Customer experience (CX) has always been vital to the insurance industry, but fundamental aspects of it have changed. Historically, agents and customer service representatives were the main points of contact with consumers and clients, and they defined CX. But today, CX is distributed across a more complex, hybrid structure; customers interact with insurers through multiple digital channels as well as trusted intermediaries, meaning insurers must support both direct and agent-led experiences to ensure the client is receiving the best customer experience possible.

Many carriers fail to meet the demands of this multichannel CX environment due to outdated, batch-based processing, lack of access to real-time data, and aging or poorly designed systems that don't support digital-first engagement. A survey of 250 producers revealed that agents increasingly support multiple lines of business—life, annuity, and P&C—demonstrating the necessity of a unified view of customer experience without the current inefficiencies and disjointedness.

Improving customer experience starts with addressing one of the biggest obstacles in insurance: data complexity.

Insurance data is complex, inconsistent and often redundant.

A single carrier can have 35,000 different data attributes in their life products alone. In addition to the natural complexity of the industry, legacy systems and decades of product layering have created overlap between data structures, making them extremely inconsistent. In some cases, a single data attribute is replicated 10 to 18 times across various internal systems.

The result of this overlap and inconsistency is that insurers lack a single source of truth when it comes to their customers. Holistic views are hard to assemble because data is spread across many systems and, in many cases, inaccessible. Business users struggle to find what they need, often using shadow systems and workarounds to piece together elements of a fragmented customer picture. Although it feels more challenging to implement, data modernization is equally important as system modernization. Without a clean, unified data foundation, carriers struggle to deliver real-time transactions, enable intelligent automation, or personalize experiences in meaningful ways.

And, if the picture can't be fully drawn, then how can a carrier build customer personas, map customer journeys, or any of the other more advanced steps in optimizing CX?

Solving the data problem isn't optional — it's the foundation for modern CX.

Unified data is essential for omnichannel success.

A single source of truth is essential for analytics, AI implementation, and optimized client service, but it remains elusive for many insurers. Legacy platforms create data silos, and multiple generations of products cause data to be inconsistently transformed and stored — determining the authoritative source at any given moment becomes a challenge. Traditional approaches to centralizing data often backfire, resulting in rigid structures that restrict access. Instead, carriers should focus on data fabrics, governance models that support usability, and democratized access. If CX platforms rely on outdated or conflicting data, any improvements will be short-lived.

True omnichannel experiences require more than channel availability. Omnichannel experiences demand consistent, connected service across every touchpoint. Agents and customer service representatives need visibility into all prior interactions, whether through digital self-service, a call center, or an in-person meeting. Agents should be able to see online transactions, even if they're incomplete, to help clients pick up where they left off. They should be able to see the attempted transaction and how it can be completed to create total understanding. Data governance across all channels is vital to making holistic CX possible.

New PAS technology helps insurers meet CX expectations.

Full spectrum transparency requires modern policy administraton systems (PAS) with real-time application programming interfaces (APIs), common data services, and unified interaction histories. Only then can the entire ecosystem of clients, agents, and employees operate efficiently to deliver a cohesive experience.

The latest PAS technology helps insurers enhance CX with a focus on modularity — like API-first design, microservices, and event-driven architecture. Modern PAS solutions support the real-time data flow critical for creating smooth and responsive CX experiences, allowing changes to propagate instantly across systems without replication.

Carriers are also embracing cross-system product bundling, intelligent workflows, advanced analytics, and, increasingly, agentic AI. These technologies reduce manual intervention, accelerate underwriting and claims, and enable dynamic, personalized engagement. Ultimately, the new generation of PAS empowers insurers to evolve with customer expectations — not just react to them.

Successful CX requires rethinking core technology.

Insurers that treat digital transformation as a front-end exercise will continue to struggle. True CX gains come from rethinking the core — modernizing policy admin systems, untangling data complexity — and embracing omnichannel strategies built on real-time, API-driven infrastructure.

In an age of automated processes, customers' expectations for a fast and responsive customer experience are only rising. The carriers that succeed will be those that can deliver seamless, data-driven, omnichannel experiences by aligning the right technology with a clear, execution-focused strategy.

You work at an insurance company, in an industry where time plays a critical role in gaining a competitive edge. Your team has an idea and a vision for a new insurance product that answers real market needs. You take it to IT, and the response is: We can deliver in two to three months. Do you really have time to wait?

For decades, the process of introducing a new tariff, modifying terms and conditions, or updating underwriting rules resembled a slow, multi-stage cycle between business, IT, and legal. Every single change, even the smallest, required developers to translate business logic into code, followed by lengthy testing and deployment.

New technologies are changing this picture for good.

A configurator for change and innovation

More and more often, the industry is talking about product configurators that, powered by business rules engines (BRE), flip the traditional dynamics of product launches. Instead of waiting on IT, business teams can create and adjust product logic on their own. With intuitive, no-code or low-code graphical interfaces, users define every aspect of how a product works. They decide how pricing is calculated, which variants and options are available, who qualifies for a policy, and under what conditions. All those complex dependencies that used to be buried deep in the code are now transparent and fully configurable.

The fundamental shift is that these tools are designed with non-technical users in mind. Instead of writing complicated scripts, they define rules in decision tables, build calculation functions, or even model entire processes through visual diagrams.

What can product configurators be used for?

One of the key roles of product configurators is speeding up time-to-market for new products and modifications. Business teams can also set up pre-defined benefit packages or dynamically segment customers to offer personalized terms.

In underwriting, configurators become the central tool for defining and updating risk assessment rules. Instead of relying on static guidelines, underwriters can continuously adjust logic to support both manual and fully automated processes. The same applies to pricing – all aspects of rating logic, from simple validations and discount/markup conditions to complex premium calculation algorithms, can be managed centrally and in real time.

Configurators also bring order to managing policy terms and conditions and integrating with policy administration systems (PAS). Mapping products and their rules into the core system becomes a straightforward, configurable process, ensuring consistency throughout the policy lifecycle. In addition, these tools often serve as a central repository for reference data such as address dictionaries, transaction codes, or vehicle classifications, ensuring data consistency across the organization and boosting operational efficiency.

How can you be sure this will work?

Traditionally, the guarantee that a solution would function as expected came from IT. When business takes on the role of product creator, there's a natural fear that something might go wrong.

However, modern configurators have built-in testing mechanisms. For example, an analyst creating a discount rule doesn't need to wait for a deployment cycle to verify it. They can instantly run single test cases or entire regression test suites to see how the change affects the entire product portfolio.

Equally important are full version control and auditability. In insurance, being able to track, compare, and roll back changes when needed is essential. Configurators maintain a complete history of every modification, making it easy to manage multiple product versions - for instance, rolling out new terms on a specific date, tailoring offers to different sales channels or customer segments. Detailed audit logs ensure complete transparency and regulatory compliance.

More than just speed

Using a product configurator should be seen as an investment that quickly pays off. The first benefit you'll notice is a dramatic reduction in time-to-market - from months down to days. That allows you to respond faster to competitor moves or regulatory changes.

You'll also gain independence from IT.

When a new product idea or modification can be tested and rolled out quickly, the organization becomes more agile and responsive.

Finally, automating manual processes directly reduces operational costs and minimizes the risk of human error.

What's next?

Analysts agree that the next stage of evolution for these tools is the integration of rule-based logic with predictive models and artificial intelligence. Imagine a system where the configurator not only executes defined rules but also leverages AI recommendations to optimize pricing in real time, automate underwriting decisions based on predictive analytics, or flag potential fraud attempts.

Personally, I can't wait to see this future unfold.

The healthcare system in the U.S. processes more than a billion insurance claims each year. With this scale comes complexity, administrative cost, and inevitable risk: denials, billing errors, fraud, and compliance issues drain billions of dollars annually from payers and providers alike. For decades, insurers have relied on manual reviews, retrospective audits, and rigid rule-based systems to manage these risks. While effective to a point, these methods have not kept pace with the increasing volume of claims, the sophistication of fraud schemes, and the demand for faster reimbursements.

Artificial intelligence, particularly when paired with machine learning and advanced data analytics, is beginning to transform this space. By shifting from reactive oversight to proactive risk detection, AI offers insurers, providers, and patients the possibility of fewer denials, lower costs, and greater trust in the system.

At its core, the value of AI in healthcare claims lies in three practical applications: predicting denials, catching billing errors, and spotting fraud patterns. These are not speculative ideas; they are real-world use cases that are already being implemented by both providers and insurers today. Let's examine how these applications reduce risk across the claims lifecycle—and what the future may hold.

1. Predicting Denials Before They Happen

Denial management is one of the costliest pain points for providers. Industry estimates suggest that five to 10% of all submitted claims are denied on the first pass, with more than half of those denials being potentially preventable. Each denied claim not only delays reimbursement but also creates costly rework that clogs up revenue cycle operations.

AI can now predict the likelihood of a denial before a claim is ever submitted. By analyzing historical claims data—including payer rules, provider specialties, diagnosis/procedure combinations, and previous denial trends—AI models can assign a risk score to each claim in real time.

For example, if a claim has a high probability of being rejected for lack of medical necessity, the AI system can alert the provider's billing team to attach supporting documentation up front. Similarly, if prior authorization is likely required, the AI can flag it before submission.

For insurers, this predictive capability reduces the need for downstream appeals and resubmissions—streamlining operations and lowering administrative costs. For providers, it increases first-pass acceptance rates, which directly translates into healthier cash flow.

Looking ahead, we can expect predictive denial prevention to become more personalized. Models will adapt not only to payer rules but also to patient-level risk factors and provider-specific patterns, allowing a more dynamic and customized submission process.

2. Catching Billing Errors With Precision

Billing errors remain one of the largest sources of claims risk. Sometimes they are as simple as mismatched patient identifiers or incorrect coding; other times they involve systemic issues like upcoding, unbundling, or duplicate charges. Historically, insurers have relied on post-payment audits and claim edits to catch these problems—but by then, money has often changed hands, and clawbacks are difficult.

AI shifts this from retrospective correction to prospective prevention. Natural language processing (NLP) models can scan clinical documentation and compare it with coded claims in real time, ensuring that the story told in the medical record aligns with the claim being billed. Machine learning algorithms can also detect subtle inconsistencies that humans or rule-based engines might miss—for example, a high-cost procedure appearing in an outpatient setting where it is rarely performed.

The practical impact is twofold:

• For insurers: Reduced leakage due to overpayments and more consistent application of policy rules.
• For providers: Fewer costly audits and repayment demands, and improved compliance with payer contracts.

Soon, we can expect even greater integration between electronic health records (EHRs) and claims processing systems. Imagine a workflow where AI not only detects an error but automatically suggests the corrected code or documentation needed—turning error detection into real-time error resolution.

3. Spotting Fraud Patterns at Scale

Fraud remains the most complex and costly risk for insurers. Estimates from the National Health Care Anti-Fraud Association suggest that tens of billions of dollars are lost to healthcare fraud annually in the U.S. alone. Fraudulent schemes—phantom billing, kickbacks, medically unnecessary services—are constantly evolving, making it difficult for rule-based detection systems to keep up.

AI excels at pattern recognition across massive datasets. Unlike traditional systems that flag claims based on rigid rules (e.g., a certain dollar threshold), AI can learn the nuanced signatures of fraud: unusual billing frequencies, atypical provider-patient relationships, or geographic anomalies that don't fit established patterns.

For example, AI might detect that a small clinic is billing for a volume of complex procedures far above the specialty's norm, or that multiple patients are receiving identical services at suspiciously regular intervals. These are signals that often escape manual reviewers but are clear to machine learning models trained on millions of claims.

Importantly, AI can also reduce false positives, which are a major burden on insurers. Instead of flooding fraud investigators with thousands of "maybe suspicious" claims, AI can prioritize the highest-risk cases with supporting rationale, allowing investigators to work more effectively.

The future of fraud detection likely lies in collaborative AI ecosystems where payers, providers, and regulators share anonymized data, allowing algorithms to learn across broader datasets. This will make it harder for bad actors to exploit gaps between organizations.

The Broader Risk-Reduction Value

These three core applications—denial prediction, error detection, and fraud spotting—represent the immediate, tangible value of AI in healthcare claims. But their impact is broader when viewed through the lens of risk management:

• Financial Risk Reduction: By preventing denials and fraud, AI helps stabilize cash flow for providers and reduces payout leakage for insurers.
• Operational Efficiency: AI reduces the rework cycle, freeing human staff to focus on exceptions rather than routine processing.
• Regulatory Compliance: Proactive error detection helps organizations stay ahead of compliance audits and avoid costly penalties.
• Member and Provider Trust: Faster, more accurate claims processing builds confidence among patients, providers, and payers alike.

For insurance leaders, the adoption of AI in claims is not just a technology upgrade—it is a strategic imperative for maintaining competitiveness in a rapidly changing healthcare landscape.

Practical Considerations for Insurance Executives

While the benefits are clear, implementing AI in claims operations requires thoughtful planning. Insurance executives should consider:

1. Data Quality and Integration: AI is only as strong as the data feeding it. Insurers and providers must invest in cleaning and integrating data across claims, clinical, and operational systems.
2. Change Management: Staff must be trained to work alongside AI tools, interpreting insights and taking action on recommendations. This is less about replacing humans and more about augmenting their effectiveness.
3. Ethical and Regulatory Oversight: AI models must be transparent and explainable, particularly when they affect payment decisions. Regulators will increasingly demand evidence that AI tools are unbiased and compliant.
4. Scalability and Interoperability: Systems should be designed to scale across multiple lines of business and to integrate with both legacy systems and emerging digital health platforms.

Looking to the Future: A More Intelligent Claims Ecosystem

We are moving toward a future where claims processing becomes increasingly real-time, proactive, and intelligent. Instead of the current sequence—service rendered, claim submitted, denial issued, appeal filed—AI will help shift the paradigm toward "right-first-time" claims.

In practical terms, this could mean:

• Near-instant adjudication of routine claims, enabled by AI-driven validation at the point of submission.
• Continuous fraud monitoring that adapts to new schemes in real time.
• Dynamic contracts between payers and providers, where reimbursement models adjust automatically based on AI-driven insights into quality and efficiency.
• Greater patient transparency, with AI tools that explain in plain language why a claim was paid, denied, or adjusted—reducing frustration and building trust.

The promise of AI is not to eliminate human oversight but to make oversight smarter, faster, and more resilient. For insurance leaders focused on reducing risk while maintaining efficiency, the time to engage with these tools is now—not five years from now.

Conclusion

AI is no longer a futuristic buzzword in healthcare claims. It is a practical, proven tool that reduces risk by predicting denials, catching billing errors, and spotting fraud patterns at scale. For insurance leaders tasked with protecting financial performance and operational integrity, AI offers a rare combination of immediate cost savings and long-term strategic advantage.

The healthcare claims process will always carry some level of complexity and risk. But with AI, insurers and providers can move closer to a system that is not only more efficient and accurate, but also more trustworthy for all stakeholders.

Over the past decade, many insurers have automated key processes, such as document classification, policy issuance, and claims triaging. Many have also implemented rule-based workflows for claims, underwriting, policy servicing, and compliance monitoring. A results has been faster turnaround and reduced operational overhead. 

Still, insurers remain caught between legacy systems and growing expectations from customers, regulators, and the business itself.

Artificial intelligence (AI) is helping bridge that gap. From underwriting and claims to compliance and customer engagement, AI is quietly but smartly reshaping insurance operations. A poll conducted by Global Data in Q3 2025 found that 46% of respondents identified underwriting and risk profiling as the functions most improved by AI. This was followed by claims management at 20% and customer service at 18%.

Let's explore how AI is fueling transformation across key insurance functions.

Smarter, Real-Time Underwriting

Underwriting has long relied on historical data and static models. But today's risk environment is far more dynamic. From climate changes and emerging health risks to evolving customer behavior and stricter regulatory pressures, underwriters face growing complexity and faster change.

One way to deal with the challenges, without missing compliance or overlooking digital fraud, is a strategic adoption of AI tools. Leading U.S. insurers are now using AI to improve efficiency, access real-time insights, and make more accurate risk assessments. According to the Zipdo Education Report 2025, AI-driven underwriting can reduce policy issuance times by up to 50%.

Keeping predefined checklists aside, underwriters now leverage AI to analyze vast volumes of structured and unstructured data sets, many of which would be extremely tedious to process manually.

For example, AI assists underwriters by:

• Surfacing localized risks through geospatial data and NATCAT data
• Flagging inconsistencies in applications or documentation
• Recommending optimal pricing strategies for individual customer profiles

AI acts as a powerful assistant to the underwriters, helping them evaluate risks more precisely and confidently. For instance, the need for health insurance or occupational risk coverage for gig workers can also be fulfilled with customized plans crafted with the help of AI-powered insights. The outcome? Enhanced scope of scalability, better pricing, faster decision-making and reduced risk exposure.

Faster, Fairer, and More Efficient Claims

Claims have long been a pain point, often drawn out, paperwork-heavy, and emotionally taxing for policyholders. AI is helping insurers transform the entire claims lifecycle, from intake and validation to assessment, resolution, and follow-up. A 2025 BCG report stated that AI is enabling up to 50% faster claims processing, 20–50% cost reduction, and, in simple claim cases, real-time resolution for as many as 70% of claims.

Let's take a simple example of an auto insurance claim to understand the role of AI in streamlining claims:

• At intake, AI-powered chatbots can guide the customer to report the incident by capturing videos and photos via a mobile app.
• For validation, AI models can instantly cross-check the claim against policy details and detect inconsistencies or signs of potential fraud.
• During assessment, image recognition tools help to evaluate vehicle damage from uploaded photos and generate repair estimates.
• For resolution, the system can recommend a settlement or flag complex cases for review, ensuring fairness.
• Post-resolution, AI can trigger personalized updates and feedback requests, helping insurers close the loop and improve customer experience.

Automating repetitive steps and offering intelligent insights enables teams to handle claims faster and focus more on empathy, accuracy, and customer satisfaction.

Personalized Customer Engagement

Customer engagement in insurance has traditionally been reactive. Insurers typically contact customers at policy renewal time or respond only when a customer reports a claim. But this model is evolving.

Today's customers are bringing expectations shaped by digital-native companies like Amazon, Uber, and Netflix. Customers have grown accustomed to frictionless, personalized experiences that anticipate their needs and offer relevant recommendations. An industry report found that 75% of consumers say they are more likely to purchase insurance from a company that offers personalized experiences.

This shift is pushing insurers to rethink engagement beyond transactional touchpoints. AI makes that possible by integrating with CRM, policy, and service systems to deliver timely, omnichannel, and relevant communication across the policy lifecycle.

For instance, AI recommends coverage updates when customers move to high-risk areas or triggers reminders ahead of seasonal risks like flood protection during hurricane season. AI also ensures consistent experiences across channels.

This level of personalization helps insurers not only meet rising expectations but also build trust, drive loyalty, and deliver standout customer experiences.

The Road Ahead

The most successful insurers are no longer asking if they should use AI; they're asking where and how it can best support their people and processes.

According to a 2025 Statista report, nearly half of global insurers plan to integrate AI into their operations this year. And it's not just for experimental pilots. AI is deployed to modernize core functions, creating real, scalable value across the enterprise.

But AI adoption must be thoughtful. U.S. insurers value transparency, explainability, and control. That means selecting AI tools that offer clear business logic, allow for human oversight, and align with ethical governance frameworks.

Conclusion: Human Intelligence, Enhanced

AI is not here to replace the underwriter, adjuster, or compliance officer. Instead, it equips them with better data, deeper insights, and more time to focus on serving customers, managing risk, and driving growth.

The most powerful transformation in modern insurance will not come from technology alone but from the synergy between intelligent systems and human expertise. An AI-first core platform for insurance can boost ROI and reduce the complexity of transformation. To realize this, insurers must build an AI-first culture, invest in explainability and ethics, and establish governance frameworks that empower humans and machines to work harmoniously.

Insurance is entering the Agentic Age. Intelligent, autonomous systems that can perceive, reason, act, and learn are redefining how insurance stakeholders operate, compete, and grow. This is not simply automation taken a step further. It is a structural shift that changes the speed, precision, and economics of the entire industry.

Agentic AI consists of intelligent agents that can sense changing conditions, interpret context, make decisions, take action, and learn from results autonomously. These agents orchestrate complex processes, uniting data, enterprise logic, and contextual memory to improve continuously.

Across the industry, scaled deployments of Agentic AI are beginning to deliver measurable results. In P&C, underwriting expense ratios will decline by 15 to 20%, and claims expense ratios by more than 15%. In life, underwriting costs will drop by more than 25%, with benefit expenses reduced by nearly 20%. Claims resolutions that once took weeks will be shortened to hours or less, and payment error rates will fall by more than 30%. These are not incremental gains but step-change improvements.

Agentic AI moves beyond workflow automation and analytics. It empowers systems to combine historical, contextual, third-party, and synthetic data with connected platforms to coordinate complex processes and make informed decisions. The result:

• Faster cycle times: Underwriting processes cut by up to 75%
• Improved retention: Customer loyalty increases by 10 to 20%
• Higher productivity: Output per colleague more than doubles
• Enhanced economics: Marginal cost trends toward zero while precision improves

Agentic AI enables firms to optimize price, product, experience, and operating economics simultaneously, at scale. This is something that was previously beyond reach.

Why it matters now: Markets are moving toward real-time, predictive, and adaptive operations. Firms that deploy Agentic AI early can capture structural advantages such as lower marginal cost, faster execution, and stronger retention that compound over time. Late adopters will struggle to close the performance gap and forgo learning curve effects.

However, many firms are not ready to capitalize on Agentic AI. Legacy technology, disconnected data, manual workflows, and fragmented governance can slow execution and block leverage. This capability debt will further widen the gap between leaders and laggards.

To help overcome such challenges, consider the following strategies:

• Design connected systems: Modernize infrastructure with orchestration layers, application programming interfaces (APIs), and cloud extensions to connect legacy cores to agentic systems.
• Rethink your operating model: Redefine roles, governance, and incentives to support enterprise-wide AI adoption.
• Create consistency: Standardize workflows and embed business logic to enable intelligent orchestration from triage to resolution.

These strategies are supported by five enablers that ensure sustainable scale and impact:

• Strategic alignment
• Organizational readiness and performance management
• Governance and risk management
• Process and workflow design
• Data and technology enablement

Agentic AI is not a future concept: It is here. The question for industry firms is whether you will lead or follow. This is a strategic decision, not a tactical one. Acting now will unlock superior economics, faster execution, and durable competitive advantage. Waiting means falling behind in a market that is rapidly accelerating away from traditional operating models. The time for decisive action is now.

For the full white paper this article is based on, click here.

The insurance industry has undergone rapid innovation over the last decade, but not all sectors within the industry have evolved equally.

Property & casualty (P&C) insurers, for example, have made impressive strides in digitizing and optimizing claims. Life insurance has been slower to modernize. This disparity has resulted in a significant innovation gap between life and P&C claims processing. It stems largely from fundamental differences in claim volume, complexity, and customer expectations. But as the gap grows, it becomes increasingly unsustainable in today's fast-moving, digitally driven world.

Outlined below are the key lessons life insurers can learn from P&C's digital transformation, as well as a road map for how life insurance carriers can accelerate claims modernization while preserving trust, empathy, and compliance.

Why the Innovation Gap Exists

P&C insurers handle millions of claims annually, many of which are low-severity, high-frequency events like fender-benders or storm damage. Keeping pace with claims volumes is what provided incentives for early investment in automation, AI, and self-service to help optimize processes for both the insurer and the insured.

In contrast, life claims are often low-frequency but high-emotion, and manual processes were deemed the most acceptable approach for these emotionally charged events.

Consumer expectations have since shifted.

Wider industry pressures – demographic changes, labor shortages, the pervasiveness of AI, and more – are further catalyzing this transformation. Gartner predicts that by 2026, 30% of enterprises will automate more than half of their network activities.

The call to action is clear: Life insurers must digitize to meet modern expectations without compromising on a customer experience that balances empathy, accuracy, and compliance.

Leveraging AI for Automation

P&C insurers have already built a foundation of speed and efficiency by embracing digital-first operations. From first notice of loss (FNOL) to straight-through processing (STP) and digital documentation, many previously manual claims processes are now automated, rules-based, and augmented with AI. The result is better fraud detection and faster triage at scale.

Major auto and home insurance carriers already use automated workflows and proprietary mobile apps to resolve minor claims blazingly fast. Insurers use these platforms to submit photos of vehicle or home damage, which AI tools instantly assess, use to estimate repair costs, and process in real time. In some cases, claims are approved and paid within minutes.

Alternatively, life claims remain labor-intensive and complex, with paper death certificates, manual policy validation, and disconnected systems leading to long delays. These laggard operations no longer align with customer expectations or enable operational sustainability.

The AI models that are widely used in P&C to assess claim complexity, detect anomalies, and flag fraud in real time can similarly be applied to life insurer workflows – flagging incomplete claims, triaging straightforward cases for fast-track approval, even personalizing communication based on behavioral or demographic data.

McKinsey anticipates that by 2030, more than half of claims activities will be automated.

Digital With a Human Touch

From mobile-first claims submission to real-time status update chatbots, many P&C carriers now offer seamless self-service options that keep customers happy and informed. This has reshaped customer expectations across all lines of insurance – and life insurance is no exception.

But the inherently emotional and often painful nature of life insurance claims make clarity, transparency, and speed essential when adopting practices from P&C.

Rather than being a one-to-one template for life insurance innovation, P&C's use of customer journey mapping and design thinking offers life insurers a model for where to begin when modernizing their operations. By mapping the end-to-end life claims experience – from the beneficiary's first contact to final payout – insurers can uncover and address friction points such as multiple document requests, long silences, or poor communications.

The X-factor for implementing these changes is that, alongside automation and personalization techniques such as instant document upload and multi-channel status updates, life insurers must also create precedents for enabling swift human intervention at key moments. For life insurance, the human touch must never be far away.

Tech solutions must then strive to make the process easier without making it feel cold. Automation should never come at the expense of empathy.

Intelligence Through Data and Ecosystem Integration

Advanced data usage has long been a defining feature of P&C claims transformation. Carriers routinely use third-party data – weather reports, IoT and telematics data, government records – to populate claims automatically, assess risk, and identify anomalies in real time, resulting in faster decisions.

Life insurers can achieve the same effect by integrating with government databases, obituary application programming interfaces (APIs), health records, and even social media, to validate deaths quickly and securely.

A Matter of Life and Death

The innovation gap between P&C and life insurance claims has finally become a solvable challenge, with the barrier to entry for automation, more empathetic customer experiences, and smarter, more connected data ecosystems lower than ever before.

By adopting the automation tactics honed by P&C insurers and anchoring them in the empathy that life insurance demands, life insurers can modernize claims in a way that enhances trust, improves efficiency, and delivers lasting value.

But life insurers must act now. Because reimagining life claims through a digital lens isn't just possible: It's imperative for long-term competitiveness and customer loyalty.

Despite more than a decade of awareness, underinsurance in business interruption (BI) policies remains stubbornly common—and costly. A Marsh study citing Chartered Institute of Loss Adjusters (CILA) data found that 40% of BI declarations were undervalued, with average underdeclarations of 45%. That was in 2012. Recent publications from CILA and the Insurance Institute of London (2024) suggest little has changed.

Insurers continue to see claims where declared gross profit is well below actual exposure, leaving policyholders exposed to reduced payouts, co-insurance penalties, or outright denial.

Why Declarations Miss the Mark

Five recurring issues explain most inaccuracies:

1. Terminology Confusion: In everyday accounting, "gross profit" has one meaning. But "insurable gross profit" is defined differently. For example, it usually leaves out things like investment income but adds back certain expenses that don't appear in normal accounts. If a business uses the wrong definition, its insurance declaration can end up way off the mark.
2. Optimism Bias: Owners assume best-case recovery scenarios, underestimating the duration and financial effect of disruption.
3. Documentation Gaps: If a business doesn't keep detailed or accurate financial records, it becomes very hard to show what the real losses are. Missing information weakens the claim and makes it less believable to the insurer.
4. Operational Blind Spots: Losses from supply chain interruptions, third-party dependencies, or contingent exposures are frequently overlooked.
5. Policy Misunderstandings: Many businesses don't fully understand the fine print in their policies. Things like what counts as extra costs, how savings are treated, or how long cover lasts can easily be misread—leading to claims that don't match what the policy actually covers.

Example of understatement: A mid-sized manufacturer declared its BI exposure based only on its own factory operations, overlooking its heavy reliance on a single overseas supplier. When flooding shut down that supplier, losses mounted well beyond the declared amount—triggering co-insurance penalties and a settlement that was less than half of the actual loss.

Overstatement: The Other Risk

While underdeclaration dominates discussions, overstating a claim can be equally damaging. Inflated or poorly substantiated submissions lead to:

• Frustration and mistrust from insurers
• Reduced settlements or outright repudiation
• Unrealistic expectations by policyholders
• Delays and costly disputes

Example of overstatement: A retail chain calculated its BI loss using projected sales growth from a planned expansion that never materialized. The claim—based on future revenue rather than proven historical trends—was challenged, leading to months of delay and a significant downward adjustment.

Insurers scrutinize every figure. If projections lack evidence or deviate from past performance, credibility suffers—and so does the claim.

Continuing Submissions and the Duty to Mitigate

Another area where claims often falter is timing. Many businesses wait until operations are fully restored before submitting their BI claim, presenting it as a single package. This approach creates delays, invites disputes, and risks misinterpretation of losses.

Best practice is to submit the claim on a continuing basis, updating the insurer regularly as losses and mitigation steps are incurred. Interim submissions allow the insurer to review, comment, and agree on methodology early, reducing the possibility of disputes at the final stage.

Policyholders also carry a clear duty to mitigate their loss. For example, if a critical machine component fails, flying in the replacement part from overseas may cost $50,000. If doing so shortens downtime and reduces the BI loss by $200,000, the insurer will cover the cost. However, if the part costs more to fly in than the BI loss it prevents, the insurer is unlikely to reimburse the expense.

Example: Fire in a Restaurant Kitchen

A mid-sized restaurant suffers a fire in its kitchen when a deep fryer malfunctions, damaging the cooking equipment and forcing the kitchen to shut down for repairs.

Rather than closing completely, the owners hire an external commercial kitchen across town to prepare meals. They then transport the food back to the restaurant, allowing them to stay partially open and continue serving customers.

• Effect if they closed: With no income, the restaurant could have lost $15,000 per week in revenue.
• Mitigation step: Renting the external kitchen and arranging food transport costs $5,000 per week.
• Insurance treatment: Since this reduces the size of the business interruption claim (the restaurant still generates revenue), the insurer covers the reasonable costs of the external kitchen.

This example highlights two key points:

1. Duty to mitigate – The restaurant took steps to limit its loss rather than shutting down completely.
2. Communication with insurer – By discussing the plan with the insurer before committing, the restaurant ensured the extra costs would be covered.

This example highlights why communication is critical throughout the process. Keeping the insurer informed ensures that mitigation measures are agreed to in real time, rather than disputed after the fact.

Whose Job Is It, Anyway?

That need for early engagement and clear communication leads to another common misconception: who is actually responsible for compiling the claim. Contrary to what many policyholders believe, responsibility rests with the insured, not the loss adjuster.

Adjusters may adjust upward where valid losses are overlooked, but their primary role is to challenge unsupported or overstated items. Notably, companies that under- or over-declare are often those that attempted to prepare the declaration themselves, without specialist knowledge of BI policy wording. Doing so not only increases the likelihood of error but can also jeopardize the credibility of the entire claim.

The Forensic Accountant Advantage

Forensic accountants bridge the gap between accounting records and insurance requirements. They:

• Translate financials into insurance terms aligned with policy wording
• Quantify loss precisely through historical analysis, forecasting, and modelling
• Build credible submissions supported by data, documentation, and rationale
• Safeguard declarations by properly quantifying exposure and reducing the risk of penalties or claim rejection

Claims Preparation Costs: A Hidden Benefit

Many policies now include professional fees or claims preparation costs coverage. This provision reimburses the insured for professional fees incurred in compiling and substantiating a claim—including the services of forensic accountants.

Depending on the policy, limits for claims preparation costs can range from $25,000 to over $100,000, more than enough to cover expert involvement from start to finish. For businesses, this often means there is no direct cost to accessing professional support.

Yet too many businesses either overlook this coverage or attempt to "go it alone," exposing themselves to underpayment, disputes, or rejection of their claim.

Final Word

CILA's recent efforts to standardize policy wording are welcome, but the accuracy of BI claims still rests with the insured. Both understatement and overstatement carry significant risk—underpayment on one hand, repudiation on the other.

With financial stakes this high—and with claims preparation costs frequently covered—engaging a forensic accountant isn't just best practice. Submitting claims progressively, maintaining open dialogue with insurers, and taking well-documented mitigation steps are the keys to a faster, fairer settlement.

In short: Get expert help early, keep communicating, and mind the gaps.

Today's digital giants didn't just change the game, they rewrote the rules. They turned customer insight into capital, behavioral data into billion-dollar products, and user experience into enduring brand loyalty. They've built trillion-dollar empires by knowing their customers better than the customers know themselves.

It's mad to think that there's only a handful of these ecosystem drivers that include the likes of Amazon, Alibaba, Apple, and Google. But that's not the craziest part. What's incredible to me is that these ecosystems don't exist in insurance. After all, what these established ecosystems do well is simply to maximize the value of a customer by maximizing their value to a customer. This is achieved through continuous, data-driven innovation and activated through a well-orchestrated ecosystem of partners.

Now consider insurance: an industry that holds more data than most tech platforms could dream of. Not just consumer data but also operational, behavioral, environmental, and risk data. To top it all off, even more data is only an arm stretch away and available from connected cars, smart home devices, wearables, and IoT systems.

The insurance industry collects fresh, high-value insights from millions of interactions every single day, yet most of it sits idle, trapped in outdated systems, fragmented across silos, and rarely used to its full potential. This is a massive missed opportunity, and it's not a stretch to say that the sector really does have the opportunity to emulate e-commerce's proven, multitrillion-dollar, customer-centric business model.

However, the issue is far more than a technology change. This shift and the huge commercial upsides that accompany it require a business model and mindset change. Rather than seeing customers as policyholders, insurers must recognize them as the central product. By harnessing the extensive data sets at their disposal, the sector can create hyper-personalized experiences, optimize pricing strategies, and drive entirely new revenue streams.

This customer-centric shift isn't just about meeting consumer demand for digital services, it's about fundamentally reshaping profitability by applying a successful, established approach.

There are many ways that these business models drive growth through value creation. Building around the customer means you integrate experiences, partners, products and services around people. However, insurers are not typically built this way. Most are built in legacy policy administration systems with data models that sit atop, trying to abstract that policy-focused data into a customer cohort, drive insight and then reapply it back into experiences.

This is far too slow. Like a hot sales lead, customer data is a perishable asset. Its value fades fast if not acted on in the moment. Customers want buying insurance to be fast and frictionless, without being dragged through a long list of opaque, hard-to-answer questions. When we are in a claims process, we need insurers to see and respond to our data in real time. Even when we are being sold new coverage, we ideally need it a few clicks away, or, better still, embedded and in context. And when we move to experiences centered on helping us understand, navigate or even mitigate risks, we need that real-time, too. Tomorrow is nearly always too late.

But this isn't just about speed or seamless claims. It's about making sure the cover we receive fits our individual needs, now and as they evolve. It's about removing stress from the claims experience, not adding to it. And it's about transforming renewal from a transactional moment into a meaningful interaction.

That means having the data to offer genuine advice, based on how a customer's life has changed, or, better still, eaching out when a change is detected through partner data. That's what it means to value a customer - using insight to anticipate their needs, build trust, and position the insurer as a true partner - not just a silent presence that reappears at renewal time with a price hike.

In essence, insurers must massively increase their knowledge of their customers, not just acquire their data. Insurers must then act on this knowledge through embedded, adaptive, and risk-mitigating propositions that meet the demand of dramatically changed demographics, economics and lifestyles.

This requires a business model change, enabled by a new technology foundation and driven by an evolving culture. Core technology built for insurers - especially when built on MACH foundations and designed to function like a true ecosystem driver - can only realize its full potential if it's matched by changes in mindset, structure, and culture.

• No more silos. Everyone in your organization needs to be customer only, not just customer first. Teams must look and act more like agile software development squads than artificial clusters of mixed functions. Actuaries, developers, product owners, experience designers, data engineers, etc., must all work together constantly with clear goals and outcomes.
• Change must become a constant, and roles must move from operational management to customer experience improvements. Claims handling becomes claims optimization.
• Experimentation needs to rise dramatically. Learning fast means never failing. Where all data is mined as a perishable asset and acted on, this includes ways of making people's understanding, use, and experience better, as well.
• Technology must become an enabler of new, unimagined futures, not just an operating entity and IT constraint. Any line of insurance and even complementary non-insurance products needs to be managed and operated from one core platform. There can be no IT bottlenecks or downtime for any reason. Where interoperating partners aren't just about application programming interface (API) models, the issue is about how quickly those partnerships can be applied to experience outcomes.

All of this needs to happen in a business model where the time to generate value from new insights is attainable in minutes, not days.

There's a compelling commercial imperative behind all of this. Happy customers, who easily self-serve through digital interactions and access human support when it counts, are more satisfied and more loyal. Ultimately, they form more trusted relationships and will buy and do more with their insurer.

When your customers buy multiple things from you, their value over risk will start to look far more interesting. We aren't just talking about "multi-car" type propositions, as useful as they can be, we are talking about insurance portfolios or relationship products.

Take life insurance, a market set to transform over the next 18 months to five years. It suffers from increasingly low relevance and low penetration rates. Lifestyles, demographics and life stages have changed dramatically. The propositions this market offers should change as well, adapting as people's financial and health profiles change. Current products, sold once and then engaged when someone dies, need to give way to more holistic protection and life models.

Perhaps underwriters and actuarial roles will finally be fused with customer experience and analytics functions, creating holistic models that when combined will stretch far beyond "policy" thinking.

However, as a result of this need for technological and business model shifts, insurers with their current legacy and modern legacy footprints will struggle. As it is, adaptivity is too slow and expensive. New insurers are emerging, and the market dynamics are now forcing legacy insurers to change -- from regulation asking them to treat customers better, all the way through to new digital and intelligently orchestrated experiences.

Insurance has a new battleground. Deeper relationships, more loved products and services, generating more value through more propositions. This has to replace price-led competition.

The value chain model is broken. Ecosystems aren't optional, and customers aren't things you bolt on to your technological core. They should sit at the center, and everything should interoperate around them.

The reality is that even if you want to operate as a "value chain" business, your best way of minimizing costs and maximizing distribution still lies in being able to value customers and service them in any channel, 24/7, in an increasingly intelligent and personalized manner.

This is the new commercial battleground for insurers. It seems most don't realize it yet. But the emergent competitive forces are beginning to bite, and we see many new emergent forces acting on the industry. Shareholders will start to see this gap, along with capital investors who are already diving in.

We are in exciting times for an industry long held at a tipping point.

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