Around the holidays, it’s common for families to get in the kitchen together and bake traditional cakes and treats. You don’t have to bake every day to know that if you’re baking a cake and each ingredient is stored in a different kitchen, the chances of making mistakes—missing a step, using the wrong measurement, or leaving something out entirely—are high. Similarly, when financial operations rely on disparate systems, mistakes are guaranteed.

In the insurance industry, managing general agencies (MGAs) and insurers often face similar struggles. Disconnected financial operations and fragmented data lead to inefficiencies, errors, missed opportunities, and bad customer experience. Unifying financial operations (FinOps) onto a single software platform brings financial systems into alignment. 

This enables automation in key areas, like invoicing, premium payment, collection, and end-of-month reconciliation. Unified FinOps improve data accuracy, fosters better de-cision-making, and enhances customer satisfaction—and that’s just the beginning.  

See also: Steps to Begin Transforming Claims

1. Improves Data Accuracy and Consistency

Imagine if you and your family were trying to bake a cake but all using different measurement systems. One person is using pints and quarts, but another is using liters. What are the odds you’ll get the recipe exactly right?

Today, many MGAs aren’t measuring the same way across different departments, and each of those approaches can be different from broker and insurer partners, leading to miscommunication and confusion.  A unified FinOps system means everyone is speaking the same proverbial “language” and using the same measurements. Real-time data updates and automatic validation checks ensure everyone in the organization has access to the same accurate and up-to-date data. 

The result? Confidence in financial reporting and regulatory compliance, smoother audits, one-click bordereaux reporting, and no miscommunication about what it all means. Just like with a perfectly baked cake, everything comes together seamlessly.

2. Fosters Better Decision-Making

Imagine trying to execute a complicated recipe with only a list of random ingredients to work from. Flour, eggs, and milk, for example, are the basis of many different things from savory to sweet and everywhere in between. Without the full picture, it’s nearly impossible to figure out whether the desired end result is a quiche or a cake! 

Disjointed financial systems create a similar challenge for MGAs and insurers. By moving all FinOps processes onto a single platform, organizations can make sure everyone is working off the same recipe. Financial data from budgeting, forecasting, and billing is centralized and standardized, enabling a clear, real-time view of cash flow and overall fiscal health. With this level of transparency, companies can generate reports and analytics that uncover previously hidden trends.

This holistic perspective also means better risk assessments, sharper underwriting decisions, and more opportunity for strategic planning. Departments can collaborate more effectively, whether they’re navigating underwriting, claims, or finance. The result is not just improved efficiency but smarter, more agile decision-making that keeps the business on course.

See also: 4 Reasons Digital Transformations Are Failing

3. Enhances Customer Satisfaction

No one likes thinking about insurance, it seems, until it is really needed. Customers expect interactions with insurers to be quick, accurate, and hassle-free. This is where unified FinOps makes a noticeable difference. Consider the experience of calling customer service. If the representative has to dig through multiple systems to find relevant policy information; every minute feels like an eternity. 

Conversely, a unified platform enables quicker policy adjustments and more efficient handling of customer inquiries. Even better, a truly unified FinOps system lets distribution partners access data on a self-service basis, so it’s not necessary to call in the first place. In a manner of speaking, they can do their own cooking with the ingredients you provide.

Accurate and consistent data management also reduces the likelihood of errors, ensuring customers and distribution partners receive correct information and services without frustration. Faster resolution of customer issues naturally leads to higher satisfaction and builds trust, and in an industry where trust is everything, these smoother interactions can boost a company’s reputation and maybe even improve its Net Promoter Score (NPS). (The industry average NPS for insurers is just 37...yikes!)

Conclusion

There’s no single recipe for success as an MGA or insurer, but unified FinOps can bring the whole kitchen together, offering benefits that extend far beyond the back office. From improving data accuracy and consistency to fostering better decision-making and enhancing broker and customer satisfaction, a unified FinOps platform streamlines operations and empowers companies to deliver exceptional service.

For MGAs and insurers, this kind of alignment is not just a technological upgrade—it’s a strategic advantage that drives growth, strengthens partnerships, and secures a competitive edge in an ever-evolving industry. 

Bon appetit!

Too many digital transformation projects are failing. Historically, the success rate of these projects is less than 30%, but in 2018, only 16% of digital transformations improved organizational performance and were positioned so businesses could sustain the improvements long-term. The question: After so many years of transformations, why are so many projects still in trouble?

Four reasons come to mind:

1. Data still hinders organizations. 2021 research shows that 61% of digital projects were canceled because an app or solution couldn’t access the right data.
2. Projects aren’t driven by high-priority business needs. Without a firm business use case that anchors the digital transformation and any new technology to the mission, vision, objectives, and strategy, life insurance carriers are left with a hammer in search of a nail.
3. Carriers aren’t getting employee buy-in. Technology might be the method of upscaling operations and front- and back-office processes, but the employees ultimately drive and determine the outcome of a modernization project.
4. Vendors and technologies are selected for the wrong reasons. Too many times, transformation project decisions are made based on preferred or existing relationships, price, timeline, or unverified claims. Instead, these decisions need to be made based on culture fit, business need matching, and specific product requirements. 

Life insurance carriers need to be smart about how they incorporate technologies into their operations to position themselves for continued growth. To ensure digital transformation efforts are successful and sustainable, now and in the future, life insurance organizations must create a strong data strategy, zero in on their key use cases, acquire support from their employees, and select strong partners for their journey.

See also: A New Era in Life Insurance Underwriting

A strong data strategy sets the foundation for sustainable prosperity.

Life insurance carriers’ systems are treasure troves of data. While carriers see the potential in leveraging that data, many have been unable to because it’s siloed and cannot move freely among multiple channels. If carriers want to unlock their data and extract its full value, they need to plan and execute a holistic data management strategy that includes data aggregation, transformation, and plans for data integration, migration, and storage.

The strongest and most mature life insurance organizations are those with solid data governance principles in place — they collect the necessary data and have comprehensive, integrated standards that encompass all facets of the business and are consistent across projects and departments. By following established standards, they create a common language and format that ensures data can be easily processed and read by other technologies in their ecosystem. Any changes that need to be made are just a matter of making one or two modifications that can be applied to every piece of data — eliminating the need to make custom modifications to millions of data points and effectively reducing future technical headaches and costs. 

Penn Mutual has already created a “triangle” model that follows this concept. The organization built a micro-API architecture that allows them to standardize and integrate the data it has on the policy, the policyholder, and the agent. Then, using that standardized unified data, the company can move quicker, create solutions at a rapid pace, and meet users when and where they are.

Mature data organizations also augment the data reaped from the application process with external data to inform decision-making and create an all-inclusive picture of their customer and the policy environment. A common mistake some carriers make is wielding data in a transient way: simply using it to make a decision and then disposing of it so they don’t have to deal with the security and storage issues that come with keeping it. By doing so, they’re missing out on the aggregated picture the data creates, which can then be transformed into actionable and insightful formats.

Data storage is another vital component of a strong data strategy. Many carriers have a data “warehouse” that allows them to store integrated data from various sources across the organization. These central repositories bear a multitude of valuable information that is essential for business direction and customer growth strategy. But if organizations want to introduce new data sources, phase out antiquated systems or simplify future data extraction and reporting, they need to have a data warehouse plan that allows them to do so without duplicating or merging data from disparate systems. 

By taking the time to create a follow a comprehensive data strategy, life insurance carriers reduce long-term costs, increase their resilience for continuous changes in the IT landscape, and provide a stronger customer experience.

Identifying core use cases can help set a vision and determine actual needs.

Amid the flurry of new information and technologies becoming available, it can be difficult to discern which would deliver the most value. Evaluating whether to adopt a new technology starts with a review of how it compares with your existing business needs and how they are prioritized. It’s not unusual for companies to fall prey to the excitement of a new technology and rush to implementation, believing it will deliver growth opportunities or create great efficiencies. But all too often the desired benefits aren’t realized. That’s because the exercise didn’t start with a clear and highly important business need that required a specific solution. While it’s important to be aware of new and emerging technologies and what they could possibly do, they should only be adopted when they match a compelling business use case. 

By focusing on use cases for technologies under consideration — whether growth related or operational — life insurance carriers ensure they’re not wasting resources on projects that are unlikely to produce great value or solve critical business needs.

Getting employee buy-in can make or break a digital transformation project.

Life insurance companies can’t undergo a digital transformation if their employees aren’t on board. Team members need to be empowered with day-to-day decision making and encouraged to take ownership over the end solution. Otherwise, there’s no amount of technology that will make the transformation successful. To determine if your organization’s personnel is ready and on board with a transformation, consider the following:

• Who in my organization is advocating for change? Executive-level support for a transformation project is necessary, but team leader and manager-level support is critical. They work closely with the employees using the new system daily and can assist with making the transition easier. 
• What personnel are critical to implementation, and do they support the project? This could include business analysts, system architects, quality assurance analysts, project managers, and information technology specialists – anyone who can assist in measuring the success of each project stage and providing final approvals. 
• Who else needs to provide input? Getting the green light from organizational departments like underwriting, actuarial, new business, policy servicing, claims, accounting and payments is essential. Without their commitment to the project, resource and review bottlenecks may emerge, resulting in delays and additional costs. 
• What are employees’ attitudes and concerns regarding this project? Conduct an organization-wide temperature check before pursuing a digital transformation. Gauge employees’ views and see what questions or concerns are present. If there are significant unresolvable objections, that could be a sign your organization isn’t ready. And if the same concerns keep appearing, that could reveal pain points the modernization team may not have considered before. 

Often, the success of digital transformation projects hinges on the alignment, commitment, and engagement of a carrier’s workforce. Without it, there’s little hope for success. 

A well-rounded governance committee brings varied perspectives and expertise to the table. 

Life insurance carriers must look both internally and externally to build a team of advisers that can help make strategic, organizational technology decisions.  

While technology and business expertise can be found in-house, carriers should still look to augment their knowledge with the fresh perspective and market overview that life insurance technology analysts can bring. Analysts understand these digital transformation projects, know many of the key vendors, and can speak to what technology solutions are available that solve carriers’ organizational needs.  

During the vendor selection process, and after assembling a star advisory team, carriers need to look for vendors with years of experience in helping to solve some of their pain points and similar technology use cases. The vendors must have experience working with legacy policy administration systems similar to the carrier’s, and they need a strong record for implementing more modern systems and advising or facilitating data modernization projects – all of which enable them to anticipate and solve for challenges that may arise during the transformation lifecycle. 

At the end of the day, advisory team members are meant to help carriers pick a long-term digital transformation partner, and both will play key roles in facilitating the success of a carrier. Picking the right people is essential.  

See also: Digital Self-Service Is Transforming Insurance

A successful digital transformation is centered on future proofing, preparing carriers to meet customer needs and expectations.  

Digital transformations offer several benefits — such as reductions in cost, strong decision-making, and the fulfillment of business needs — but at their core, they’re all about future-proofing a life insurance carrier so it's in a strong position to improve customer and agent experience, which will be essential in the coming years. 

When Gen Z and millennials enter the life insurance market in full force, being able to make intelligent underwriting decisions with limited information supplied by the customer will become paramount. These consumers expect fast, frictionless, tech-driven service, and life insurers will need to work to maximize the data they have to deliver on expectations. Wearables and Internet of Things devices mean carriers could potentially automate underwriting that occurs throughout the policy lifecycle, instead of the one-and-done method of 40 years ago. But for carriers to fully harness those technologies’ capabilities, they need to consider where their organization is now and where they want to be in the future. What other innovations could be released that help improve customer experience and are they fully prepared to implement them when the time comes? 

The same can be said for the life insurance agent workforce. Agent demographics now consist of traditional agents who are used to writing policies on paper and younger agents who have the same seamless digital experience expectations as consumers from their generation. To support this bifurcated agent workforce, carriers need to consider what organizational systems and processes may need to be adapted or updated to cater to and support the needs of agents of all age brackets.

By undergoing a future-proofing-focused digital transformation, life insurance carriers can ready themselves to meet the evolving needs of their consumers, skillfully adapt to rapid technological advancements, and effectively manage risks.  

Insurers rely heavily on data for risk assessment, pricing optimization, and claims management, but around 80% of all insurance data lies trapped in everyday correspondence, including submissions, legal demands, and medical records. 

The information within this unstructured data is frequently incompletely analyzed, misread, or unused, costing insurance companies tens of billions of dollars annually due to underpricing premiums, insurance fraud, and overpaying claims. The insurance industry’s reliance on human experts to carry out non-core and administrative activities, such as manually entering and reworking information from documents, will contribute to an estimated $85 billion to $160 billion in value lost to inefficiency by 2027, according to Accenture.

Generative AI provides an opportunity to avert these losses by maximizing efficiency and effectiveness across underwriting and claims operations. Accenture forecasts that generative AI will automate up to 62% of underwriting and claims processes. Reducing the manual effort associated with accessing and assessing this data is a critical step toward increasing the value created by human experts. It allows experts to concentrate exclusively on higher-value tasks that drive good decision-making. Additionally, according to Swiss Re, by using AI to extract insights from unstructured data sources, insurers can see a 12% to 25% improvement in their loss ratio compared with companies that don’t use this technology.

What are other potential benefits from generative AI in underwriting and claims?

See also: Cautionary Tales on AI

AI Impact on Underwriting

An insurance-specific AI solution provides several benefits. The first is faster speed-to-quote by accurately extracting data from submissions with 95%-plus accuracy while keeping up with broker demand to bolster the customer experience for policyholders. AI also increases capacity and lowers administrative overhead, reducing the time underwriters spend manually reviewing and inputting data from unstructured sources. 

Generative AI designed for insurance drives better profitability by surfacing more information within unstructured documents, which can reduce error rates and premium leakage by 1% to 3%. Lastly, the technology can improve talent retention to mitigate the effects of insurance’s talent crisis by reducing agents’ tedious, administrative work and empowering them to focus on more strategic, engaging activities. 

Claims Benefits From AI

Insurance-specific AI lets claims teams use insights from unstructured data to reduce claims costs across the entire process, from first notice of loss through claims recovery opportunities. It also enhances customer experiences by accelerating claims document handling time by 90% to settle claims faster. 

The technology lets insurers increase their capacity to contend with spikes in claims submissions driven by growing natural catastrophe risks. It enhances security and reduces compliance overhead through greater quantities of decision data, which can yield insights to help insurers adhere to full process transparency. Thus, AI enables greater cost control while lowering users’ risk exposure.

See also: Who's Getting Results From AI, and Why?

How can insurance companies get started with AI? Below are some steps for success:

1. Select a pilot project by launching a small-scale implementation in a specific use case for a single product or line of business. This allows for testing and refinement before a broader rollout—or to “fail fast” if the value proposition is unclear.
2. Prepare your data to ensure the accessibility of unstructured data. This may involve digitizing paper documents and centralizing data from various sources.
3. Plan your integrations by closely coordinating with IT teams to plan how AI will integrate with existing systems and workflows. Review APIs and other integration strategies.
4. Employ training and change management to prepare teams for AI tools. This includes training on how to use the new systems and understanding “the change curve” to manage a cultural shift toward AI-augmented work.
5. Partner with legal and governance/compliance teams to ensure that AI use aligns with regulatory requirements, internal risk management policies, and broader company values.
6. Enable continuous monitoring and improvement by setting up systems to monitor the performance of AI-augmented processes, gather user feedback, and continuously refine the model based on new data and insights.

The insurance industry faces a significant challenge with unstructured data. However, the opportunity is equally massive for businesses that effectively harness this information. AI uniquely developed for insurance companies’ specific needs empowers insurers to easily and effectively transform unstructured data into structured data, informing underwriting and claims decisions and driving efficiency for maximum business impact.

Let’s challenge some conventional wisdom. It’s not about whether you need an emergency fund—that’s obvious. The real question is about where you keep it. Most people stick their money in savings accounts or money markets, but here’s the problem: These accounts don’t just sit idle—they lose value as inflation eats away at your purchasing power. 

What if your emergency fund didn’t just sit there but actually grew? By placing your emergency fund in an indexed universal life (IUL) insurance policy, you can link its growth to a market index like the S&P 500. This means your money benefits from market gains while staying protected from losses during downturns. It’s not just insurance against emergencies—it’s a way to grow the foundation of your financial future. 

Let’s break down why this strategy offers a smarter way to transform the value of your emergency fund. 

What Is an Emergency Fund? 

Think of your emergency fund as your financial spare tire. It’s there for when life goes flat—a medical bill, a busted transmission, or a sudden layoff. The go-to advice is to start with $1,000 and work your way up to three to six months of expenses. Solid advice, right? 

Here’s the catch: If you’re following this advice by the book, your emergency fund sits in a low-interest savings account, losing value as inflation chews away at it like termites in a wooden house. And emergencies? They’re not always as simple as a flat tire. What if the “emergency” is relocating for your dream job? What if you’re juggling multiple crises and need flexible options? The old-school advice can leave you stranded when life gets messy. 

See also: 10 Reasons to Stress Customer Retention

What Is Safe Money? 

Safe money isn’t just a souped-up emergency fund—it’s the backbone of your financial stability and peace of mind. While emergency funds are there for immediate needs, safe money is your launchpad for bigger opportunities and protection against life’s curveballs. It’s the financial buffer that lets you take calculated risks with your “non-safe” money. Unlike an emergency fund, safe money isn’t just for unexpected expenses; it’s for achieving long-term goals like funding college, retiring early, or leaving a legacy for your family. 

Sure, starting with $1,000 and building a three- to six-month reserve is fine, but stopping there? That’s like bringing a squirt gun to a four-alarm fire. Safe money goes further. It’s about covering big-ticket items—like your kid’s college education or paying off your mortgage—while also setting yourself up for long-term success. Once you've hit those goals, think even bigger. Save beyond six months and use your indexed universal life (IUL) policy to become your own infinite bank. 

Infinite banking transforms your savings into a personal banking system that works smarter, grows faster, and keeps you in control—all thanks to the IRS tax code for life insurance. 

With an IUL policy, your money grows with the market, stays safe from downturns, and can be accessed tax-free when you need it. Plus, with an IUL, your money grows without wealth adviser fees, so more growth stays in your pocket. 

How Much Safe Money Do You Need? 

That depends on your goals. Here are three North Stars:  

1. Financial Freedom: How much passive income do you need to cover your minimum retirement lifestyle? 

2. Ideal Retirement Lifestyle: What’s the price tag on your dream retirement or early retirement? 

3. Enduring Legacy: What size legacy do you want to leave behind to set future generations up for success? 

Once you’ve nailed down these goals, you can reverse-engineer the steps to get there. 

Start With a $1K Emergency Fund 

An IUL provides liquidity through its cash value, allowing you to access your money for emergencies via tax-free policy loans or withdrawals. The funds grow based on the performance of a market index, like the S&P 500, but are protected by a zero-loss floor, ensuring your money doesn’t shrink when markets decline. 

As your cash value grows, it transforms into safe money—a financial foundation that balances growth and protection. This money isn’t just for emergencies any more; it becomes a shield against market downturns and inflation while offering flexibility for future opportunities. 

Over time, your IUL policy can evolve into a private family bank. By max-funding your policy within IRS guidelines, you minimize the cost of insurance and maximize cash value growth. This allows you to borrow against the policy for significant expenses like a down payment on a house, starting a business, or funding your child’s education. The best part? You’re essentially paying yourself back, keeping the money within your financial ecosystem. 

See also: Harnessing Data to Improve Decision-Making

Why This Strategy Works 

• Growth Potential: Your emergency fund isn’t just sitting idle. It’s growing alongside the market, with gains capped to ensure stability and losses avoided entirely. 

• Tax Advantages: The cash value grows tax-deferred, and loans are tax-free, giving you access to your money without unnecessary penalties. Similar withdrawals from a money market account trigger capital gains taxes, eroding your money’s value. 

• Long-Term Flexibility: Unlike traditional savings accounts, an IUL offers a pathway for your emergency fund to evolve and serve broader financial goals, for yourself, your extended, or even your business. 

• Family Legacy: As your policy matures, it not only supports your immediate needs but also becomes a tool for multi-generational wealth, creating a lasting legacy.

By rethinking where you keep your emergency fund, you’re not just saving money—you’re building a dynamic financial tool. An IUL starts as a safety net, evolves into safe money, and eventually becomes a private family bank that grows with you and your family’s needs. 

How to Build Safe Money 

Here’s where the magic happens: building safe money that grows while protecting you from financial potholes. As Warren Buffett says, “Rule No. 1: Don’t lose money. Rule No. 2: Don’t forget rule No. 1.” 

Safe money isn’t about stuffing cash under your mattress or parking it in a savings account. It’s about making your money work for you, carefully. By leveraging strategies like indexed accounts, you can link your funds to the market’s growth, while zero-loss floors ensure you'll never go backward. Bonus points if you minimize taxes and inflation’s slow erosion along the way. 

A Smarter Way Forward 

The wealthy don’t play the money game with one hand tied behind their back, and you shouldn’t either. Here are some strategies: 

1. Leverage the Tax Code: Shift your safe money into vehicles like IULs or FIAs, which offer tax advantages and no advisor fees. 

2. Protect Against Market Losses: Use indexing to benefit from market growth while avoiding losses when the market tanks. 

3. Design Your Policy Account: Work with an agent to “max fund” your account—contribute the maximum allowed under IRS rules to minimize insurance costs and maximize cash value growth. 

4. Access Funds Through Policy Loans: Borrow against your policy’s cash value tax-free and pay yourself back on your own terms.

5. Start Small and Scale: You don’t need a fortune—start with as little as $1,000 and let compound interest work its magic. 

Emergency funds are your financial first aid kit, but safe money is the fortress that protects your financial future. 

Why Traditional Approaches Fall Short 

The problem with old-school advice is that it’s stuck in a fear-based mindset: fear of risk, fear of mistakes, and fear of the unknown. Here’s where it goes wrong: 

1. Hiding Money Under the Mattress: Inflation quietly drains your savings, turning today’s $100 into tomorrow’s $85. Indexing strategies, by contrast, grow your money without exposing it to losses. 

2. Parking Money in Savings Accounts or CDs: Low risk, sure—but painfully low returns. With tools like IULs or FIAs, you can get stronger growth while maintaining security. 

3. Playing the Stock Market: Selling assets to access funds creates taxable events and locks in losses during downturns. Indexing offers market-linked growth without the risks of direct investing. 

It’s time to ditch outdated advice. Stop letting fear hold you back, and start building a financial foundation that thrives in any economy. Your safe money isn’t just a safety net—it’s your ticket to financial freedom, stability, and peace of mind. And placing your safe money in an indexed universal life (IUL) insurance policy is a strategy that’s within reach for almost everyone.

In the insurance industry, maintaining clean and accurate data is crucial for informed decision-making. Data cleansing—the process of eliminating errors and inconsistencies—directly affects underwriting, risk management, and operational efficiency. The CRO Forum highlights that data quality maturity outside regulatory frameworks is inconsistent, often managed at the operational level with limited top management involvement.

Additionally, Deloitte identifies data quality as a top challenge for insurers, necessitating regular audits to ensure data accuracy, timeliness, and relevance.

As the insurance sector evolves with technological advancements, prioritizing data cleansing is more vital than ever to enhance decision-making, ensure compliance, and improve customer satisfaction.

Key Takeaways

• Data cleansing is crucial for accurate underwriting and risk assessment.
• Technological tools like AI can automate data cleansing, making it faster and more reliable.
• Human oversight is still necessary to validate results and make complex decisions.
• Poor data quality can lead to significant financial and compliance risks for insurers.
• Investing in data cleansing tools can enhance customer experience and operational efficiency.

The Importance of Data Cleansing in Insurance

Understanding Data Quality Challenges

Insurance companies deal with heaps of data every day. But not all of it is in tip-top shape. Bad data can mess things up big time, leading to wrong decisions and unhappy customers. Imagine trying to build a house with a crooked foundation. That's what it's like when insurers rely on faulty data. To keep things running smoothly, insurers need to tackle data quality issues head-on.

Impact on Underwriting Precision

When it comes to underwriting, precision is key. If the data is off, even by a little, the errors can lead to big mistakes. Policies might be overpriced or underpriced, and that's not good for anyone. Clean data helps insurers make better, more accurate decisions. It's like having a clear map when you're on a road trip. You get where you need to go without any detours.

Role in Risk Management

Risk management is all about knowing what you're up against. If your data is a mess, you're flying blind. Clean data helps insurers see the whole picture, spotting potential risks before they become big problems. It's like having a crystal ball that actually works. With clean data, insurers can protect their bottom line and keep their customers happy.

Keeping data in check isn't just a nice-to-have for insurers—it's a must. When data is accurate and reliable, everything else falls into place. Policies are fair, risks are managed, and customers get the coverage they deserve. It's a win-win for everyone involved.

See also: A Data Strategy for Successful AI Adoption

Technological Advancements in Data Cleansing

AI and Machine Learning Applications

AI and machine learning are really shaking things up in data cleansing. These technologies are like having a super-smart assistant that can sift through mountains of data, spot errors, and fix them faster than any human could. It's like magic, but real. The technologies help insurance companies clean up their data mess and make sure everything is accurate and up-to-date. This means fewer mistakes and a smoother operation.

Augmented Data Platforms

Imagine a tool that not only cleans your data but also makes it better. That's what augmented data platforms do. They're like the ultimate upgrade for your data, adding extra information from other sources to fill in any gaps. This means insurers can make decisions based on more complete and reliable data. It's like having the full picture instead of just a piece of the puzzle.

Real-Time Data Processing

Picture this: You're making decisions based on data that's up-to-the-minute fresh. Real-time data processing makes this possible. No more waiting around for updates or dealing with outdated information. Insurers can react faster to changes and make decisions that are spot-on. It's like having a live feed of all the important stuff happening in your data world.

Data Cleansing Techniques for Insurers

Anomaly Detection and Pattern Recognition

Here's the deal. Insurance companies deal with a ton of data, and sometimes things just don't add up. That's where anomaly detection comes in. It's like having a super-sleuth for your data. It spots things that look off, like a property value that's way too low or something misclassified. Spotting these outliers early means fewer headaches down the road.

Data Enrichment Strategies

Data enrichment is all about taking what you have and making it better. Think of it as giving your data a little boost. You might add stuff like location information or other third-party data to fill in the blanks. This way, your data isn't just a bunch of numbers—it's got context.

Automated Data Validation

Nobody wants to spend all day checking data manually. Automated data validation tools are like having a robot assistant that makes sure everything checks out. They cross-check data against reliable sources, saving time and cutting down on mistakes. Plus, they let your team focus on the big stuff instead of getting bogged down in the details.

By adopting these techniques, insurers can keep their data clean and reliable, which is super important in making smart decisions and staying ahead in the game. It's about making sure the data is top-notch so everything else can fall into place.

Balancing Automation and Human Oversight

The Need for Human Judgment

Automation speeds things up and cuts down on mistakes. But you can't just let machines do everything. Humans are needed to make those tough calls, especially when things get tricky. Machines might miss the little details that a person can catch. So it's all about teamwork between tech and people.

Setting Parameters and Validating Results

You have to set the rules for these machines. Think of it like setting the GPS before a road trip. If you don't, who knows where you'll end up? People need to check the results, too. Just because a machine says it's right doesn't mean it is. Double-checking keeps things on track.

Avoiding Overreliance on Automation

Relying too much on automation is like putting all your eggs in one basket. Sure, it makes life easier, but what if something goes wrong? You need a backup plan. Mixing automation with human oversight ensures everything runs smoothly. It's like having a safety net.

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

Impact of Poor Data on Insurance Operations

Challenges in Exposure Management

Insurance companies really need solid data for managing risks. Imagine trying to price a policy when the property's value is all wrong or missing. That screws up the whole exposure management thing. You could either end up overestimating and wasting money or underestimating and getting hit hard by unexpected claims. It's a nightmare. Bad data can lead to big financial losses or even leave companies unable to pay claims when disaster strikes.

Consequences for Risk Models

Risk models are like the backbone of insurance. They help figure out how much risk is involved in insuring something. But if the data going into these models is off, the results are going to be off, too. This can lead to mispricing, which means insurers might charge too much or too little for coverage. Over time, this can really hurt a company's bottom line. Plus, it makes decision-making a lot harder. You end up with a skewed view of your risks, which can lead to bad calls on where to allocate resources.

Compliance and Regulatory Risks

Insurance is super regulated. If your data isn't up to par, you're going to have a hard time meeting all those legal requirements. Regulators expect insurance companies to have top-notch systems for managing their data. If you don't, you risk fines and other penalties. It's not just about keeping the regulators happy, though. Good data management also means better security and privacy for customer information, which is super important these days.

Keeping data clean isn't just about avoiding fines and penalties. It's about making sure you're providing the best service to your customers and protecting their personal information.

Benefits of Clean Data in Insurance

Enhanced Customer Experience

Getting your data right means you can give your customers what they want, when they want it. Clean data helps insurers understand their customers better, letting them offer services that hit the mark. No more messy data means claims are handled faster, and everyone is happier.

Improved Operational Efficiency

When your data is squeaky clean, everything just works more smoothly. You don't waste time fixing errors or hunting down missing information. Instead, your team can focus on what really matters, like helping customers and making smart decisions.

Competitive Advantage

Having the best data means you can make the best decisions. Insurers with clean data can spot trends and risks before anyone else, giving them a leg up on the competition. Plus, a reputation for accuracy and reliability? That's a win-win in any market.

Clean data isn't just about avoiding mistakes; it's about setting yourself up for success. Insurers that invest in data cleansing tools not only protect their policyholders but can also see a boost in their profits. It’s all about making the most of the information at your fingertips.

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

Investing in Data Cleansing Tools

Cost-Benefit Analysis

Investing in data-cleansing software can seem like a big expense at first, but these tools save time and reduce errors, which means less money spent fixing mistakes and more time for your team to do important stuff. Plus, they help keep your data in tip-top shape, which can give you an edge over the competition.

Key Features to Look For

When you're shopping for data cleansing tools, there are a few things to keep an eye out for:

1. Automation: Look for tools that can handle repetitive tasks without needing a human to step in all the time.
2. Integration: Make sure the tools can easily fit in with the systems you're already using.
3. User-Friendly: You want something that doesn't require a degree in rocket science to operate.
4. Scalability: The tool should grow with your business and handle more data as you expand.

Implementation Best Practices

Getting these tools up and running isn't just plug-and-play. Here's how to make the process smoother:

• Start With a Plan: Know what data you're dealing with and what you need the tool to do.
• Train Your Team: Make sure everyone knows how to use the new system.
• Monitor and Adjust: Keep an eye on the tool's performance and tweak settings as needed.

Investing in data-cleansing tools might seem like a lot at first, but when you think about the time and headaches saved, it's a no-brainer. Clean data means better decisions and happier customers, plain and simple.

Data Integration and Its Strategic Advantages

Enhanced Risk Assessment

By pulling together information from all sorts of places, insurers get this full-on view of the person they're insuring. It's like having a 360-degree look at everything from personal habits to outside risk factors. This means they can make way better calls on risks, and that's a big win for the underwriting team.

Increased Efficiency and Speed

Now, with real-time data at their fingertips, insurers can zip through the underwriting process. No more dragging their feet; they can check out applications in record time. This is a win-win because insurers can handle more work, and customers don't have to sit around waiting forever.

Improved Customer Experience

When insurers integrate data, they can really start to tailor their products to fit customers like a glove. It's all about making sure the insurance matches what people actually need. Plus, with everything moving faster, customers are happier because they're not stuck in limbo waiting for things to get sorted out. A smooth process makes for a happy customer, and that's what it's all about.

Data integration is like the secret sauce that makes everything better in insurance. It pulls everything into one place, making decisions smarter and faster, and keeping customers smiling.

Future Trends in Data Cleansing for Insurance

Integration With Exposure Models

Insurers are starting to clean data in real time, which means fewer delays when they're figuring out risks. This helps them spot problems quicker and make better decisions. It's like having a super-efficient filter that keeps the bad stuff out and lets the good stuff through.

Evolution of AI Systems

AI is getting smarter. We’re talking about systems that can learn on the fly and adapt to new data patterns. This means less manual work and more accurate data. Imagine having a tool that not only finds mistakes but also fixes them without needing a human to step in every time. This could mean less time fixing data and more time using it to make smart moves.

Real-Time Data Cleansing

Finally, there's real-time data cleansing. This is all about cleaning data as it comes in, not after the fact. It’s like having a broom that sweeps up messes as soon as they happen. For insurers, this means they can act faster, whether it’s pricing a policy or handling a claim. Real-time cleansing gives them an edge, helping them stay ahead in a fast-moving market.

See also: Re-Imagining the Actuary in the Gen AI Era

Challenges in Data Sharing and Collaboration

Ensuring Data Accuracy and Compliance

When you're sharing data in insurance, accuracy and compliance are big deals. You have all these partners like brokers, agents, and reinsurers, and everyone has to be on the same page. If one person is working off bad data, the whole chain is messed up. This can lead to disputes and delays, especially when settling claims. 

Risks of Incomplete Data

Incomplete data is like a ticking time bomb in the insurance world. If you're missing pieces of the puzzle, your risk assessments can be way off. Imagine thinking a building is on solid ground when it's actually floating on a barge, like what happened with some casinos during Hurricane Katrina. That kind of mistake can lead to underestimating risks and making bad calls. 

Strategies for Effective Data Sharing

To share data effectively, you have to have a plan. Here are some steps to get it right:

1. Regular Audits: Keep checking your data for errors or gaps.
2. Trusted Sources: Only rely on data from sources you know are legitimate.
3. Clear Communication: Make sure everyone involved knows what's being shared and why.

Data sharing is not just about the numbers; it's about trust and transparency among partners. Get it right, and you can avoid a lot of headaches and keep things running smoothly.

The Role of Data Cleansing in Regulatory Compliance

Meeting Legal Requirements

Insurance companies have to comply with loads of regulations. Keeping data clean is like your secret weapon for staying on top of those rules. If your data is messy, you're risking fines and other headaches. Clean data means you can easily prove you're following the rules.

Ensuring Data Privacy and Security

Data privacy is a big deal. When your data is in tip-top shape, it's easier to keep it safe from hackers and leaks. You don't want to be the company that loses customer information, right? Clean data helps you lock it down tight.

Maintaining Accurate Records

You ever try finding something in a messy room? That's what it's like searching through bad data. Accurate records mean you can find what you need, when you need it. Plus, it helps keep everything running smoothly when auditors come knocking.

Conclusion

Cleaning data is crucial for insurance companies to work effectively. As the amount of data grows, having accurate and reliable information becomes even more important. By using advanced tools and techniques, insurers can quickly fix data issues, which helps them make better decisions and serve their customers more efficiently. However, while technology plays a big role, human oversight is still necessary to ensure everything is correct. Looking ahead, the insurance industry will continue to rely on clean data to improve their services and stay competitive in a fast-changing market.

Paul Carroll

What do you see as the main challenges in insurance claims today?

Chris Bassett

Most of these topics have been on our clients' radars for quite some time and are now receiving increased attention because the industry has been focused on addressing significant issues over the last couple of years. We've seen global disruption from COVID-19, which led to dramatically changed behaviors around auto insurance, accompanied by various forms of inflation.

Customer experience remains a consistent challenge, particularly in recognizing evolving customer service expectations. There's also the less-discussed aspect of affordability - how to provide exceptional claims experience while maintaining profitability and keeping policies affordable for insureds. This involves understanding what policyholders truly value in their claims experience, while also educating them about the costs associated with providing these services. 

Efficiency continues to be a major focus, with different technologies rotating through the spotlight. We've seen phases of RPA [robotic process automation], digital tools and chatbots, and now generative AI. What we're learning is that efficiency challenges are best addressed by focusing on people and processes first, then strategically implementing technology to support these elements.

Understanding and influencing customer behavior has become increasingly important, particularly following the unexpected shifts in behavior during the pandemic. While the Internet of Things [IoT] was a major focus two to three years ago, we've recognized its complexities and are now exploring different approaches to understanding clients. From a claims perspective, we're seeing increased interest in efficient claims management through parametric coverages for personal lines, especially in challenging areas like Northern California property insurance. 

The industry is also exploring advanced technologies like generative AI, but with a notably more cautious approach than with previous technological innovations. Chief claims officers are taking a responsible stance, carefully ring-fencing trials and waiting for the technology to mature before full implementation. This is particularly important given the sensitive nature of claims decisions and their impact on people's lives. 

These themes have remained largely consistent with what we've observed previously, and we expect them to continue in the short to mid-term.

Paul Carroll

Are you talking to people much about agentic AI? If so, how are organizations responding to agentic AI compared with their current focus on generative AI?

Chris Bassett

The focus right now is predominantly on the fundamentals. While there's significant interest and we've seen some impressive demos of advanced AI technology, most conversations center on addressing fundamental challenges like managing multiple legacy systems. Organizations are concentrating on establishing common data models to improve reporting visibility, streamlining operational processes, identifying capability gaps and working with partners to incorporate effective solutions - essentially getting their core business operations tight. When it comes to more advanced technology, many companies are taking a wait-and-see approach before implementing it into their business operations. This marks a shift from a few years ago when there was more eagerness to experiment with emerging technologies like IoT and chatbots. 

This caution stems from two factors. First, some prior technology advancements haven't delivered the expected results as quickly as had been expected, and that’s led insurers to adopt perhaps a more critical mindset. Second, while there's recognition that generative AI and more advanced forms of AI could play a significant role, these technologies begin to overlap with decision-making functions traditionally handled by claims agents. In practice, we recommend that AI ought to complement human reasoning – but inevitably it adds a layer of complexity to their adoption.

Paul Carroll

I always love the theory of chatbots, and then I deal with one and get brought back to reality. What are some notable examples of how generative AI is being used in insurance claims processing?

Chris Bassett

The most compelling example I've seen is a demo currently being developed into a trial for personal property claims. What makes it particularly impressive is that it features an interface fully built around a generative AI model that not only draws from historical claims knowledge but also actively incorporates real-time information to update its recommendations.

In a live demonstration, the system could convert phone call audio to text and feed that information into the model, automatically recalibrating its decisions and recommendations. When working with photographs, adjusters could isolate specific elements within the images for detailed analysis, which would then update the recommendations regarding the cause of loss. While still in early stages, the technology’s potential is remarkable. 

However, this example also highlighted that human claims agents with sufficient expertise would still be essential for effective decision-making. The real value of the system lies in its ability to reduce ambiguity and provide claims professionals with a comprehensive suite of tools to support their work. It's the most sophisticated application we've seen in this space so far, and it's quite impressive.

Paul Carroll

It's interesting you talk about the human interaction because I gather one of the things you're talking to people a lot about is talent. How are insurance companies addressing talent development and recruitment while integrating new technologies into their operations?

Chris Bassett

Talent development is one of the core areas we're focusing on with clients. We're facing a dwindling talent supply across the insurance industry, particularly in underwriting and claims. The challenge lies in ensuring our clients have well-developed talent pipelines to address these shortages. 

The most effective claims adjusters possess extensive knowledge across different lines of business and understand how their operations work. We're concentrating on establishing methods to bring people in, get them engaged in claims work, and rotate them effectively to build that essential breadth of knowledge. 

When it comes to new talent, we're finding that younger professionals are generally more tech-savvy and familiar with available technologies. This makes the technological adoption process smoother at that level. However, what remains constant is the adjuster's role as the core decision-maker. 

Our most advanced clients are exploring ways to integrate technologies to support and enhance the adjuster's capabilities rather than replace them. When we start considering AI performing the majority of an adjuster's role, we're looking at fundamental changes to the overall insurance model - that's a level of transformation that requires careful consideration.

Paul Carroll

Do you have a favorite example or two of organizations successfully nurturing and developing talent?

Chris Bassett

We recently worked with a client that had an exceptional talent base, yet was looking to take their claims team to the next level. We assessed their existing internal review models and updated them to become part of their talent development cycle. This organization deals with several specialty areas, some highly complex and regionalized. Initially, they were working predominantly in silos and sharing claims handling experience and decisioning insights orally in focus group settings, which is often the case in claims management teams. 

We are helping them transform this model into a comprehensive talent development program that addressed two key challenges. First, it makes it easier for staff to rotate through different loss types, rather than staying confined to one vertical, which often led to eventual exits. Second, we are introducing a focus on coaching as opposed to simply sharing information so that it’s purposeful and learning-focused. 

The redesigned program will create a defined pathway starting with standardized lines around auto and home insurance and then progress to more complex coverages. As employees progress, they will participate in team-based groups with a mandate for structured coaching. This will be integrated with formal learning and development structures in conjunction with HR (with appropriate feedback loops) to provide oversight and facilitate horizontal movement at each level of seniority. 

Under this new system, employees at the director level, for example, will move horizontally between relevant management positions at that rank, completing coaching and training in the process, before being promoted to the next level. This approach reduces the pressure for frequent vertical promotions while creating well-rounded leaders with comprehensive organizational knowledge. The result are senior claims leaders who have been afforded a clear view across the entire claims organization.

Paul Carroll

Any final thoughts on the evolution of claims management?

Chris Bassett

I think the biggest standout to me has been the significant shift in focus over the years. We've moved away from "shiny objects" to concentrating more explicitly on the fundamentals. 

There’s also been a change in how organizations are approaching their foundation-building. Rather than implementing spot fixes, companies are focusing on establishing strong fundamentals that will support future investments and growth. This shift in thinking has been particularly evident over the last couple of years. 

This evolution speaks volumes about our recent experiences, but it also reflects how firms are thinking ahead. Organizations are more focused on getting their fundamentals in place so they're prepared to take advantage of significant changes on the horizon. This pragmatic yet forward-thinking approach represents a significant shift in our industry.

Paul Carroll

I’ve wound up in the middle of a fight among three very smart and accomplished friends who are disagreeing on the future of AI. One of them is basically accusing the other two of claiming that artificial general intelligence, or AGI, is going to be possible soon, while the other two guys say he’s distorting their positions.

I tend to be the pragmatist, saying we can do an awful lot of stuff before we get to AGI. That’s certainly been the history. I've been hearing people say for decades now that AGI is almost here, and they’re still saying that. In the meantime, though, look at all the progress that’s been made even though we aren’t yet answering to robot overlords.

Chris Bassett

The most forward-thinking clients in the insurance industry are considering AGI's potential impact, particularly how it might fundamentally transform the nature of insurance beyond claims processing alone. Whether AGI is realized in the foreseeable future is up for debate, for sure. 

It's similar to quantum computing, with Google's recent announcement generating excitement. While we have clients in the insurance space exploring these technologies and trying to understand their implications, organizations need to be strategic about where they invest their resources. You can't pursue every emerging technology simultaneously at any meaningful depth. 

Exploring emerging or, in the case of AGI, potentially distant technologies requires a clear-eyed approach. All of our executive resources are scarce – time, attention and so on. Understanding advanced technologies and the impact they may have from a risk perspective – what impact they may have on the business – may make good sense, but understanding when the job is done is critical. 

Alternately, exploring these technologies and evaluating them for potential to drive performance benefits or develop new products and services may be a solid bet, but when will you know enough to make a decision to act and how much will that cost? These are important questions to evaluate in the light of the fundamental aspects of current technology that may need to be mastered first.

Paul Carroll

As with AGI, I think I first read, maybe even wrote, about quantum computing 30 years ago. It’s wildly promising, but I'm not expecting to have to deal with it in my working lifetime.

Chris Bassett

Insurance companies aren't technology companies, and that's something we often forget. Insurance, especially claims processing, remains a very traditional business that has been enhanced by technology - which is fantastic – but perspective is key. 

These decisions ought to be grounded in a clear understanding of a firm’s business identity. The best insurers I've observed are the ones who truly understand this concept. They focus on their core business and use that understanding as a litmus test for investments: Will this technology enhance our core business or is it something better suited for a tech company? 

This perspective may be part of the shift we've seen in recent years. While there have been many exciting technological developments over the last couple of decades that have moved incredibly quickly, it's clear that if you don't stay true to your fundamentals and core competencies, you won't remain in business for long.

Paul Carroll

Perfect. I appreciate your taking the time.

About Chris Bassett

Chris Bassett is an insurance strategy and innovation specialist who partners with executives to drive profitable growth through new solution development and solving for complex business and operational challenges. He is currently a senior director with Capgemini U.S.

While I do my best every week to find a topic that will resonate with you and to then provide some insight, I'm always interested to see how many of you click on the link in the Six Things email and read the full commentary I've written. 

I use the information to calibrate my thinking about what's top-of-mind in the insurance world. In that spirit, I looked back at 2024 to see what themes most piqued your interest in the Six Things commentaries I wrote. I think of the readership numbers as a sort of survey, though based on your actual investment of time rather than on abstract questions about what topics most matter to you.

As you might imagine, AI shows up often in the 10 most-read commentaries from 2024. How could it not? But I was surprised by the commentary that topped the list.

Let's have a look.

The most-read commentary was on "peak auto," the fact that car sales appear to have peaked in the U.S. and many other major economies. In fact, that commentary was the most-read by a lot — almost 50% higher than No. 2 — even though it was only published two weeks ago, and a lot of these commentaries have a long tail; in other words, a lot more people are likely to read this piece in the days and weeks ahead. 

I suspect there were two reasons for all the interest. First, auto insurance is a massively important industry, and having sales plateau or even fall will have major effects on who drives, how much they drive and the risks for the cars they drive. Second, despite all the attention to electric vehicles, autonomous vehicles, rising repair costs, etc., there just hasn't been much attention to the peaking of auto sales. 

Nos. 2, 3 and 4 were all about AI, which I believe is both the biggest challenge and biggest opportunity of the next few years. 

One commentary was on "The Third Wave of AI," which involves having an AI act as an agent, a sort of highly efficient executive assistance capable of carrying out tasks on your behalf — with all the efficiencies and risks that new wave entails. A second warned to "Beware the Algorithm!" It noted that lawyers and consumer advocates are demonizing algorithms any time a decision goes against a policy holder, so we should talk about them less and always put a human face on a tough decision. The third looked at "Who's Getting Results From AI, and Why." It described research that found the insurance industry is above average in innovating with AI but has more levers it could be pulling to get the full benefits.

No. 5 asked, "Can We Insure Against Heat?" This was a notion I wrote about simply because it had never occurred to me. I've read (and written) a lot about the indirect effects of rising temperatures, such as intensifying hurricanes and wildfires, but it never occurred to me that we might issue insurance covering the direct effects of heat on people and property. 

No. 6 and No. 9 were on another massive topic, the nuclear verdicts that are growing in size and frequency. One, "Private Equity's Hot New Investment: Suing Insurers," looked at the source of the financing for many of the lawsuits. It said that, while legal system abuse has long been a problem for insurers, private equity and AI are taking the problem to a whole new level. The second, "Forget 'Social Inflation'; Think 'Legal System Abuse,'" looked at how the insurance industry can better make its case that many of these nuclear verdicts are unwarranted and harm consumers by raising premiums for everyone. Hint: Terms like "legal system abuse" and "billboard lawyers" work better with regulators and consumers than the benign, somewhat confusing "social inflation."

No. 7, "Dashcams for All," argues that smartphones and apps are now at the point where we can all have dashcams — reducing fraud, simplifying the adjudication of claims and curbing risky driving. 

No. 8, "The Lawsuit That Had to Happen," looked at how some auto makers and data analytics firms were providing information on driver behavior to insurers, in some cases leading to far higher rates. The piece argued that the suit should clarify a key issue in auto telematics... but perhaps at the cost of a class action and probes by the FTC and Congress.

No. 10, "Steve Ballmer's Classic Mistake," was one of my occasional rants about how important it is to sweat the details when designing the customer experience. Ballmer doesn't need me to tell him that. He's the former CEO of Microsoft. But as the owner of the Los Angeles Clippers, he spent $2 billion on a beautiful new arena, only to then require that attendees interact with the arena's personnel and services through an all-encompassing app that baffled and angered many.

Now let's see what 2025 brings.

In the meantime, I hope you and yours are getting some time to relax and enjoy each other during this holiday season.

Cheers,

Paul

OpenAI released ChatGPT in late 2022, and it became the fastest app to reach 100 million users in history. Consumer novelty, not enterprise business utility, fueled the growth. In the enterprise, outside of content creation and coding, AI is still very much a solution in search of problems.

The tech ethos of "move fast and break things" is challenging the enterprise ethos of "move slowly and protect things," such as market share, customers and cash flow, all things tech startups lack.

As unprecedented sums of tech capital flow into AI infrastructure and startups, enterprise executives are asking the question they always ask: "What's all this going to cost?"

See also: AI: Beyond Cost-Cutting, to Top-Line Growth

Anthropic invested $100 million in training the latest version of Claude, their flagship foundational AI model. CEO Dario Amodei recently predicted training costs will climb to $1 billion next year and $10 billion by 2027. Sharing this with an enterprise executive, she said, "Geez, if a great customer service agent is going to cost us $300,000 a year, I'd rather pay a human."

The trend in enterprise AI is toward smaller predictive and agentic models trained on proprietary data. The challenge is enterprise data is without fail scattered across a phalanx of applications and storage tiers. The big cost isn't model development but data transformation, typically a two-comma proposition.

Beyond cost, enterprise executives are asking, "What's the real business utility of this stuff?"

In health insurance and healthcare broadly, new regulators in Washington and disgruntled providers and policyholders are poised to break the status quo in pursuit of better health outcomes per dollar spent, simplicity over complexity, and transparency over opacity.

See also: Who's Getting Results From AI, and Why?

A Gallup survey released recently finds that only 44% of Americans rate U.S. healthcare good or excellent, down from 62% in 2010. A mere 28% rate the country's insurance coverage highly, an 11-point decline over the same period, despite spending $2 trillion more on premiums.

AI will meaningfully improve health insurance and healthcare delivery. Payers and providers are engaging point solutions and proofs of concept in operational efficiency, value-based care models, member engagement, price transparency, and regulatory compliance.

Typically, enterprise AI developers, based in the data science or IT function, get their hands on a data set and ask, "What tool can we develop?" The better approach is to identify the business problem, find the data and develop the tool.

Developing the algorithm tends to be the easy part. The hard part is changing the workflow, often across fiefdoms, to accommodate the new AI. Starting with the business problem generates big ideas, and big ideas transcend siloed self-interests.

Here's an example of a big idea: Blue Shield of California's Pharmacy Care Reimagined replacing their incumbent pharmacy benefits manager (PBM) with a consortium of AI-enabled partners, leveraging AI to orchestrate the new operation. It's a bold initiative that will take two years to implement. As always with new partners and technologies, a lot can go wrong. The executive sponsor of the program is CEO Paul Markovich.

The payoff? An estimated $500 million in annual savings on pharmacy costs with better price transparency for Blue Shield of California's nearly 5 million members. Now those are gains making all the pain worth signing up for. Everyone wins except the status quo.

Outside of payments, customers interact with their insurance carrier an average of once per year. With so few touchpoints to work with, the written communications insurers send throughout the customer lifecycle, such as welcome kits, policy renewals and claims updates, play a major role in shaping the customer experience. Rather than treating these communications merely as a means to satisfy operational and compliance requirements, insurers should leverage them to build trust and emphasize what sets them apart in an increasingly competitive industry.

Unfortunately, the communications most insurers provide are long and unnecessarily complex and are mostly delivered by mail — or, at best, posted as static PDFs on web portals. This won't cut it with today's mobile-first customers, who rely on their phones to manage nearly every aspect of their lives. Consumers of all ages expect businesses to deliver accessible, user-friendly communications that integrate seamlessly into their digital routines. Anything less feels outdated and inconvenient.

Meeting these expectations isn't possible without first modernizing the outdated, often custom-built systems many insurers use. Major advancements in customer communications management (CCM) technology enable insurers to eliminate the manual processes, technical debt and outdated technology that is stifling efficiency and blocking innovation. 

Here are five capabilities insurers should prioritize when evaluating potential CCM solutions:

1. Centralized control of communications for all channels

When adopting a new communication channel, insurers typically set up an operational and technological silo — for example, managing email separately from printed communications, SMS, web portals and others. These solutions are managed by disparate teams, which increases costs, duplicates operational processes, such as managing the same content in multiple locations, and potentially fragments the customer experience.

Insurers can benefit from a centralized content hub from which communications can be delivered to any channel where it's required. These systems manage content independently from channel-specific templates and layouts — known as the "presentation layer." This approach eliminates the need to manage content in disparate systems for each delivery channel and enables teams to reuse content across channels.

See also: Language Barriers Create Claims Challenges

2. A cloud-based platform to lower infrastructure costs

A 2023 report from Capgemini states that 91% of insurers have begun moving to the cloud, recognizing it as essential to reducing costs and eliminating tech debt. While many have focused on migrating their core policy and customer management systems to the cloud, CCM infrastructure is still typically hosted on premise.

Moving to a cloud-based CCM system significantly reduces infrastructure and maintenance costs. Built on modern tech stacks, cloud-based systems reduce reliance on a small pool of specialized employees needed to keep outdated or custom-built systems up and running. The systems also provide seamless upgrade processes, eliminating the need for time-consuming manual updates and regression testing, while enabling businesses to immediately benefit from the latest features and security enhancements. Additionally, cloud platforms support real-time collaboration, enabling teams to access and modify content from anywhere — an essential capability for modern, dispersed workforces.

3. No-code content to accelerate change cycles

For many insurers, minor updates to their communications can take weeks — or even months — because their legacy CCM systems require costly IT resources to code content updates into communication templates.

Insurers need systems that enable nontechnical personnel to design and update content, set targeting rules and arrange layouts without having to write code. This enables business and communication teams to take control of the content, rules and template layout. As a result, change cycles can happen in minutes, not months, giving the business the speed and flexibility to launch products faster and meet regulatory deadlines with ease.

4. Integrated AI capabilities for improving content clarity

To the average customer, insurance is a complex and unfamiliar subject. Unfortunately, insurers often compound the issue by sending communications filled with industry jargon and dense language that confuse and frustrate customers. A recent report from Forrester states that "customers are 2.7 times more likely to spend more when companies communicate clearly." Insurers that simplify their communications will not only drive better customer engagement but also foster long-term loyalty and trust.

Innovative CCM platforms are making it easier to achieve this with AI-powered capabilities that streamline the process of improving communication clarity. These tools can quickly evaluate an insurer's entire library of content, identifying unclear content using established benchmarks like Flesch-Kincaid scoring. They can also provide rewritten alternatives that simplify complex information, such as coverage details, claims instructions or compliance notices, into language that is easily digestible by the average customer, or even align content with plain language standards like those set by ISO. Some platforms also have capabilities for AI translation, offering insurers the ability to cater to the 22% of Americans who speak a language other than English at home. AI can translate content more than 20 times faster than humans while validating semantic similarity and translation accuracy, thereby reducing translation teams' effort to one of reviewing exceptions and inaccuracies that are caught by the algorithms.

See also: 3 Steps for Insurers to Keep the Human Touch

5. Capabilities that streamline responses from frontline teams

With the agent-led sales model on the decline, a carrier's claims, adjustor and customer service teams are more essential than ever to building a personal connection with customers. These frontline teams often need to follow up with customers after speaking with them, sending personalized, one-off communications that address specific inquiries or provide additional information discussed during their interactions.

Modern CCM platforms enable frontline teams to quickly locate, personalize and send communications while giving their managers control and visibility into what gets sent. These systems integrate seamlessly into the portals and platforms frontline teams use every day, saving them from having to search through shared servers and Word templates to find the most up-to-date version of the desired communication.

Agents can quickly personalize content and graphics using a guided interview process, after which a purpose-built communication using the customer's specific data is generated. Predefined sections of the communication can then be customized through a controlled editing process, which achieves true one-to-one personalization without the risk of sensitive branding and regulatory content being altered. From there, if required, the communication can then be passed to other stakeholders for approval before being delivered to customers on their channel of choice.

Insurers can't afford to wait any longer. The technical debt from legacy systems is only going to keep growing, and the bar for customer experience will only get higher. By moving to modern customer communications management systems which have the capabilities outlined above, insurers can drive efficiency, reduce costs and equip themselves with the agility needed to stay competitive in a rapidly changing market.

The auto insurance industry has long relied on underwriting methodologies that focus on demographic data to determine premiums. However, this static approach ignores critical dynamic factors, such as driving behavior and environmental conditions, that play a significant role in assessing risk accurately.

The application of artificial intelligence in auto insurance is more than just a technological evolution — it's a fundamental shift in how the industry approaches risk, pricing and customer engagement. With advancements in telematics, app-based innovations and computer vision, insurers now have the tools to create data-driven solutions that enable fairer premiums, safer roads and a better customer experience.

See also: Underwriters' Productivity Can Double

Moving Beyond Traditional Underwriting

Traditional auto insurance underwriting relies on static demographic data — age, ZIP code, gender and car type — to determine premiums. While this approach is straightforward, it overlooks key variables that directly influence risk, such as driving habits, routes and time of day. For example, highway driving carries five times more potential loss magnitude than urban driving. Similarly, driving at night is three times more dangerous than daytime driving.

Conventional methods fail to factor in these real-time behaviors, often resulting in generalized risk assessments that either overcharge or underprice premiums. AI-led underwriting changes this equation. By leveraging telematics data, insurers now have access to real-time insights into individual drivers' behavior.

Telematics for Real-Time Risk Assessment

Telematics serves as the backbone of real-time risk assessment. This technology captures data-rich insights from drivers, vehicles and their surroundings, enabling insurers to create comprehensive driver profiles and dynamic, behavior-based premiums. Over the years, telematics has evolved across four key generations:

1. First-Generation Telematics (Hardware-Based)

The earliest telematics involved installing hardware like OBD (on-board diagnostics) devices or black boxes in vehicles. While these devices could accurately track driving data, their high operational and logistical costs made them unscalable. Installing, maintaining and managing these devices presented numerous challenges for scaling across broad consumer bases.

2. Connected Car Telematics

Every new vehicle sold today comes with built-in connectivity features. Connected car telematics taps directly into the vehicle to extract key driving data without the need for additional hardware. While this data may not always be used for underwriting, it enables insurers to promote driver safety through rewards and enhanced risk insights.

3. App-Based Telematics

Smartphones are redefining telematics with their advanced sensors, such as gyroscopes and accelerometers, enabling insurers to collect rich driving data via mobile applications. App-based telematics is cost-effective and scalable, accessible to virtually any policyholder with a smartphone. This tool is expected to dominate over the next decade, as insurers adopt app-driven models for capturing real-time behavioral data.

4. Computer Vision and Video-Based Telematics

The latest innovation in telematics is video-based technology using computer vision. For instance, Tesla's fleet uses multiple cameras to assess contextual driving behavior, predict accidents and enhance safety through automated maneuvers. Commercial fleet owners are rapidly adopting this technology to improve road safety and implement precise risk assessments. Beyond underwriting, video-based telematics also optimizes claims management, offering highly accurate reconstructions of driving incidents.

Together, these advancements in telematics form the foundation for scalable, actionable and real-time risk analysis, driving the future of the auto insurance industry.

Real-World Impacts of AI in Auto Insurance

The integration of AI into insurance is already yielding significant outcomes:

• Dynamic Premium Adjustments: AI factors in diverse variables, such as time and environment, for more precise underwriting. Studies suggest that despite 60% less traffic on the roads, more than 40% of all fatal car accidents occur at night. Real-time data allows dynamic adjustments to reflect these risk levels more accurately.
• Personalized Policies: Customers can now benefit from policies tailored to individual driving habits, replacing one-size-fits-all models. Drivers with safer habits, such as daytime local commutes, may pay significantly lower premiums.
• Improved Customer Experience: Using AI-driven systems, insurers can provide instant quotes, reduce processing times and offer actionable insights for safer driving behavior.

This level of personalization is unprecedented in auto insurance, offering fairer pricing models and incentivizing safer driving.

See also: The New Era of Underwriting

Benefits Across the Insurance Value Chain

AI in auto insurance extends its value beyond underwriting and pricing. It also addresses key components across the insurance value chain:

• Claims Management: Telematics data assists in reconstructing accident scenarios with high accuracy, ensuring fair claims processing. Video-based systems are proving invaluable for settling disputes efficiently.
• Driver Safety Programs: By capturing real-time driving habits, insurers can offer safety recommendations and reward responsible drivers with premium discounts, promoting overall road safety.
• Fraud Detection: AI algorithms identify anomalies and flag potential fraud, saving insurers billions annually. Telematics data brings transparency to claims and mitigates fraudulent activities.

AI's Role in Defining the Future of Auto Insurance

AI is enabling insurers to shift from generalized, outdated methods to dynamic risk assessments grounded in data science. The adoption rate of telematics and AI in the insurance landscape is expected to grow significantly in the coming years. According to McKinsey, the telematics insurance market could grow by more than 25% annually through 2025, driven by a push for personalized services and regulatory support for safer roads.

For auto insurers, this presents both an opportunity and a necessity. Those who fail to integrate real-time, AI-driven solutions risk being left behind, unable to meet evolving customer expectations or compete with agile, tech-enabled players.

For consumers and insurers alike, the benefits are compelling — fairer pricing models, safer roads and streamlined claims experiences. The adoption of AI signals a new era of risk management, allowing insurers to not only respond to risks but actively mitigate them. This is the next generation of auto insurance, and it has AI driving the wheel.