends and changes in technology and use cases to effectively help manage fraud from generative AI technology. Knowing one key risk lies with data privacy means insurers can focus on improving their data security systems to help reduce some of the risks introduced by generative AI.

Another way to strategically fight against generative AI fraud is through the use of a fraud detection and prevention platform. An average of 28.18% of respondents to the FRISS survey said they had no platform currently in place to help detect and prevent fraud. This represents an opportunity area for those without a platform to consider an external or homegrown solution to supplement other tools they already deploy to help fight fraud.

33.82% of respondents worried that keeping up with modern fraud methods was one of their biggest organizational fraud challenges and 39.8% were concerned with data protection and privacy. But the biggest challenge was with data quality, with 61.98% of respondents expressing their concerns about the quality of internal data.

Insurers can focus on these challenges by improving their data security methods and tools. The quality of internal data has historically been a challenge for incumbent insurers as they have tried to analyze their data and draw conclusions from it. Modernizing to a digital platform to detect, manage, and prevent fraud at the application and claims level could be a way to shift to a more predict-and-prevent model when it comes to fraud. To learn more, read the full 2024 Fraud Report, available for download on the FRISS website.

Sponsored by ITL Partner: FRISS

As an insurer, if you’re looking to simplify your core processes and make them digital-ready, insurance management software can help, but, you’ll have to make several key choices, from the many features and functionalities to the implementation plan to training, onboarding and customer support experience and more. 

One of the most-often-overlooked aspects of the software is its deployment format. Selecting the right deployment model is crucial for optimizing operations, ensuring data security and maintaining resilience. In this blog, we talk about cloud, on-premise and hybrid deployment models, along with their pros and cons, to help insurers make a well-rounded decision.

See also: Moving From Legacy Systems to the Cloud

Cloud-Based Insurance Management Software

As the name suggests, cloud-based insurance management software stores, manages and distributes software applications, computing resources and databases over the cloud. Such a deployment model makes the insurance management software accessible to all authorized stakeholders regardless of their physical location. 

Benefits include:

• Cloud-based insurance management solutions are highly scalable and allow businesses to adjust resources, processing power, and other parameters as business needs change.
• Because the cloud-based insurance software company offering this service is responsible for end-to-end platform management, you don’t have to dedicate resources to maintenance, security updates, etc.
• They follow a pay-as-you-use model that can make them more affordable.
• They boast a solid infrastructure paired with disaster recovery capabilities that infuse reliability into insurance processes and workflows.

In contrast, cloud-based insurance industry software struggles with the following limitations:

• Not having control or ownership of the data and infrastructure can make insurers hesitant to try out cloud solutions due to data security concerns.
• Cloud-based solutions rely on a stable internet connection to operate. Any disruptions on this front can suspend or break down insurance processes.
• With insurance deeply ingrained in legacy systems, transferring all the data onto the cloud without losing any of it can be a challenge.
• Relying on a third-party cloud hosting service provider could result in the insurance business getting exposed to technical issues and downtime.

On-Premise Insurance Management Software

On-premise software is a deployment mode wherein the server, software applications and hardware infrastructure, are hosted locally within the organization. On a large scale, server closets, server rooms and server warehouses may be rented out. The software is installed on a single computing device, and the data is stored in a physical location. 

Some of its key advantages include:

• Absolute data control and security, as the insurance company is solely in charge of the data and exercises granular control over it.
• Ownership of data also allows them to adhere to stringent industry regulations more effectively and maintain compliance.
• Because all the software and data are hosted internally, there are fewer chances of disruptions or downtime attributable to external factors.
• Although there is a significant upfront cost, the best insurance software solution on-premise will have predictable and controlled expenses.

Limitations of on-premise insurance management systems include:

• Setting up the insurance software system can be expensive.
• Maintaining the insurance management software will add to the costs and resource-intensiveness.
• On-premise systems lack the scalability to adapt to changing workloads and business requirements.
• They restrict accessibility, as insurers cannot use on-premise solutions for remote teams.

See also: Why Cloud Platforms Are Critical

Hybrid Insurance Management Software

Hybrid insurance management systems combine the elements of on-premise and cloud-based solutions. Such a blend offers flexibility and customization, mixed with security and control. In such a setup, critical or sensitive data may be stored on-premise to ensure control and compliance. Business processes can be deployed on the cloud for greater scalability and accessibility.

This combination offers the following advantages:

• Hybrid insurance software systems are flexible, as insurance technology service providers allow you to choose what parts to store on-premise and what to deploy on the cloud.
• They are efficient in terms of costs. 
• They encapsulate robust disaster recovery strategies to ensure business continuity and reliability, even in the face of disruptions.
• You can customize a hybrid insurance software system to match the unique requirements of your insurance business.

Despite the many advantages, hybrid systems also possess the following limitations:

• Managing and adopting such an infrastructure can be complex, as it requires careful planning and implementation while maintaining consistency.
• Ideally, hybrid models are cost-efficient. However, it is easy to get wrapped up in different features, options and complexities that give rise to cost creep.
• Integrating hybrid insurance management systems can pose challenges while introducing friction or bottlenecks within processes.
• Managing a hybrid environment requires a diverse set of skills, as the IT team would have to be proficient in on-premise and cloud technologies.

Conclusion

There is no black-and-white answer to what’s better -- cloud vs on-premise vs hybrid. The choice eventually boils down to the insurance business’ specific needs, priorities, resources and capabilities. Moreover, the choice may also be dynamic, as what you choose may change with the evolution of the business objective, regulatory norms and long-term business sustainability. As such, insurers also need to know how to overcome common challenges when implementing insurance software.

Policyholders may look back on 2023 and remember the trend of premium increases. However, new technologies such as artificial intelligence (AI) are reducing costs for underwriters, which will translate to more competitive customer rates.

You can expect the remainder of 2024 to bring continued technological improvements powered by AI. Policyholders can insure more assets in less time, while carriers continue to reap the cost benefits of greater efficiencies. Of course, when it comes to emerging tech like AI, it's not all good news. New technologies can be used to cheat the system as well as benefit underwriters and policyholders.

When we take a closer look at AI applications in the insurance industry, here are five trends we anticipate seeing in the months ahead:

Insurance On-Demand

Are you renting out your vacation home, trailer or snowmobile? Consumers can monetize their assets through rental companies and online brokers. If you plan to rent your assets, particularly for short-term use, insurance is necessary and can't be left to the renter's discretion

You must be sure that your assets are adequately covered and that you are protected from personal liability. Thankfully, with on-demand policies, getting the necessary coverage becomes simple, whether you are a renter who requires a policy or an owner looking to guard against liability. With the help of AI risk assessment, your policy is ready in minutes and can be customized to your specific needs.

See also: 5 Ways Generative AI Will Transform Claims

Embedded Insurance Everywhere

Sure, you may know how to add insurance for your purchases or services, but why not sign up at the point of sale and make it all a simple transaction? After all, who wants to wait and go through yet another transaction to add insurance? This process isn't a new strategy. For example, travel insurance is often offered with a plane ticket.

What's new is that embedded insurance is being extended to other purchases, whether it's concert tickets, a new e-bike, ride-sharing or online banking. Embedded insurance reduces risk for consumers and avoids their having to shop for coverage. Just sign on the dotted line to include insurance with your purchase, and, who knows, the merchants may give you a special deal you can't find elsewhere. Embedded insurance presents an opportunity for insurers to capture new customers before they have a chance to shop for coverage.

More Benefits From Generative AI

Would you buy insurance from an AI bot? Perhaps you already have. AI-powered automation cannot replace the human touch entirely, but sometimes underwriting can be very transactional, and generative AI can make that process much faster and smoother.

There are several ways generative AI will benefit underwriters and policyholders:

1. Personalized insurance - Generative AI can analyze vast amounts of data, including local news and statistics, social media, IoT devices and historical claims, which makes it easier to create custom risk profiles for personalized policies at more accurate prices.
2. Underwriting - The same generative AI technology can be used for underwriting, making it easier to analyze nontraditional data points.
3. Claims processing - While some larger, more complex claims require careful human scrutiny, others are just business as usual, and who doesn't like business to be faster and more efficient?
4. Customer service - More companies are using chatbots and virtual assistants powered by generative AI to provide information on policies, claim, and more.
5. Marketing and sales - Generative AI also plays an increasingly large role in marketing content and sales strategies.

See also: Balancing AI and the Future of Insurance

More Platforms to Protect From Generative AI Fraud

AI isn't only about improving speed and efficiency. Fraudsters see generative AI as a new tool to falsify and exaggerate real claims and defraud insurers. But similar technology being used by fraudsters can be applied to protect insurance companies and their customers.

Here are just some of the ways that AI is being used to turn the tables on cyber criminals:

1. Anomaly detection - AI models trained to identify normal transaction patterns and claims data can identify outliers and anomalies that could point to fraud, flagging them for investigation.
2. Behavior analysis - Using AI to analyze behavior patterns associated with fraud helps identify potential fraudsters with greater accuracy.
3. Synthetic data - Synthesizing data that mimics fraudulent behavior can help train fraud detection models to make them more accurate and effective.
4. Predictive modeling - AI can analyze historical data such as trends, patterns and past claims to predict fraudulent activities.
5. Document and image analysis - AI is an ideal tool for detecting phony images and forgeries, detecting alterations and inconsistencies. For example, generative AI can compare images submitted for claims with similar images or analyze the image to detect digital alterations.
6. Text analysis and NLP - Using natural language processing (NLP), generative AI can provide investigators with fraud indicators and suggest areas that need further investigation, making the review process more efficient.
7. Continuous learning - As the fraudsters become more sophisticated, so do the fraud detection tools. Generative AI learns and adapts as it encounters new forms of fraud, adjusting fraud detection models accordingly.

Demand for More Cyber Insurance

Now that the bad guys are getting smarter about using AI, there are more cyber threats to worry about. It's time to batten down the hatches because, in addition to fraudulent claims, hackers are using AI to access critical systems, release malware and launch more elaborate phishing schemes. While steps can be taken to protect and guard against these threats, ultimately, insurance is the backstop to protect against losses. As the landscape for cyber-threats broadens, it's only natural for companies to cast a wider net to protect their businesses and reputations.

These are just some of the trends AI will shape in the insurance industry. Companies that embrace AI-based insurance will be able to develop innovative products for policyholders. Underwriters can also use AI to improve efficiencies and reduce costs. At the same time, generative AI can pose new threats to insurance underwriters.

Those insurance companies positioning themselves to thrive are finding new ways to apply AI to improve operations, develop innovative products and protect themselves from AI-generated fraud and cyber threats.

There were unspeakable scandals during the COVID-19 outbreak among certain nursing homes and veteran hospitals in New Jersey. 9,000 patients died in nursing homes, and 2,000 died in veterans hospitals during the pandemic. Two facilities were suspended from the Medicaid program and forced to close or sell to new owners, and the state is continuing to crack down.

Just recently, a long-term care facility was found to have serious health and safety violations that led to the death of seven residents from COVID-19. An additional 66 residents and staff members were infected. A patient died after waiting for days to get the antiviral medication prescribed for treating COVID-19. The state halted admissions in November until a new owner could take over.

In New York, a recent state health inspection gave a long-term care (LTC) facility one star, far below average, for not preventing the spread of COVID-19 and for dangerous falls and broken bones. 

A long-term care ombudsman in New Jersey recently stated, “There should be no question about the health and safety of long-term care residents.” I could not agree more.

Aging baby boomers like me will drive the long-term care crisis. Currently, there are 14 million people in the U.S. receiving some form of long-term care. That number is expected to double by 2050. This growing number of people who will need support and services will result in a major burden on their children and families. A substantial number, roughly 25%, will need “severe care.”

Medicare and private health insurances generally do not pay for long-term care, other than for recovering from acute health problems. The Medicaid system is only available to people who have exhausted their financial resources to under $2,000. There is no national solution on the horizon, and for now, it is up to individuals and their families to solve this evolving crisis.

See also: The Staffing Crisis in Insurance

The crisis will only escalate, as 70% of people over 65 will need some form of long-term care down the road, such as assisted living or nursing homes. Research shows that 45% of baby boomers have no retirement savings, and only 25% have more than $100,000 in savings. The average cost of a long-term care facility is $100,000 per year, which will wipe out most retirement plans.

The impact of the long-term care crisis is parallel to that of the caregiver crisis, which is staggering. As a caregiver for my mom, dad and now my older brother, I had to learn on my own how to deal with power of attorney, healthcare proxy, Medicaid, Medicare, their finances, Social Security, pensions, living wills, taxes, selling a home, healthcare directives, DNR (Do Not Resuscitate) orders, doctor appointments, visiting nurses, prescription medications, surgery and slips and falls, just to name a few.  I also had to become an expert in congestive heart failure, diabetes, transitional delirium, dementia and depression. 

There is no way to prepare oneself to become a caregiver. It will be the toughest job you have ever had, and you have no choice. Your job and life become secondary, or just a welcomed diversion. One night, I got a call that both my mom and dad were on their way to the emergency room in two different ambulances at the same time. The only good news was that they were going to the same hospital.

Two years ago, I got a call that my brother was in intensive care for a week before they found my healthcare proxy in his medical records. He had a blockage in his colon leading to both sepsis and pneumonia. This caused development of encephalopathy, referring to overall brain dysfunction, which was hallmarked by his transitional delirium. He was hallucinating and seeing things like gorillas climbing up the tree outside his window.   

After several months in intensive care, he was discharged to a nursing home. I had to fight tooth and nail to get him on Medicaid so that he could be treated in a long-term care facility. The nursing home was a nightmare; he was found lying on the floor unattended and ended up back in the emergency room with internal bleeding. He almost died.

From that minute onward, I dropped everything and saw to it that he received the finest medical care available to him. The situation became even further complicated when I discovered that $10,000 was stolen from his bank account by a roommate while he was in the hospital, leading me to need to file criminal charges. The entire situation epitomized a living nightmare.

See also: The Future of Caregiving

The moral of the story is that we are facing a crisis in both long-term care access and caregiver responsibilities. Baby boomers and their families need to plan now for the inevitable cost of long-term care. The Centers for Medicaid and Medicare has created a five-star rating system for health inspections, staffing and quality measures. That is a good place to start.

The need for long-term care insurance is staggering, yet long-term care coverage has been declining for years. Major insurers have pulled out of the market due to complete lack of understanding of the need, as well as associated costs. Research shows that only 3% to 4% of Americans over 50 have a long-term care policy. It is estimated that 70% of people 65 and over will need critical services in the near future.

The current political environment shows little, if any, hope for long-term care solutions. It is up to individuals and their families to speak to qualified and licensed professionals who are experts in their field to help determine their options and needs.

In the insurance industry, digital fluency has emerged as a critical element in transforming the relationship between insurance carriers and policyholders. Until recently, the primary focus of insurtech was on digitizing the initial stages of a policyholder's journey, such as policy shopping, pricing, binding and management. However, a noticeable disconnect arises when policyholders experience inconsistencies or challenges during any part of this process. The most significant divide becomes apparent when policyholders file claims, as the digital experience during this crucial touchpoint often falls short. 

Bridging the gap between insurance carriers and policyholders is essential, and there are numerous benefits to elevating claims services through digital fluency. Read on as we explore the digital disconnect between insurance carriers and policyholders, the challenges faced by carriers in embracing digital integration and how insurtech solutions aim to improve the process.

The Digital Disconnect in Claims Processing

The claims process is a critical touchpoint in the customer journey, and it's here that the digital experience often falls short. From the first notification of loss (FNOL) to the release of recoverable depreciation, every interaction should offer a consistent and cohesive user experience. This includes a seamless experience across web portals and apps.

However, the adoption of digital tools in the claims process remains relatively low, leaving room for improvement. A significant challenge is the lack of digital fluency among policyholders, who may find the process daunting due to complex interfaces, unclear instructions or the need to switch between digital and non-digital platforms during the claims journey. These various friction points make the process more challenging, which creates unpleasant experiences for policyholders when they anticipate efficiency. 

A recent McKinsey article emphasizes this digital disconnect in the insurance industry, highlighting the fact that more than 30% of policyholders are not satisfied with their insurer’s available digital channels. Moreover, only 20% of policyholders consider digital channels their top choice for interacting with insurers. This data underscores the need for bridging the digital fluency gap in the insurance industry.

See also: 3 Ways to Maximize Digital Transformation Projects

Challenges Insurers Face With Digital Integration

While the benefits of digital integration in insurance are substantial, carriers face numerous challenges. Legacy systems can hinder the smooth transition to a digital-centric approach, slowing the adoption of modern tools and creating compatibility issues between old and new processes. Insurance carriers may face difficulties in consolidating data, automating processes and providing real-time access to information due to the limitations of legacy technology. Working through multiple or legacy systems can be a complex and costly endeavor, as moving from these systems to modern, digital solutions often requires significant resources.

In addition, encouraging both employees and policyholders to embrace digital solutions can be a significant challenge. Employees, particularly those accustomed to traditional processes, might resist change. They may fear job displacement, feel overwhelmed by the need to learn new tools or simply hesitate to leave their comfort zones. Overcoming this resistance requires robust change management strategies, training programs and clear communication about the benefits of digital integration.

On the policyholder side, not all individuals are digitally fluent or comfortable with online interactions. Some policyholders may prefer traditional communication channels, such as phone calls or in-person interactions, and encouraging them to use digital tools for tasks like filing claims or checking policy details can be a challenge. It's essential for insurance companies to offer user-friendly, intuitive interfaces and provide excellent customer support for those who need assistance.

Benefits of Elevating Claims Services Through Digital Fluency

Oftentimes, the digital disconnect between insurance providers and policyholders is rooted in mistrust, leaving policyholders feeling isolated and uncertain about the claims process. However, digitization provides a unique opportunity to ground the policyholder in the process and streamline effective communication. When communication is robust, and expectations are managed clearly, consumer experiences improve, trust is rebuilt within the industry and brand loyalty is magnified.

Elevating claims services through digital fluency offers numerous benefits for both insurance carriers and policyholders:

• Increased Employee Morale: Streamlined digital processes can reduce the administrative burden on employees, leading to increased job satisfaction and better morale.
• Improved Customer Experience and Retention: A seamless and user-friendly claims experience can lead to higher customer satisfaction and, in turn, better customer retention rates.
• Adaptability: Companies that are digitally fluent are more agile in adapting to evolving industry demands and regulatory guidelines, ensuring compliance and competitiveness.

See also: The Next Phase of Digital Transformation

Tips for Prioritizing Digital Integration

To bridge the digital disconnect between insurance carriers and policyholders, insurers should prioritize digital integration. To do this effectively, carriers would need to:

• Set Clear Objectives: Understand the organization's goals with digital transformation and how it will affect both employees and consumers.
• Align With Consumer Needs: Ensure that digital implementations are aligned with the needs and preferences of the consumer base.
• Prioritize Road Maps: Establish a clear road map for digital integration, prioritizing essential areas such as the claims process.
• Communicate the "Why": Share the rationale for digital transformation with employees to garner their support and facilitate its evolution in the organization.

Insurtech solution providers play a crucial role in improving the digital fluency of the insurance industry. They offer comprehensive solutions that encompass the entire claims process, from FNOL to intelligent triage, efficient process solutions and automated reporting and claim conclusion. By providing a streamlined, end-to-end digital process, insurtech solutions help overcome the pitfall of fragmented processes or niche solutions that add to the friction points within the claims handling process.

Digital fluency is essential for bridging the gap between insurance carriers and policyholders, particularly in the often-neglected claims process. By prioritizing digital integration, addressing challenges and engaging insurtech solutions, the insurance industry can offer a more consistent and satisfying digital experience to its policyholders. In doing so, it can rebuild trust, enhance brand loyalty and adapt to the evolving landscape of the insurance world.

The onset of the pandemic brought in a new era for homeownership, with a noticeable surge in Generation Z buyers – i.e., those born between 1997 and 2012. Studies show that the homeownership rate among 25-year-olds today surpasses that of their parents, as 30% owned homes in 2022, compared with the 27% homeownership rate among Generation X individuals when they were the same age. 

As Gen Z continues to seek homeownership opportunities, the focus on property/casualty insurance becomes increasingly crucial to ensure comprehensive protection for their valuable assets and safeguard against potential risks and unforeseen events. Nearly 40% of Gen Z does not have homeowners’ or renters’ insurance, highlighting the need for innovative and custom solutions tailored to this group. Given the surge in Gen Z homeownership, it is important to understand the profound impact of Gen Z's digital-first approach, preference for personalization, sustainability focus, demand for transparency and emphasis on value and education throughout the insurance industry.

Gen Z's Influence on Insurance Offerings

It’s no secret that technology is part of everything the Gen Z demographic does. Their preference for fast and seamless online experiences influences the insurance landscape, prompting insurers to invest in digital platforms for policy management, claims processing and customer support. While more than seven out of 10 Gen Z consumers expect companies to deliver personalized interactions, the demand for personalized services drives insurers to use data analytics to tailor insurance solutions to individual preferences and risks.

In addition, Gen Z’s focus on environmentally friendly solutions has prompted insurers to introduce sustainable products, such as discounts for eco-friendly homes and vehicles. Their lifestyle preferences are driving demand for non-traditional and cost-effective insurance options such as on-demand or usage-based coverage for specific events or periods. This cost-conscious nature is pushing insurers to focus on transparent communication about policy terms, benefits and competitive pricing.

See also: Strategic Guide to Unlocking 'Gen Zalpha'

The Case for Customization and Transparency in Insurance Solutions

Recognizing the importance of customization in insurance solutions is paramount, especially when catering to the diverse preferences and lifestyles of Gen Z homeowners. Customization ensures effective coverage for emerging risks linked to gig economy work, digital assets or eco-friendly living. It enables more accurate risk assessment and pricing, ensuring policyholders are neither underinsured nor over-insured. Additionally, given Gen Z's technological savviness, insurers have an opportunity to innovate and integrate advanced technologies like AI, IoT and blockchain into their offerings. This generation is more likely to embrace and benefit from digital insurance platforms and tools, providing a promising avenue for the industry to continue evolving and innovating in the insurtech space.

The Gen Z homebuying boom underscores the need for adaptable insurance products. Gen Z's different risk factors, such as environmental concerns or work-from-home implications, emphasize the need for customization. Tech-oriented solutions like smart home discounts or coverage for high-tech appliances resonate with this connected generation. Customized policies can adapt to evolving home ownership dynamics, offering coverage for home offices or green home improvements. Transparency, a value held high by Gen Z, is also enhanced through customization. It provides a clearer understanding of what is covered and what isn't, leading to better-informed decisions. 

Insurers carrying out transparent practices in policy details, pricing and claims processes cultivate robust, trusting relationships. Beyond building trust, transparency is instrumental in enhancing customer engagement and reducing confusion about policy terms and claims processes. Transparency paves the way for a smoother customer journey, fewer disputes and improved customer service experiences. The straightforwardness it brings to customer support boosts the overall experience and effectiveness of the service provided. A transparent relationship encourages honest and constructive feedback, which is invaluable for continuous improvement.

Challenges and Considerations for Tailoring Solutions for Gen Z

As Gen Z homeowners weigh insurance options, it is important to be aware of the challenges and considerations insurers are facing when tailoring solutions to this group. Three significant impacts and challenges arise with Gen Z policyholders: 

• Technological Expectations: Gen Z has high expectations for technology integration in all aspects of their lives. Insurance companies must offer advanced digital interfaces, mobile apps and AI-driven services. Keeping up with these technological demands can be challenging and resource-intensive.
• Data Privacy Concerns: With the use of big data and analytics to customize insurance solutions, there's an increased focus on data privacy and security. Gen Z is particularly conscious about how their personal data is used and protected.
• Educational Gap: Despite being highly informed, Gen Z can misunderstand insurance products and their benefits. Insurers need to invest in educational and engagement strategies to bridge this gap.

​​​​​​​See also: Rethinking Insurance With a Gen Z/Millennial Mindset

Moreover, carriers should be cognizant of changing risk profiles, shifts in marketing and communication preferences, the need for product flexibility and scale, economic factors and the importance of building long-term relationships. 

To stay agile in a rapidly changing landscape, insurers should leverage advancements like data analytics and AI to gain insights into Gen Z's preferences and behaviors, aiding in the development of customized insurance solutions. Modernizing IT infrastructure is crucial to providing digital-first services that align with Gen Z's tech-savvy expectations. Moreover, fostering a culture of innovation is critical for staying ahead of the curve. Creating innovation labs, collaborating with technology startups and engaging with Gen Z through surveys and social media to guide product and service development are all beneficial when developing solutions. Strategic partnerships with insurtech companies can help fill gaps and drive success in transparency and digital initiatives.

Insurtech solutions can significantly enhance an insurance provider’s capabilities, providing seamless digital platforms that integrate into an insurer’s current offerings and cater to Gen Z policyholders. For the claims process, in particular, advanced insurtech solutions are designed to promote transparency for both insurance professionals and consumers, addressing the educational gap and improving comprehension from the initiation of a claim to its resolution. Features like real-time updates and expectation management can keep policyholders well-informed and in control throughout every stage of the claims process. Insurtech platforms also place a high priority on data security, incorporating robust security protocols to safeguard personally identifiable information. Moreover, insurtech solutions cater to policyholders' preferences through various support channels, including phone, email and live chat.

The Gen Z homebuying boom necessitates a paradigm shift in insurance solutions, with customization emerging as a key driver for success. Insurers must adapt, innovate and prioritize transparency to meet the diverse needs and preferences of this influential demographic. This strategic approach aligns with Gen Z’s expectations for individualized services while providing a competitive edge over traditional one-size-fits-all offerings, opening up the potential for increased market penetration. As the industry evolves to cater to the evolving demands of Gen Z, those embracing these changes stand poised for sustained relevance and success in the dynamic insurance landscape

When I started executive and leadership coaching in the 1990s, as an add-on to my leadership workshop training business, it was mostly something new. Clients asked for 1:1 time to discuss what they learned in the workshop and how to best apply it in their situation. Now coaching is a multibillion-dollar global industry, with thousands of coaches working with as many leaders, from brand new supervisors to the most experienced executives.

For all the investments made, we have found there are some critical, basic strategies to follow to ensure you get the maximum ROI from a coaching engagement. 

Who Should Get Coaching?

One of the most important decisions you make, before you even find a coach and launch a coaching engagement, is deciding who should even have a coach.

Providing a coach to the right person, at the right time for the right reasons will go a long way in making sure you realize the often profound benefits from leadership coaching.

The success of any coaching engagement depends mostly on sufficient internal motivation to improve, so the person being coached will invest fully in the process. They must want the outcome. Coaching will almost never lead to any substantial change or real improvement if forced on the leader. Counseling from supervisors or HR experts can be used for those times when a coach is not appropriate.

Your manager can provide access to a coach, and is key in supporting change, but -- in the end -- the person must see benefits for them, not just for the team or organization. 

Motivations vary:

• Foundational Coaching – the person is new to management or leadership and needs the basics of moving from individual team member to leader.
• Transitional – the person is now leading an innovation effort, growth initiative or other large-scale change.
• Executive/Middle Manager – the leader is now taking on corporate or organizational/global scale responsibilities, or managing other managers.
• Senior Executive – the leader is taking on a C-suite role leading the company or key division or function.
• Stakeholder Centered Coaching – The leader has received input from stakeholders on a need to change, which the leader would like to address.

See also: Insurance Needs More Women in Leadership

When Is Coaching Most Effective?

In the process of coaching, the individual, with the support of their coach, will need to set specific goals for gaining and applying new skills and capabilities. They will need “real life cases” where they can try out new skills and ways of thinking. If a person is not facing any growing scope of work, or new role, or new goals or challenges, coaching will be like talking about skiing but never going to the slopes! 

21st century coaching is expected to be more than academic learning but should be immediately applicable for better impact on business results. 

Coaching can be very significant if provided at the right time:

• Promotion Preparation – This leader has recently not been selected for a role they wanted. Coaching can be used to prepare the leader and improve their internal brand, so they are both qualified for the next opportunity, and have good relationships with the decision-makers.
• After Promotion – Coaching can be provided as the leader takes on a new or expanded role, so the leader gets off to the right start and uses their first 100 days as wisely as possible.
• Closing the Gaps – This can be used for a very effective leader with a good track record who has a few areas that are holding them back and that will keep them stuck in their current role if those gaps are not closed.
• Reinvention and Transformation – Sometimes in a rich and successful career one must take on roles that are not within the leader’s current knowledge or capability -- maybe leading a new region of the world, or a new function within the company that the leader is not accustomed to or expert at. Coaching will help apply their strengths to maximum benefit in the new situation.

Why Do Coaching?

Ultimately, the purpose of coaching is to accelerate the self-development of the leader, so they can achieve new outcomes for themselves, their teams and their organizations.

This vision is reached more quickly with a coach, with more flow and success for the leader – in whatever ways are most important to them. Most coaching is not in any way corrective or a means to solve a performance problem or issue. Sometimes a coach can be helpful in those situations, but most coaching is to provide support to great leaders who have yet to fulfill their full potential. Most coaching is not focused exclusively on financial goals, though increased compensation due to higher performance may be part of the outcome, depending on the role.

We sometimes call the people being coaching “HiPos,” which is short for “high potentials,” who are offered coaching because it will help them grow and be even better, sooner. They are individuals management has decided are worth the investment of a coach, with the expectation this will pay off for the company in the form of higher performance and new achievements.

See also: The Evolution of Leadership Intelligence

How We Do Coaching Now

In the new 2024 world of coaching, a few key processes must be in place. In 30 years, I have learned we do not need very much oversight or bureaucracy, but just the right amount of process discipline can go a long way.

The problem is it can be hard to make sure the coaching process is working and is worth the cost. How do we know what is going on in the coaching sessions if they are confidential? How can we trust the time is productive and helping the leader and the business? Even the best-meaning coach and the leader can easily get distracted by the topic of the moment, having nice conversations but in the end not making a sustainable impact on the leader’s performance.

For each coaching engagement, the key initial deliverable must be some form of written goals for the engagement, which should include:

1. what skills will we work on,
2. what business impact do we expect to make using these skills,
3. how will we measure ROI (what indicators will we track) and
4. which stakeholders do we plan to check with to evaluate results?

Initially, the leader and their coach will draft the goals, based on all the input they have, including employee surveys, 360 degree feedback, performance appraisals, assessment tools and any other input they have received. 

Once that draft is complete, the leader, the coach and the leader’s manager should meet to agree on those goals. Are they best for the leader? Will they affect the team? How can the manager support the leader to achieve these goals? How will we know if it’s working?

This approach will ensure from the start that the coaching investment has a payoff in the end and will make later evaluation easy.  

Later, the goals sheet will also support celebration of accomplishments in a closing meeting, again with the leader, the coach and the leader’s manager, when we discuss the changes the leader has made and the positive impact they have had.

Give the Gift, but Give Wisely

Getting a coach can be the biggest gift you can give yourself or an employee. The right coach can support true transformations – I know it can be done. 

Do not hesitate to use coaching, but be sure you follow the key practices of who, when, why and how, to be sure you get it right.

A major challenge in all sectors is resolving the intricacies involved in consolidating independent data clusters into a modernized, unified architecture. 

Imagine being able to access data all in one place, any time and to have the power to fulfill endless business needs. This should become the goal in the age of digital transformation. The rise of cloud-based technology, big data and machine learning tools has made this increasingly possible. 

Integrated data platforms can open a plethora of opportunities for intelligence, innovation and modernization. With data easily accessible in real time, businesses can make more informed decisions, predict customer behavior, manage risks, optimize operations and eventually realize their business vision. 

Typically, with the right data management solutions and data analysis tools, businesses can access and analyze all their data from a single platform. This not only improves efficiency and accelerates the decision-making process but enables businesses to unlock new insights, identify growth opportunities and drive innovation to stay ahead of competitors. 

The primary aim is to provide a unified and comprehensive view of data to all groups of people within a company such as managers, analysts, marketing teams and business decision makers. 

Let's look at the key factors that are essential in planning for building an enterprise platform involving the process of defining, integrating, storing, and managing all data related to business operations and transactions. 

Define Objectives 

Clearly define the goals of the data platform. It could be to improve decision making, streamline business operations, increase customer satisfaction, manage applications or predict trends. 

Identify Data Sources and Create a Data Dictionary 

Record all places from where the company collects data. It could be customer databases, data documents, web analytics, revenue tracking, social media activity, competitors' performance data, market data and more. Designate each data set by its function in the overall business process. This data dictionary will serve as a foundation to build the data model and lay out the logic for data relationships. 

See also: 6 Steps for Cultivating a Data Culture

Prepare a Data Model 

Design dataset relationships to view communication connections between different data sets. The data model will outline the integrated view of the organization's data. Data modeling provides well- defined technique to organize data to meet business goals, effectively becoming a road map to strategize and guide an organization's IT infrastructure. 

Data modeling tools are used to design and develop complex database structures and ensure that data objects, relationships and their rules are accurately represented. Here are some commonly used data modeling tools: 

• ER/Studio: Embarcadero’s ER/Studio provides robust logical and physical modeling and allows for forward and reverse engineering. 
• Sparx Systems Enterprise Architect: This tool provides well-rounded support for data modeling, including UML, BPMN, SysML and many other designs. 
• IBM InfoSphere Data Architect: IBM's tool allows for visual design, management and analysis of relational database schemas, with a specific focus on IBM DB2.
• Oracle SQL Developer Data Modeler: This is a free tool that provides forward and reverse engineering capabilities for Oracle SQL and is also integrated with Oracle's SQL Developer. 
• Toad Data Modeler: It supports a wide range of databases, has a user-friendly interface and provides functionalities for both logical and physical modeling. 
• PowerDesigner (SAP): It supports model-driven architecture for data modeling and can handle metadata management. 
• Microsoft Visio: While not strictly a database modeling tool, Visio includes templates and shapes for building entity-relationship diagrams. 
• CA ERwin Data Modeler: ERwin enables users to visualize complex database structures, build and maintain databases and work collaboratively on data models. 
• MySQL Workbench: This free tool from MySQL includes database design and modeling along with other development features. 
• Navicat Data Modeler: Navicat supports multiple databases, offers functionalities for both logical and physical modeling and provides reverse/forward engineering capabilities. 

Build a Data Integration Framework 

Develop a process to automatically gather and ingest data from multiple sources and formats into one consolidated and cohesive view. This can be achieved through ETL (extract, transform and load) processes. There are many ways to build data ingestion frameworks based on business model architecture. However, data ingestion itself can be done in two ways, batch or streaming. 

Data ingestion tools help in the process of importing, transferring, loading and processing data for later use or storage in a database. Here are some popular data ingestion tools: 

• Apache NiFi: It's an open-source tool for automating and managing data flows between different systems. It allows data routing, transformation, and system mediation logic. 
• Fluentd: This is an open-source data collector that unifies data collection and consumption. 
• Logstash: This is part of the ELK Stack (Elasticsearch, Logstash, Kibana), which is primarily used for log and event data. It provides real-time insights from the ingested data. 
• AWS Kinesis: This is a cloud-based service from Amazon that's used to process large streams of data in real time.
• Google Pub/Sub: This is a scalable event ingestion service provided by Google that allows real-time analytics from ingested events. 
• Apache Flume: This is a distributed, reliable and highly available service for efficiently collecting, aggregating and moving large amounts of log data to a centralized data store. 
• StreamSets: It's a platform designed for building, executing, operating and protecting enterprise data flow pipelines. 
• Sqoop: This is a tool designed to transfer data between Hadoop and relational databases.
• Informatica PowerCenter: It's well known for its ETL capabilities. PowerCenter also supports data ingestion tasks. 

These tools play a crucial role in managing the challenges of volume, velocity, variety and veracity of big data. The choice of the tool depends on the specific requirement and the technology stack implemented in your organization.

Decide How and Where the Data Will Be Stored 

The right answer could be on-premises, cloud or a hybrid solution depending on company size, budget and business needs. A variety of digital technologies can be used depending on data usage from analytical systems to transactional processing. The data storage mechanism used should be robust, scalable, cost-effective, efficient and reliable, with fault tolerance. The right storage solution should offer capabilities to enable automation and digitalization, apply machine learning models and be dev-ops compliant. 

These are some methods pertaining to data storage platforms: 

• Databases: This is perhaps one of the most common forms of data storage. Databases like MySQL, Oracle, PostgreSQL and SQL Server help in storing structured data. 
• Data Warehouses: Data warehouses such as BigQuery, Snowflake, Amazon Redshift and Teradata help store data drawn from transactional systems, relational databases and other sources. 
• Data Lakes: They store both structured and unstructured data at any scale. Examples include Amazon S3, Microsoft Azure Data Lake Storage and Google Cloud Storage. 
• Cloud Storage: Services like Google Cloud Storage, Amazon S3 and Microsoft Azure Storage provide a scalable environment to store data. 
• Distributed Storage: Distributed storage systems like Apache Hadoop, Cassandra and MongoDB store data across multiple nodes to ensure redundancy and speed and to lower the risk of data loss. 
• On-Premises Storage: This includes traditional data storage like hard drives and SSDs. 
• Hybrid Storage: It uses a combination of on-premises and cloud-based storage to create a balanced infrastructure that optimizes cost, speed, security and availability. 
• Object Storage: It offers infinite scalability at a lower cost. Examples include Amazon S3, Google Object Storage Cloud Storage and Azure Blob Storage. 

Each of these platforms is suited to different types of data and use cases; the right platform for an enterprise depends on their specific needs. 

See also: Data-Driven Transformation

Design Data Transformation Rules 

Data transformation tools are software or services that convert, clean and standardize data into a format that can be used for data analytics and reporting. The different types of data transformation involve data cleansing, Ddata deduplication, application of business rules, master data management, validation and data quality checks. 

Here are a few commonly used data transformation tools: 

• ETL Tools (Extract, Transform, Load): These tools, like Informatica PowerCenter, Microsoft SQL Server Integration Services (SSIS) and IBM InfoSphere, help extract data from various sources, transform the data into an appropriate format or structure and load it into a final target, commonly a data warehouse.
• ELT Tools (Extract, Load, Transform): These are similar to ETL, but the transformation is done after loading data into the target system. Examples include Google's BigQuery, Amazon's Redshift and Snowflake. 
• Data Cleaning Tools: OpenRefine, Google Cloud's Dataprep and Trifacta Wrangler are examples of this type of tool that help clean up messy data, finding inconsistencies and making the data more usable. 
• Data Pipeline Tools: Tools like Apache Beam, Fivetran, Stitch and Airflow allow you to build data pipelines that can extract, transform and load data in real-time or batch modes. 
• Scripting Languages: Python, especially with pandas library, and R are often used for data transformation because of their flexibility and the extensive amount of libraries they offer for data manipulation. 

Selection of the appropriate tool depends on the specific requirements, like the type and volume of data, the complexity of transformations, the target system and the required performance. 

Build Data Security Mechanisms 

Data Security: Deploy methods to ensure the security of data both during transit and while at rest in the database. This includes robust access management system, sensitive data encryption, and frequent security audits. Modern data security tools leverage advanced technologies, like artificial intelligence, machine learning and automation, to provide robust security measures for data protection. Some of these tools are: 

• Cloud-Native Security Platforms: Tools like Prisma Cloud by Palo Alto Networks, Google's Chronical and IBM Cloud Pak for Security provide comprehensive security for multi-cloud and hybrid cloud environments. 
• AI- and ML-powered Security Solutions: Tools like Darktrace and Cylance use artificial intelligence and machine learning to predict, detect and respond to threats in real time. 
• Security Orchestration, Automation and Response (SOAR) Tools: Solutions like IBM Resilient, Splunk Phantom and Swimlane enhance the efficiency of security operations by automating tasks and orchestrating responses to incidents. 
• Endpoint Detection and Response (EDR) Solutions: Tools like CrowdStrike Falcon, SentinelOne and Carbon Black provide real-time monitoring and protection for endpoint systems from various cyber threats. 
• Zero Trust Network Security Tools: Solutions like Zscaler, Akamai's Zero Trust and Cloudflare Access help enforce the zero trust model, which assumes no user or system is trusted by default, whether inside or outside the network. 
• Data Loss Prevention (DLP) Tools: Modern DLP tools such as Symantec DLP, Forcepoint DLP and McAfee DLP provide advanced features such as fingerprinting data, machine learning-based analytics and integration with cloud and other IT services. 
• Blockchain-Based Data Security: Blockchain technology can improve data security due to its decentralized, transparent and immutable characteristics. For example, Guardtime uses blockchain to ensure the integrity of data. 
• Advanced Threat Protection (ATP) Solutions: Tools like Microsoft ATP and Symantec ATP offer comprehensive, coordinated protection against sophisticated threats across endpoints, networks and email.

Data security is a continuing process and requires not only the use of the latest tools but also a commitment to best security practices, regular audits and continuous staff education. 

Data Visualization 

Incorporate a dashboard that makes data easily digestible and visible to stakeholders. This tool will provide insights and metrics that will help in making business decisions. Here are a few commonly used data visualization tools: 

• Tableau: This is a powerful tool to create interactive, real-time dashboards and access data sets from multiple sources. It offers robust reporting and sharing capabilities. 
• Power BI: This tool by Microsoft allows users to create interactive reports and dashboards using a simple drag-and-drop interface. It also offers the ability to embed reports in other applications. QlikView: QlikView supports a variety of analytics and business intelligence functions, facilitating the creation of sophisticated reports and dashboards. 
• Looker: Looker is a data platform that makes it easy to create, deploy and iterate on data visualizations and to share these across an organization. 
• D3.js: This is a JavaScript library that allows users to create sophisticated data visualizations for web applications. 
• SAS Visual Analytics: It provides interactive reporting and dashboards, and self-service data discovery, and is a part of the SAS Business Intelligence Suite. 
• Google Charts: This is a straightforward tool for creating a variety of charts and graphs that can be used on websites. 
• Datawrapper: This is an online tool popular among journalists for creating simple charts or maps quickly. 

These tools cater to different needs and vary in their complexity, required skill level, cost and versatility. 

Real-Time Data Processing 

We are living in an era where real-time data streaming is becoming extremely important. Almost every consumer-based application requires real time data updates. We need companies to make this transformative change and adopt real-time techniques. Real-time analytics will make sure the latest information is available for consumers for accurate, timely decisions. 

Real-time data processing tools are designed to process or analyze data as soon as it enters the system or database. These are often used in streaming applications where immediate insights are crucial. Here are some commonly used real-time data processing tools: 

• Apache Storm: Known for its real-time processing capabilities, it enables users to smoothly process unbounded streams of data.
• Apache Flink: This open-source stream processing framework is mainly designed for real-time data analytics, batch processing and machine learning. 
• Spark Streaming: As part of the Apache Spark platform, it delivers high throughput for processing real-time data streams. 
• Kafka Streams: Developed by LinkedIn, Kafka Streams is a client library for building applications and microservices that process streamed data in real time. 
• Google Cloud Dataflow: It's a fully managed service for executing Apache Beam pipelines within the Google Cloud Platform, performing both batch and real-time data processing tasks. 
• Amazon Kinesis: Provided by Amazon Web Services (AWS), Kinesis is capable of processing massive amounts of data in real time and can be used for real-time analytics, log and event data collection, and more. 
• Apache Samza: Developed by LinkedIn and incubated by Apache, Samza is designed to handle real-time data feeds at scale. 
• Azure Stream Analytics: A real-time event data streaming service from Microsoft Azure that includes out-of-the-box integration with Event Hubs, IoT Hub and Blob Storage. 
• Pulsar: Apache Pulsar combines high-performance streaming with flexible queuing in a unified messaging model. 

Each of these tools has its own strengths, and the choice among them depends on factors like the volume and velocity of data, the nature of the insights required, the existing technology stack, and the required reliability and fault tolerance. 

See also: The True Cost of Big (Bad) Data

Here is how a modern data stack looks in an integrated modernized data platform: 

Other important factors to consider include:

User Training: Provide usage manuals or training to the employees on how to use the data platform and understand the information presented. 

Maintenance and Upgrading: Ensure regular performance tuning, keep up with the changing business goals and meet new project requirements. Implement feedback loops for constant iteration on the design and function of the data platform. 

Compliance: Make sure all the data collection, storage and usage comply with the applicable legal and regulatory requirements. 

In conclusion, moving toward a unified architecture for managing data is crucial for businesses in the age of digital transformation. This process involves defining clear objectives, identifying data sources, creating a data mode and managing the ingestion, storage, scalability, transformation, security and visualization of the data. Numerous tools are available to support each of these steps, each suited to different types of data and use cases. The right selection depends on the company's specific needs. This integration not only fosters efficiency and accelerates decision-making but also opens opportunities for innovation, helping companies to stay competitive and realize their business visions. 

An African proverb says, "It takes a village to raise a child." Similarly, it takes a team of data scientists, data architects, programmers, systems analysts and end-users from various business divisions for a successful implementation of the data platform.

The customer journey begins the moment a consumer first visits an agency’s website and grows as touchpoints are made throughout the relationship. Customer success is the process of using these touchpoints to keep customers happy and engaged.

Today’s hard market is causing insurance premiums to rise, leading more and more customers to shop around for new policies. This is why it's more important than ever for agencies to focus on the role customer success plays in retention.

Sounds easy enough, but where to start?

See also: The Key to Preventing Insurance Agent Burnout

Use the Right Tools

The easiest way to make an insured happy is to provide the best coverage for their needs. Agents typically need to manually submit quotes individually through each carrier’s portal, which is a time-consuming and tedious process. It limits the number of quotes an agent can provide their client, which means the client may not get the best possible coverage option.

The good news is that there are solutions out there to help! Agencies should consider adopting technologies that streamline the quoting process. Single-entry quoting solutions, for example, allow agents to enter a client’s information once and receive quotes from multiple carriers. Agents can even quote multiple lines of business at the same time. Expanding distribution channels in this manner is an easy way to give clients better coverage options and set them up for success. As a bonus, agents save a significant amount of time, freeing them for higher-value tasks that further contribute to enhancing their customers’ experience.

Embedded insurance solutions are another valuable tool agencies can employ in their customer success efforts. In today’s increasingly digital world, consumers expect an Amazon-like experience -- from access to products and services at the best price to meeting customers where they are -- which is online and at whatever time is convenient for them. Unfortunately, agents cannot be available 24 hours a day. This is where embedded quoting tools come into play. Embedding quoting tools into an agency’s website allows buyers to get multiple, real-time quotes wherever and whenever is most convenient for them. There’s no need to delay getting quotes if the client’s and agent’s schedules don’t align; plus, the clients have time to weigh options before speaking with their agent.

Foster Relationships

Now that the agency has streamlined its quoting process to find the best possible coverage for the client, and the client is happy, what comes next in the customer success process? Fostering and maintaining a positive relationship.

It’s important to maintain an open line of communication with clients. Agents are in a unique position to serve as trusted advisers as clients navigate new risks and claims. Checking in regularly gives agents a chance to answer questions, assess new risks and provide strategic advice when needed. It also allows the agent and client to develop a comfortable rapport, making the client more likely to stay with the agency.

Integrating an organized CRM system can make this easier. Throughout the customer lifecycle, agents can log calls with the customer and leave notes. This allows the agent to refer back to past conversations. They can also set reminders to reach out to the customers at various touchpoints to ensure no customer gets left behind. These little actions pay off in a big way by strengthening relationships with clients.

See also: Retaining the Millennial Insurance Agent

Increase Retention

Customer success efforts look different at every company, but the goal remains the same: develop strong relationships and help customers be successful and satisfied with both the service and the company as a whole.

Many things go into making a customer happy, but providing the best service possible and having strong relationships are the best ways to increase retention and contract renewals for the agency.

The world of insurance is being shaken up by technology startups targeting major emerging markets such as Latin America, Africa, and the Middle East. These insurtech ventures are using mobile apps, digital platforms and data analytics to provide insurance to millions of underserved people in developing economies.

After taking root in the U.S. over the past two decades, insurtech is now rapidly expanding across emerging markets that have large, underinsured populations, rising middle classes and widespread smartphone usage. Powered by mobile technology, insurtechs are delivering customized insurance solutions tailored to local needs and customs.

Insurance is often overlooked as a driver of economic growth in emerging markets, but it's a key behind-the-scenes factor. Technology is providing a path to bring affordable insurance as a financial safety net to the masses.

Insurtech's Latin America Surge

Latin America has emerged as a hot insurtech market, fueled by a growing middle class, low insurance penetration rates and high digital adoption. The region's insurance market hit $174 billion in 2022 and is expanding at an 11% annual clip, outpacing global growth.

Latin American insurers have higher expense ratios than their European counterparts, pointing to massive opportunities for insurtechs to support and enable the current market players, while expanding product portfolios, digitalizing customer interactions, creating new business models and contributing to closing inequality gaps.

Brazil has taken the lead, accounting for nearly half of Latin American insurtech investment. Its insurance regulator is implementing an "open insurance" system that advocates sharing data across industries to create innovative cross-sector products and services at lower costs.

One insurtech cashing in is Olé Life, a digital life insurer offering instant approval for up to $1 million in coverage across 30 countries. The Miami-based startup uses artificial intelligence and decades of underwriting data to rapidly assess applicants via mobile apps and web platforms.

Life insurance protection gaps totaling $162 billion remain in Latin America, underscoring the opportunities.

See also: Insurtech Startups Are Doing It Again!

Africa's Daunting but Promising Market

Africa represents the most underinsured region globally. Despite a $70 billion market, 97% of Africans lack insurance coverage. Technology can help bridge this vast protection gap.

South Africa accounts for 65% of African premiums, with life insurance representing 80% of that market. Microinsurance for low-income populations is an area of focus for insurtechs across the continent.

Companies such as Pula Advisors are using digital platforms to sell affordable crop, life and health policies tailored to smallholder farmers and informal workers. In just a few years, Pula has amassed over 15 million microinsurance customers.

Cultural barriers such as lack of awareness, religious objections and distrust of conventional insurance hinder adoption, however. And regulatory hurdles, uneven mobile penetration and lack of underwriting data pose challenges.

In the realms of healthcare and insurance, insiders in the industry suggest that many individuals still prefer to rely on God as the ultimate — or only — physician or allocate their financial resources to address other needs first. The tendency among Africans is to neglect their health until it becomes an emergency.

Middle East's Islamic Insurance Drive

With the lowest global insurance penetration, at under 1%, the Middle East is a tough nut to crack for insurtechs. But respecting Islamic financial principles, insurtechs are gaining ground with innovative "Takaful" insurance policies based on concepts like mutual protection and risk-sharing.

The global Takaful insurance market hit $31.7 billion in 2022 and is projected to reach $126.8 billion by 2032, with Saudi Arabia the largest market. Takaful policies, while open to all faiths, align with cultural values across the Muslim world. 

The United Arab Emirates and Saudi Arabia have emerged as Middle Eastern insurtech hubs, nurturing startups through regulation, sandboxes and partnerships with conventional insurers. UAE's Sukoon Insurance recently partnered with startup WAX to launch the country's first insurance product for digital collectibles.

See also: The Next Wave of Insurtechs

Hurdles and Necessity

While insurtech companies face challenges navigating complex regulations, underdeveloped insurance ecosystems and a lack of underwriting data in emerging markets, the primary objective remains clear: providing affordable insurance and risk protection to billions of underserved individuals.

Insurance plays a pivotal role in financial stability and inclusion, yet its penetration in emerging economies has been hindered by accessibility barriers. Insurtechs have the potential to overcome these obstacles through technology, customized products and innovative business models.

As incomes rise and demographic shifts occur, emerging markets present the largest growth opportunity for the insurance industry in this century. Insurtech holds tremendous potential to enhance financial resilience and inclusion. By harnessing the combined power of technology and local market knowledge, start-ups can create new avenues to protect the underserved.

As insurtech ecosystems mature in emerging markets, their impact is expected to extend beyond insurance alone. Analysts predict that they will expand access to financial services, contribute to economic development and improve society's ability to manage risks.

This article is adapted from a longer version on LinkedIn.