In the wake of recent data breaches, like the 2023 Infosys McCamish Systems incident affecting over 6 million individuals, the insurance sector faces mounting cybersecurity challenges. The National Association of Insurance Commissioners emphasizes, "Insurers and insurance producers must protect the highly sensitive consumer financial and health information collected." Effective B2B Identity and Access Management (IAM) is crucial for safeguarding third-party identities and maintaining trust in this high-stakes industry.

Protecting the identities of third parties — brokers, partners, subsidiaries, agents, reinsurers and customers — through an effective B2B IAM strategy is critical. Failure to protect these identities can lead to data breaches, regulatory penalties, loss of customer confidence and even legal battles.

Managing Third-Party Identities to Meet Stringent Requirements

In the insurance industry, compliance with regulations like the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA) and Digital Operational Resilience Act (DORA) is non-negotiable. These regulations impose strict rules on how companies handle customer data, including third-party access to sensitive information. This challenge grows as external identities — contractors, consultants and brokers — outnumber internal employees.

According to the Thales 2024 Data Threat Report (DTR), external identities account for almost half (48%) of total users accessing corporate networks, a number that rises to 64% if corporate clients, which fall under the B2B category, are included.

This is critical because data breaches resulting from third-party vulnerabilities can have devastating effects, both financially and reputationally. According to industry research, 54% of organizations suffered data breaches thanks to third parties in the last year, and the average cost of a data breach reached $4.9 million in 2024.

B2B IAM systems designed specifically for third-party users can play a critical role in achieving compliance. By controlling who accesses sensitive data, ensuring consent management and implementing robust authentication processes, insurance firms can maintain regulatory standards.

For instance, relationship-based access control (ReBAC) enables these entities to assign permissions based on roles and relationships. Such fine-tuned control prevents unauthorized access and maintains compliance with data privacy regulations, limiting the risk of violations and hefty fines.

See also: How Insurance Fraud Erodes Consumer Trust

Safeguarding Information Through Third-Party Access Control

Protecting customer data is paramount in an industry built on trust. The insurance sector deals with sensitive personal information, from financial records to medical histories. Any breach of this data can have severe consequences, such as regulatory penalties, legal woes and loss of customer trust.

Third-party access is a chink in the armor, as supply chain attacks and data leaks often involve external identities. According to the DTR report, nearly one-quarter of respondents cited external identities as a top three target for cyberattacks.

Effective IAM solutions mitigate this risk by only allowing third parties access to the data they are authorized to view based on customer consent. Multi-factor authentication (MFA), single-sign-on (SSO) and self-service access requests boost security further by reducing the chances of unauthorized access.

User delegation, another key feature of B2B IAM, further improves data protection. Letting partners manage their own users within defined boundaries means the burden on internal IT teams is lifted, and users who no longer need access are rapidly offboarded. This shrinks the window of opportunity for malefactors and removes standing access privileges, which could be exploited.

Streamlining Access While Minimizing Risks

For mid-sized to large insurance companies with complex B2B networks, operational efficiency is paramount. The cornerstone of effective B2B IAM is delegated user management (DUM), which significantly offloads IT and internal business managers. This feature allows organizations to delegate access rights to business partners, which can then manage their own users, creating a streamlined chain of authorization.

For example, P&V Group, a Belgian insurer, implemented a customer identity and access management (CIAM) solution to provide secure, 24/7 access to services for its vast network of brokers and independent advisers. This implementation reduced administrative tasks, created more efficient workflows and improved customer service.

Similarly, Klaverblad, a Dutch insurer, adopted a cloud-based B2B IAM platform that features complete eHerkenning login capabilities, including SSO and two-factor authentication. This solution not only enhanced security but also improved user experience for their independent advisers and business users.

These case studies demonstrate how B2B IAM solutions can simplify the onboarding and offboarding of external users while ensuring compliance with regulatory requirements. By unifying the management of internal and external identities, these systems grant access securely and efficiently, addressing the challenge of security consistency across workforce and non-workforce identities.

As insurance companies digitize their operations, scalable IAM systems become crucial. Automated workflows for provisioning and de-provisioning access improve security, enable faster response times and ultimately lead to improved productivity and cost savings.

See also: Top 10 Challenges for Data Security

Integrating Security and Business Enablement

As business environments grow more connected, managing third-party identities goes beyond security to become a true business enabler. B2B IAM solutions help streamline all these external relationships, ensuring that security does not become a barrier to business growth.

Insurance firms that effectively implement B2B IAM can cut the costs of administering third-party users while protecting themselves against identity-based attacks. These solutions also help insurers stay on the right side of regulatory watchdogs, protect their customers' personal information and enhance operations.

The adoption of dashcams is skyrocketing. While the global dashcam market garnered $4 billion in 2022, it is predicted to surge to $12 billion by 2033.

Compared with many parts of the world, the U.S. has been slow to adopt dashcams, but that is changing. Recent statistics reveal that between 10% and 18% of U.S. drivers employ dashboard cameras. 

Their growing popularity is linked to an unfortunate trend of insurance fraud. Each year, staged auto accidents cost insurance companies enormous amounts of money, which increases premiums for honest policyholders. Worst of all, these incidents put innocent lives at risk. 

The growing presence of dashboard cameras on the road is proving to be a game changer, ensuring accountability and providing reliable evidence in traffic incidents.

See also: Dashcams for All

Examining the latest viral example of car insurance fraud

Just weeks ago, Ashpia Natasha was driving on the Belt Parkway in Queens when a Honda Civic abruptly cut her off, forcing her to brake hard. The Civic stopped completely, before reversing, and smashing into Natasha's Acura. Three of the Civic's occupants jumped out of the vehicle, acting injured and taking photos of Natasha's car.

Unnoticed by Natasha at the time, a fourth passenger exited the Civic and jumped into a waiting red Kia that fled the scene. Initially confused by the bizarre incident, Natasha later reviewed her dashcam footage and suspected it was a case of insurance fraud.

Fortunately, Natasha sustained no injuries in the crash, but her car will cost over $8,000 to repair. The dashcam video clearly reveals the suspicious activities of the Honda's occupants, which have garnered over 67 million views on TikTok.

Common scams that can be revealed with dashcams

Though what happened to Natasha is shocking when viewed online, the scam, or "swoop and squat," is well known. In a dangerous maneuver, a car (the "swoop" vehicle) cuts in front of a "squat" vehicle, causing its innocent driver to brake abruptly and rear-end the "swoop" car. An even more dangerous version occurs on freeways, where multiple criminals box in the victim, preventing evasive maneuvers. Only with dashcam footage do the suspicious movements and orchestration of the crash become evident.

Another scam to watch for is the "left turn drive down." In this scam, the fraudster waves at the victim to indicate that it's safe to turn, then suddenly blocks the path, causing another vehicle to crash into the victim. Fortunately for drivers caught in this scenario, a dashcam can provide clear evidence that the perpetrator gave misleading signals and purposely caused the crash.

In the similar "right turn drive down," the criminal intentionally crashes into the victim's car as they make a right-hand turn. With a dashcam, the recorded footage can show the exact movements of both vehicles and highlight that the collision was set up.

Finally, during the "curb drive down," a driver leaving a parking spot at a curb and merging into traffic is deliberately struck by another car. In these scams, dashcams provide invaluable proof of the staged nature of the incident, showing fraudsters suddenly and intentionally crashing into the victim.

Dashcam footage is becoming the keystone evidence in disputed incidents

One of the most complex and contentious aspects of traffic incidents is dealing with conflicting accounts. When two parties provide differing versions of the same event, the truth often becomes mired in uncertainty. Witness testimonies, while valuable, are influenced by perspective, memory or personal bias. If available, dashcam footage gives us an objective viewpoint.

Imagine a scenario where two drivers claim the other was at fault during a collision at an intersection. Determining the liable party can be challenging without clear evidence, resulting in prolonged disputes and potentially incorrect settlements. However, with dashcam footage, the sequence of events is laid bare in front of investigators, insurers and the courts. The camera captures crucial details, such as the speed of vehicles, the timing of traffic signals, the presence of road signs and the actions of pedestrians.

See also: Preventing Sophisticated Car Insurance Fraud

Dashcams create safer and more accountable drivers

The advantages of dashcams extend beyond personal protection. With more and more people adopting this technology, the widespread use of dashcams is expected to significantly improve overall road safety and accountability.

Fraudsters are less likely to attempt deceitful claims when they are aware of the presence of dashcams because dashcams capture compelling evidence that can substantiate the events leading up to, during and after an accident. Naturally, reduced instances of fraud lead to lower operational costs for insurance companies and more competitive premiums for consumers.

Research shows that, in addition to preventing fraud, dashcams cause reckless drivers to think twice because they know that risky or illegal driving behavior is likely to be recorded. A study of commercial vehicles reveals that 87% of U.S. crashes are due to avoidable errors or risky driving. Another study claims that commercial accidents are 60% less likely and 86% less expensive when drivers employ a dashcam solution that offers feedback to the driver.

The unexpected can unfold in seconds, but dashcams emerge as a revolutionary tool providing clarity amid chaos. The footage they capture saves countless drivers from unwarranted financial burdens and ensures the truth prevails. The presence of dashcams promotes a transparent and accountable road environment and even prevent accidents.

Imagine a world where insurance carriers can predict claims before they happen, personalize policies with laser-like precision and reduce fraud with pinpoint accuracy. Sound like science fiction? It's not — it's the power of artificial intelligence at work.

In a rapidly changing insurance landscape, traditional methods are struggling to keep up with growing complexities like increasing fraud risks, evolving customer demands and regulatory challenges. Insurance carriers are now at a crossroads — either embrace the potential of AI or put the business at risk.

AI is no longer just a buzzword; it's the backbone of smarter, faster and more controlled operations. From claims processing to customer service, AI helps insurance carriers optimize their operations like never before. But how can AI truly unlock the doors for unmatched operational performance for insurers? Let's dive into the AI-driven future and discover how it's reshaping the way insurance carriers operate today.

Statistics related to the impact of AI implementation in the insurance industry:

• Four out of 10 insurance providers use a form of machine learning
• 74% of consumers say they would be happy to get insurance advice online
• 80% of insurers believe AI will transform how they seek information

How Insurance Carriers Can Prepare for Accelerating Changes

As the industry evolves, AI, hyper-automation and data ecosystems are propelling insurers into uncharted territory. No one can predict exactly what insurance will look like by 2030, but one thing is for sure: Carriers that adapt today will have a maximum survival rate.

Embracing the "Known Unknown" of AI

Artificial intelligence is evolving at breakneck speed. Despite the complexities, insurance carriers are embracing it with enthusiasm. According to the KPMG Global Tech Report 2023, 52% of insurers identified AI — specifically machine learning and generative AI — as the key technology to help them achieve their goals over the next three years.

It's no surprise that confidence in AI is growing and integrating with existing systems, too. The 2023 Insurance CEO Outlook from KPMG found that 58% of insurance CEOs feel optimistic about AI delivering strong returns on investment within five years.

Leveraging AI's Abundant Use Cases

Why is AI adoption accelerating at this speed? The answer lies in its practical, wide-ranging uses. From automating back-office processes like claims handling to enhancing fraud detection and improving customer interactions, AI is making its mark across the board. Many of these functions still involve humans in the loop, but the potential for full automation is on the horizon.

As Ilanit Adesman-Navon, head of insurance and fintech at KPMG in Israel, explains, "AI goes beyond simply prompting agents with answers. It can be trained to understand customer sentiment, empathize and offer personalized solutions in real time — transforming customer interactions into seamless, data-driven experiences."

Carriers that recognize and act on the power of AI today are setting themselves up to navigate the accelerating changes of tomorrow with greater efficiency and control.

AI Uses in Insurance and the Overall Impact

• AI-Enhanced Underwriting: AI algorithms analyze vast amounts of data from external sources (e.g., social media, wearables, IoT devices) to provide deeper insights into customer risk profiles. For instance, Swiss Re uses AI for detailed life insurance risk assessments, streamlining the underwriting process and enabling faster decision-making. This leads to more precise underwriting, particularly in personal lines where customer preferences and behaviors can be analyzed in real time.
• Fraud Prevention and Detection: AI-powered fraud detection systems use machine learning models to identify patterns and anomalies in claims data. Allstate, one of America's largest insurance firms, uses AI tools to scrutinize claims that have irregular patterns, identifying fraudulent claims and reducing fraud-related losses. Additionally, AI systems predict the likely cost of claims and assist in triaging them based on severity, allowing insurers to prioritize cases that require immediate attention.
• Predictive Reserving and Triage: These models help adjust reserves and triage claims efficiently. For example, Lemonade's AI, named Jim, reviews claims and cross-references policy details to settle claims. It takes a few seconds, significantly reducing the timeframe of claims process for customers.
• AI-Powered Chatbots and Virtual Assistants: Insurers leverage AI-driven chatbots to improve customer interactions. Geico's virtual assistant, Kate, is a mobile, AI-enabled chatbot that assists customers with policy questions and updates anytime, providing quick, accurate responses that enhance customer satisfaction while reducing the workload on human agents.
• Next Best Offer (NBO): AI systems analyze customer interactions and behavior to provide real-time, personalized product recommendations. By understanding customer sentiment, these tools enhance the agent's ability to cross-sell or upsell insurance products effectively.
• IoT and Wearable Integration: In commercial lines, AI processes data from IoT devices (e.g., sensors in vehicles or factories) to predict equipment failure and reduce risks. This risk management approach helps both insurers and clients to minimize claims through timely interventions.
• AI-Driven Catastrophe Modeling: AI overpowers traditional catastrophe models by incorporating real-time weather data, satellite imagery and historical claims information. This allows insurers to predict the impact of natural disasters more accurately, improving risk assessment and disaster response strategies.
• Automated Renewal Processing: AI systems analyze historical data to predict which policies are likely to renew and which are at risk of churn. Insurers can reach out to customers with personalized offers or adjustments to increase retention rates.

To stay ahead of technology trends, carriers can leverage insurance solutions. These services offer specialized expertise in automating repetitive tasks, such as data entry, documentation processing and customer inquiries.

Firms offering these services integrate advanced AI technologies, including natural language processing and machine learning algorithms. Insurance service providers can efficiently handle vast amounts of unstructured data, extracting key insights and streamlining routine workflows. This reduces the administrative burden on insurance staff, enhances operational efficiency and allows carriers to focus on more complex, value-added tasks.

Embracing Modern Technology Imperative for Carriers

Today, implementing modern technologies is not just a choice — it's a necessity for insurance companies aiming to thrive. Among these innovations, artificial intelligence stands out as a transformative force, with many sectors still beginning to tap into its vast potential.

To gain a competitive edge, being an early adopter of AI can set you apart from the competition. The digitalization of the insurance sector promises immense benefits for all. Insurers can streamline operations and reduce costs, while customers can access tailored services and better offers that meet their unique needs.

Back when I was young and athletic, I was invited to play on a softball team organized by a large law firm in Chicago, where a good friend of mine from college was a first-year associate. When a newbie showed up after a few games, we stashed him in right field, hoping no one would hit the ball to him until we could figure out whether he could play. Alas, someone lashed a one-hopper to him, and the ball hit something in the ground, took a funny bounce and obliterated the poor guy's nose. 

After we got the bleeding to stop and someone volunteered to take him to the emergency room, the young associates indulged in some lawyer humor and wondered whom they should sue.

The batter was clearly liable for hitting the ball so hard, right? But what about the pitcher, who served up such an inviting ball? Quickly, they settled on what everyone agreed was the right answer: They should sue the township that owned the field. After all, it had the deepest pockets, and any jury would root for the poor guy with the shattered nose rather than the nameless, faceless organization that maintained the field.

When I first heard the term "social inflation" and learned how "nuclear" and even "thermonuclear" verdicts were inflating insurers' costs far beyond what was reasonable, I assumed the issue was just another flavor of that conversation on the softball field in Chicago. Insurers have deep pockets and don't have sympathetic faces. (Sorry, Flo.) Individuals who sue insurers can generate enormous sympathy. 

But a presentation at the recent Joint Industry Forum, hosted by the Insurance Information Institute, convinced me that there's much more to the issue than little-guy-versus-big-guy — and that the language we use is part of the reason insurers keep losing.

While insurers have been talking about social inflation for decades, roughly no one outside the industry knows what the term means or finds it terribly offensive. But when you talk about legal system abuse — a term that can be justified in many, many cases — the issue pops into focus. References to the "dark money" financing the surge of lawsuits and to the "billboard attorneys" who pursue them also can grab people by the nose hairs. 

Let's fight back with sharper language.

After the Joint Industry Forum, I chatted with Sean Kevelighan, the CEO of the Triple-I, about the testing they've been doing on what language works. He said the term "social inflation" has been around in the industry since it popped up in a Berkshire Hathaway earnings statement back in the 1970s and had just become accepted. But when the Triple-I began testing alternatives, including "legal fraud," they found that "legal system abuse" resonated. When the Triple-I representatives met with editorial boards in Florida to talk about the problems there, which have been exacerbated by a wild number of lawsuits, they found that "people were repeating the term by the end of the meeting," Sean said.

To reinforce the fact that many of the lawsuits are abusive, not just part of the normal friction between those making claims and those paying them, the Triple-I also tried to figure out how to zero in on third-party litigation funding. 

Sean said investors are viewing insurance lawsuits as an asset class. Investors provide what appear to be billions of dollars to back lawsuits and expect a certain (very high) return on their money. The investors don't care about the merits of the case, and settlements they win don't do much, if anything, for the little guy.

"Most legislators don’t even know about the industry," Sean said. "They’re surprised." 

But "third-party litigation funding" is an ungainly term. What works better? "Dark money." 

So do references to the "billboard attorneys" who are soliciting claimants. If you drive any distance these days, you can't escape the ads that feed into the litigation frenzy. 

The Triple-I is working to connect the legal system abuse, dark money and billboard attorneys to higher premiums for insureds, but the conceptual leap for regulators and state legislators turns out not to be that hard. The surge in lawsuits financed by investors and pursued by attorneys who are only out to make a buck is pretty obviously connected to increased premiums for everyone.

To test the recommended new language, the Triple-I recently took out a billboard in Atlanta and set up related digital advertising. Lo and behold, the billboard company wouldn't let the Triple-I mock billboard attorneys in its ad. The company wouldn't allow a reference to trial attorneys, either, but accepted the claim that legal system abuse raises insurance costs — and digital advertisers at nearby bus stops happily accepted the language about billboard lawyers. Sean said those digital ads had roughly a 5% clickthrough, versus the norm of maybe 1%.

I realize that social inflation... er, legal system abuse... is a highly complicated issue that goes far beyond language, but I focus on the language here both because the import took me by surprise and because I've written and published extensively on the other aspects of the problem.

Back in March, I wrote a Six Things on the growing involvement of private equity in lawsuits against insurers. In it, I cited an article that dramatized the threat of spurious litigation. I also published two highly read articles, here and here, on how plaintiffs' attorneys are winning the battle against industry attorneys when it comes to using AI. I'd also encourage you to check out a deep look at legal system abuse published by the Triple-I. 

Among other things, it reports:

"The Wall Street Journal reports that nearly 800,000 television advertisements for mass tort cases ran in 2023, with costs exceeding $160 million. The article also revealed the average loan directly to law firms fell in the range of $20 million to $100 million, with returns for funders expected to climb as high as 20 percent. Meanwhile, federal civil cases saw a 24 percent increase in filings during 2023, a trend driven by a rise in mass tort lawsuits."

I wish I could offer hope that the excessive litigation against insurers would just fade away. In fact, I was struck by something I wrote back in March. I marveled that a startup, EvenUp, that uses AI to sue insurers had managed to raise money at a $325 million valuation. Well, it raised money again, just six months later. The current valuation: More than $1 billion.

Cheers,

Paul

According to a recent analysis by IDC Financial Insights, AI is expected to generate a cumulative economic impact of $19.9 trillion by 2030, reflecting a compound annual growth rate of 3.5%. Notably, 50% of this impact will be concentrated in North America, while 25% will come from the EMEA region, with the remaining 25% from Asia-Pacific. This distribution largely favors areas that had robust foundational infrastructure at the beginning of the AI revolution.

A crucial takeaway from the IDC report is that AI's economic influence extends beyond direct investments in AI services and solutions. Its disruptive potential is significantly driven by ripple effects throughout the economy. AI affects various sectors along the supply chain, affecting both backward providers of AI solutions (like network infrastructure, hardware, and data storage companies) and forward buyers of AI technology (businesses that integrate AI into their operations to enhance performance).

Additionally, the report highlights "induced effects," where AI influences consumer households, resulting in higher salaries for AI professionals and the emergence of new roles such as AI ethicists, algorithm auditors, and prompt engineers. This rapid adoption of AI technologies is poised to have far-reaching economic consequences, reshaping industries, creating markets, and transforming the competitive landscape.

Since 2023, the insurance industry has entered the digital business era, with generative AI emerging as a key player. While insurers are making substantial investments in generative AI, success rates for deploying this technology vary across different regions. According to a 2024 survey by IDC, nearly all industry professionals anticipate that generative AI will significantly alter competitive dynamics within 18 months, which has heightened the emphasis on integrating this technology throughout the insurance value chain.

Despite this enthusiasm, challenges persist. In 2024, only 68% of the average 24 generative AI proofs of concept met their key performance indicators, and only two were fully integrated into production. This highlights the difficulties organizations face when moving from experimentation to full-scale deployment. Over the past 18 months, insurance CIOs have launched numerous business-led AI initiatives, but these efforts have often resulted in scattered, fragmented, and sometimes redundant applications—a phenomenon IDC refers to as the "GenAI scramble."

Consequently, many insurance carriers have fallen into a productivity trap, focusing on short-sighted value-generation opportunities rather than fostering collaboration or planning for scalability. This approach has limited their ability to create reusable data and models across departments, leading to execution failures.

Underwriters in commercial lines are investigating how generative AI can enhance data submissions for complex risk programs and streamline access to unstructured information. Similarly, claims adjusters are assessing how generative AI can aid in cognitively demanding tasks such as fraud detection and improve claims negotiation strategies to minimize leakage. Compliance experts are also curious about how vendors are using generative AI to alleviate the challenges of regulatory reporting and compliance.

See also: Cautionary Tales on AI

While these initiatives are noteworthy and offer valuable insights for technology leaders to better understand generative AI, they do not fully harness the transformative potential of this technology. To effectively leverage generative AI's capabilities and innovate business models within the industry, a more comprehensive integration and strategic approach are crucial.

Several key factors are preventing insurers from successfully moving AI projects from concept to production:

• High Costs Undermining ROI Goals: The top challenge is the inability to meet return on investment objectives. C-level executives face immense pressure to deliver ROI, and business leaders have little tolerance for generative AI project failures. Investments are scrutinized for tangible business impact. Contributing factors include weak strategies for monetization, superficial feasibility assessments, changing use case requirements during development, and ad hoc deployments that lead to poor infrastructure decisions.
• Shortage of Skilled AI Developers: Finding developers with the right AI expertise remains a challenge. Many organizations struggle to secure talent capable of executing AI projects effectively. 
• Poor IT and Line-of-Business Coordination: AI projects are often viewed as IT responsibilities, with limited accountability from the business side. However, success requires strong collaboration between IT and business units. AI use cases frequently involve cross-departmental data, requiring multiple layers of validation to prevent issues like data toxicity or misalignment.
• Inadequate Infrastructure for Scalability: Organizations often struggle to move from experimental setups to scalable, AI-native infrastructure. Optimized and portable workloads are crucial, but many insurers face difficulties in making this shift. Inadequate architecture increases infrastructure costs, especially in areas like training, tuning, and inference.

Is generative AI just a passing trend? While challenges certainly exist, the preliminary data suggests that underestimating its potential would be a significant miscalculation.

Recent IDC surveys indicate that insurance organizations stand to gain considerable advantages from effectively implementing generative AI. Early adopters in the sector are already seeing marked improvements in operational efficiency, productivity, and profitability—especially those that have advanced their AI maturity and are better equipped to manage business risks. A clear link between digital revenue share and AI maturity underscores the necessity of enhancing digital capabilities to fully leverage these benefits.

To successfully pivot to AI by 2025 and drive meaningful business transformation, insurers should focus on three key areas:

• Develop a Comprehensive AI Strategy: Insurers must prioritize the early integration of generative AI technologies. Appointing an AI orchestrator can facilitate cross-functional collaboration, ensuring efforts are directed toward high-impact use cases. Enhancing customer experience through intuitive, AI-powered digital platforms is essential, along with reimagining business models to foster innovation and strengthen capital management.
• Establish a Unified AI Governance Framework: Maintaining data integrity and alignment with overarching AI strategies is crucial. Insurers should prepare their data for readiness by consolidating systems and standardizing processes to unlock efficiencies. Additionally, addressing talent shortages and regulatory challenges through responsible governance solutions is vital.
• Adhere to the "Buy, Reuse, Build" Principle: Technology investments should focus on cost-effectiveness and operational efficiency. Insurers should first purchase or reuse existing tools before building custom technologies. This approach ensures efficient deployment and optimizes AI-related investments. Leveraging AI for cloud cost optimization and governance through FinOps practices will enhance resource management, ensuring that cloud infrastructure operates efficiently and maximizes returns on IT investments.

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

Insurance decision-makers will need to develop a strategic plan for AI adoption, including how to overcome key obstacles. Those that do will be able to move beyond the current "GenAI scramble" and successfully navigate AI-driven business transformation.

The 2025 edition of IDC's Worldwide Insurance FutureScape is designed to help insurance decision-makers develop a strategic plan for AI adoption. It highlights the critical steps insurers must take over the next five years to move beyond the current "GenAI scramble" and successfully navigate AI-driven business transformation. To learn more about IDC's Worldwide Insurance FutureScape, please click here.

In the fast-paced world of insurance, one thing is becoming increasingly clear — customers expect more. The demand for quicker, more personalized and transparent services has never been higher. The insurance claims process is no exception. To stand out in a competitive market, having a customer-centric claims process is essential.

See also: Research Findings on Customer Retention

Here are the top 10 reasons why a customer-centric approach in your insurance claims process is vital:

1. Enhances Customer Satisfaction

A customer-centric claims process puts the customer's needs and experiences at the forefront, which directly affects satisfaction. A smooth, transparent and efficient claims process reduces frustration and uncertainty for policyholders. By focusing on clear communication and quick resolution, insurers make customers feel valued, which significantly boosts satisfaction and long-term loyalty. Satisfied customers are more likely to renew their policies and recommend services to others.

2. Reduces Claim Resolution Time

Speed is critical in the insurance sector. Policyholders filing claims are often dealing with stressful situations like property damage or medical issues. A customer-focused claims process ensures that claims are handled as quickly as possible, from the initial report to the final settlement. Using digital tools such as online portals, automated workflows and real-time updates helps expedite the process. The faster the resolution, the more satisfied the customer.

3. Builds Trust and Transparency

Transparency is key to building trust. A customer-centric insurance claims process offers real-time visibility into the status of the claim, ensuring that the customer knows what is happening at each stage. This level of transparency not only helps customers feel in control but also builds trust. When customers feel that they are being kept in the loop, they are more likely to feel confident in the services and remain loyal to the brand.

4. Improves Operational Efficiency

By placing the customer at the heart of the claims process, insurers are driven to improve operational efficiency. A customer-first approach encourages the adoption of technology such as artificial intelligence, machine learning and predictive analytics to streamline workflows. This leads to faster claim assessments, reduced paperwork and fewer errors, benefiting both the insurer and the customer. Efficient operations lead to a seamless experience, which ultimately results in higher customer retention rates.

5. Boosts Brand Reputation

In the age of social media and online reviews, customer experiences — good or bad — can significantly affect a brand's reputation. A customer-centric claims process ensures that policyholders have positive experiences that they are more likely to share publicly. A reputation for providing excellent customer service can lead to organic growth through word-of-mouth referrals and online recommendations. Customers who feel well-treated during the claims process will spread the word, and this positive buzz can greatly enhance a brand's visibility and appeal.

6. Encourages Customer Loyalty

The insurance industry is built on trust and long-term relationships. A customer-centric approach to claims processing can help build these relationships. When customers feel valued and supported during their time of need, they are more likely to stay with the company in the long run. Customer retention is critical in the insurance industry because acquiring new customers can be significantly more expensive than retaining existing ones. A loyal customer base leads to higher lifetime value and repeat business.

See also: 10 Reasons to Stress Customer Retention

7. Minimizes Disputes and Complaints

A transparent, customer-focused claims process helps minimize disputes and complaints. By keeping communication clear and expectations aligned, there are fewer misunderstandings. The availability of detailed information on how claims are handled can reduce the number of escalated cases, improving overall customer relations. This not only enhances the customer experience but also reduces the burden on the claims team, allowing them to focus on more critical cases.

8. Drives Innovation Through Feedback

A customer-centric claims process creates a feedback loop where customer insights can be used to improve services. Listening to customer feedback about their experience with the claims process can help identify areas for improvement. These insights drive innovation, enabling the introduction of new features, tools and services that align with the needs of policyholders. Continuous improvement based on customer feedback will set an insurance company apart from competitors.

9. Aligns With Digital Transformation

As the insurance industry continues to undergo digital transformation, a customer-first approach to claims processing aligns with the growing demand for online services and self-service options. Customers now expect to manage claims through mobile apps, receive updates via SMS or email and access their claim status at any time. A digitally enhanced, customer-centric process ensures that a business stays relevant in a tech-driven world while also meeting the evolving needs of modern customers.

10. Reduces Overall Costs

A customer-centric insurance claims process can reduce costs in the long run. By adopting digital tools, automating tasks and reducing errors, insurers can lower the operational costs associated with manual claims handling. Additionally, a smoother, quicker claims process decreases the chances of legal disputes and escalations, further saving costs. While it may require an initial investment in technology and training, the long-term savings and increased customer satisfaction make it worthwhile.

Incorporating a customer-centric approach into the insurance claims process is no longer optional — it's a necessity. A customer-first strategy enhances satisfaction, boosts operational efficiency, builds trust and reduces disputes. It also aligns with digital transformation trends and positions a business for long-term success. By prioritizing the needs and expectations of policyholders, insurers create a smoother, more efficient claims process that benefits both the customer and the organization.

In the modern insurance industry, artificial intelligence (AI) and data analytics have revolutionized how insurers assess risk, price policies and deliver customer service. This article delves into the role these cutting-edge technologies play in commercial insurance, shaping a more efficient, accurate and customer-centric industry.

1. AI and Data Analytics in Risk Assessment

Risk assessment is the backbone of the commercial insurance industry. Traditionally, insurers relied on historical data, underwriting expertise and manual processes to assess risk, which often led to inaccurate pricing and inefficient processes. However, with the integration of AI and data analytics, insurers can now analyze vast volumes of real-time data, including social, environmental and economic factors, to make more informed decisions.

AI for Predictive Risk Modeling

AI has enabled insurers to move beyond historical data by incorporating predictive risk models. These models leverage machine learning algorithms to predict future outcomes based on patterns in data. For instance, AI can predict potential claims by analyzing trends in customer behavior, geographic risk factors and industry-specific risks. This leads to more accurate pricing and enables insurers to offer personalized policies that reflect the true risk profile of their clients.

See also: A Data Strategy for Successful AI Adoption

2. Enhancing Claims Processing with AI

Claims processing has historically been one of the most time-consuming and costly aspects of insurance operations. AI and data analytics are changing this by automating much of the claims process, reducing the need for manual intervention and improving the customer experience.

AI-Powered Automation in Claims Handling

AI-powered tools like natural language processing (NLP) and computer vision are transforming claims handling by automating document analysis, damage assessments and even fraud detection. NLP enables AI systems to interpret unstructured data such as claims forms, emails and customer interactions, while computer vision can analyze images to assess physical damage in auto accidents, property damage or workplace injuries.

By implementing these technologies, insurers can process claims faster, improve accuracy and mitigate fraud, leading to cost savings and higher customer satisfaction.

3. Fraud Detection and Prevention

Fraud remains a significant issue for the commercial insurance industry, leading to billions of dollars in losses annually. Traditionally, fraud detection involved manual investigation, which was often time-consuming and prone to human error. AI and data analytics have vastly improved fraud detection by enabling insurers to analyze patterns in data and identify anomalies that may indicate fraudulent behavior.

AI and Machine Learning for Fraud Detection

AI systems can be trained to detect suspicious patterns by analyzing various factors such as claim amounts, frequency of claims and customer behavior. These algorithms can flag potentially fraudulent claims for further investigation, significantly improving the speed and accuracy of fraud detection.

Furthermore, insurers can deploy predictive analytics to anticipate fraudulent activities before they occur by examining large datasets, which include customer profiles, market trends and even social media data. This preemptive approach helps minimize losses and keeps premiums lower for legitimate policyholders.

4. AI in Underwriting

Underwriting has traditionally been a highly manual and labor-intensive process that involves reviewing large volumes of data to assess risk and determine policy terms. AI has the potential to dramatically improve underwriting by automating data collection, analysis and decision-making.

Smart Underwriting With AI

With AI, underwriters can access a broader range of data sources, including real-time information from Internet of Things (IoT) devices, satellite imagery and customer digital footprints. AI systems analyze this data to provide underwriting recommendations, allowing insurers to make faster and more accurate decisions.

AI-driven underwriting systems also enhance efficiency by reducing the time it takes to analyze risks and issue policies. This enables insurers to handle a larger volume of applications while maintaining high accuracy and customization in policy offerings.

See also: Cautionary Tales on AI

5. Data Analytics for Better Customer Insights

Understanding customer needs and behavior is crucial for any insurance provider, and data analytics offers unparalleled insight into the preferences and habits of commercial clients. By harnessing data analytics, insurers can create more tailored insurance products and improve customer engagement.

Personalized Customer Experience

AI-driven analytics platforms allow insurers to segment customers into specific risk categories and deliver personalized policy options based on their unique profiles. For instance, companies in high-risk industries can receive customized packages that address their specific needs, while lower-risk clients might enjoy discounted premiums for lower liability exposure.

In addition, data analytics helps insurers predict customer churn, enabling them to take steps to improve customer retention. By identifying factors that lead to dissatisfaction or policy lapses, insurers can offer timely solutions to prevent customers from switching providers.

6. AI and Data Analytics for Compliance and Regulatory Adherence

The commercial insurance sector is heavily regulated, and insurers must comply with a wide range of legal requirements. AI and data analytics help insurers stay compliant by automating the monitoring and reporting of regulatory obligations.

RegTech: Streamlining Compliance

AI-powered RegTech solutions provide real-time insights into regulatory requirements and ensure that insurers remain in compliance with local, state and federal laws. These systems can also automatically generate compliance reports and flag potential violations before they escalate into costly fines or legal actions.

By ensuring compliance through AI-driven systems, insurers can reduce operational costs associated with regulatory adherence and focus more on business growth and customer satisfaction.

7. The Future of AI and Data Analytics in Commercial Insurance

The future of AI and data analytics in commercial insurance looks incredibly promising. As AI technology continues to evolve, we expect to see more sophisticated risk models, faster claims processing and enhanced fraud detection systems that adapt to emerging trends.

AI-Powered Innovation for Tomorrow's Insurers

Future advancements in AI and data analytics will enable insurers to improve risk management, offering more dynamic pricing models based on real-time data, and leveraging AI to predict emerging risks from climate change, cybersecurity threats and evolving global markets.

In this new landscape, insurers that invest in AI and data analytics will be better positioned to offer competitive advantages to their clients, delivering policies that are tailored, cost-effective and aligned with the dynamic needs of the commercial insurance market.

Paul Carroll

What's the current state of play with generative AI? It's been two years since ChatGPT launched, and while there's been a lot of publicity, I wonder how much are people actually using it versus just talking about it.

Fady Khayatt

Generative AI has certainly grabbed headlines regarding the possibilities in those two years. Over the last year or so, the insurance industry has been working to understand generative AI's potential, separating hype from reality, identifying use cases, and starting to test solutions. This has primarily involved relatively small-scale, cautious pilots.

There's been an awareness about not diving head-first into massive investments, with some tentativeness about what this technology will really deliver and what the return on investment will be. Some players have been burned by previous promises around technologies like blockchain, where there was a lot of hype, potential overinvestment, and no real delivery. This caution has meant that much of the experimentation and evolution has been more iterative than transformational.

Rather than true experimentation, the work with generative AI has been focused on deploying tools and copilots from providers into current processes, instead of considering how we can use this technology to transform our processes or make a step change in how we interact with customers, distributors, and internal stakeholders. This has somewhat limited the impact of these pilots and the identification of generative AI's real potential.

Paul Carroll

At this point, it seems we can divide the use of generative AI into two buckets in the insurance industry. One is becoming more efficient, and the other is actually using AI to make decisions. From what I've heard, the uses are more about AI gathering documents for agents, claims adjusters, and underwriters, rather than gaining insights for more effective underwriting. Does that match what you're seeing?

Fady Khayatt

The use cases we've identified span both categories. There is recognition that generative AI can improve both efficiency and decision-making. However, the latter is very hard and requires a more transformative change than just providing access to generative AI tools. It requires bigger changes to processes, governance, and structures.

The pilots that have been run and systems deployed have tended to focus more on the efficiency side. There's recognition across the market about the risks of this approach. At Oliver Wyman, we conducted a survey earlier this year of CEOs from companies listed on the New York Stock Exchange, and around 40% expressed concern about not moving fast enough. 

Giving everyone access to tools like Microsoft Office Suites with Copilot, or other AI modules integrated within existing systems, has been helpful and driven significant usage, including internally at our organization. However, the tools are primarily making existing processes easier rather than changing how people make decisions or improving the quality of those decisions.

There's been much discussion about usage within underwriting and claims, particularly regarding changing the balance between art and science in complex underwriting and claims handling.

Paul Carroll

Are there any standout examples you've seen that others should try to emulate?

Fady Khayatt

In terms of efficiency, as we see both in our surveys and in our conversations, Gen AI has a huge impact on code and software development. That's probably the leading area of deployment and has driven a lot of efficiency. But those processes where it's been easy and quick to deploy are not really core to insurance industry priorities or needs. 

We do see some examples of insurance companies deploying Gen AI into their claims process in terms of collecting unstructured data across handwritten claims files or hundreds of claims files that have been stored but are difficult for a claims adviser to go through. We are starting to see insurers using Gen AI to go through their archives and identify what critical items determined the outcome. We are starting to see some of that deployment, but it's relatively limited.

Paul Carroll

What do you recommend to folks you talk to about what they ought to be doing now and where they ought to be trying to get over the next year or two?

Fady Khayatt

I think there's a question about identifying where internal development of Gen AI is worth focusing on and worth investigating. There are areas where Gen AI will have a big impact but where it's not necessarily right for insurance companies to lead the charge. Gen AI is going to be very relevant to areas in the value chain around IT, HR, and legal and compliance. However, these aren't necessarily areas where insurers want to be developing their own proprietary solutions — there may be industry solutions they can deploy instead.

The key is identifying areas that will create a distinct competitive advantage if insurers take the lead. This will be different for different players depending on their areas of focus and strategic priorities. 

The second point is ensuring alignment with broader transformation objectives within the business. What we've seen so far is some Gen AI experimentation that's disconnected from broader change programs. You'll get more traction by integrating Gen AI thinking into existing transformation goals, whether that's developing a new line of business around energy transition or cyber, or upgrading the underwriting workbench.

The third element is making sure that the focus is on both top line and bottom line. There's been a lot of focus on efficiency, but we need to understand these opportunities from both a growth perspective and an efficiency perspective. Otherwise, you're looking through too narrow a lens.

Finally, consider whether the capability you're going to deploy is specific to your organization. You need to assess whether to build it yourself, work with a partner, or adopt an industry standard solution. Not everything needs to be built in-house, and not everything should simply be taken off the shelf. There's a lot in between, and being open to all of those options is important.

Paul Carroll

You bring up an interesting point about efficiency. I've heard people discuss the importance of triage processes within insurers and whether AI can support this sort of decision making. How important is this in your view?

Fady Khayatt

I think AI can play an important role in triage. Often, when we talk about triage, it's about underwriting or claims triage in terms of simple cases versus complex cases. You could deploy generative AI to help with these processes, but beyond that there's potential for generative AI to also support case underwriters or claims handlers in dealing with complex cases and claims.

However, there are other parts of the value chain where AI can help with decision making, with similar benefits. For example, in the sales process, this depends on markets and products, but identifying the next best action or next best product for a certain customer is another opportunity. AI could also be deployed in retention processes — if a customer calls to cancel their policy, there are opportunities there. 

Beyond that, there's generating content and personalization of marketing that's going to be sent to the client.

So I think you can see Gen AI being deployed across many different parts of the chain. For example, in the U.K., for a personal lines player, the retention process is critical and can be very differentiating in terms of outcomes. Choosing to invest there rather than in the front end of distribution makes sense in a market dominated by price comparison websites. 

Depending on the market and line of business, there are opportunities in lots of places, but the critical opportunity for that business model in that market will be different.

Paul Carroll

Looking ahead, what are your thoughts on how things might change over the next few years, particularly regarding “agentic AI”? I'm personally concerned about AI systems being set up as agents that can take autonomous actions on our behalf.

Fady Khayatt

My kids often say I’m like “an old man shouting at the wind” because they think I underestimate the impact of these technologies. But in a heavily regulated market like insurance, there's going to be significant caution before implementing tools that execute multistep processes or make autonomous decisions. When you consider the level of regulation around ensuring fair customer treatment, and proper capital adequacy, deployment of fully autonomous systems will take time.

We've already seen this with generative AI chatbots and voice services. In Europe, particularly, we're very cautious about deploying anything that touches the customer because it's not just about reputational and brand risk — there's also regulatory risk to consider. I believe this cautious approach to deployment will continue within the insurance industry.

Paul Carroll

Makes sense. Anything else either in terms of the future or just anything I didn't ask you about that you wanted to touch on?

Fady Khayatt

I think the key point I'd touch on is how to move to a more transformative approach with generative AI rather than an incremental one. We've talked about aligning with broader strategy and being clear on impact areas that matter for business differentiation. But there's another crucial aspect we haven't discussed: actually making transformation happen. There needs to be a business-led change rather than a technology-led change. If generative AI is really going to fulfill its promise, it has to change how key people in the business work and fundamentally change those processes.

This transformation needs to start not from what generative AI can do or what's available to integrate into existing processes. Instead, it should begin with identifying where the current processes  are broken. Can we reimagine how we'd like that process to work ideally? Once we've done that, we can think through whether generative AI can help us get there. There might not be anything off the shelf that allows us to achieve this, but the technology would enable us to get there.

Then you can work with IT teams and vendors to develop something that delivers this transformed process, rather than just taking existing tools and slotting them into our current process. The impetus has to come from the business or really engage the business, which I don't think happens enough today.

Paul Carroll

I just came from a conference where an old colleague of mine made this point on a panel with CEOs of big insurance companies. They were talking about what the technology could do, but Andrew said, "Forget the technology. This has to be driven by business people." They were taken aback, but it was a good point to make.

Fady Khayatt

It has to start with the business saying, "We could do this better." Whether that's customer interaction, underwriting, or financing, we could do these things better. We could see that if we could extract more from our data, improve our interactions, or change processes fundamentally, it would transform both how we present to the market and our operational efficiency. Now let's see if we can either co-construct or find solutions out there that allow us to do it. And I don't think there's enough of that thinking going on.

Paul Carroll

Thanks, Fady. This is great. 

About Fady Khayatt

Fady Khayatt is a partner at Oliver Wyman in the European Insurance and Asset management business. Based in the London office, Fady has worked around the world for insurance businesses covering strategy, operations, governance, risk, digital transformation, and capital management.

The life insurance and annuities (L&A) industry stands on the precipice of a massive generational wealth transfer, so the millennial market represents an unprecedented opportunity for growth. But the insurance industry — burdened by legacy technology and human intensive and inefficient processes — has traditionally been ill-equipped to capture this burgeoning market.

Rather than viewing annuities as products that are simply “sold, not bought,” the industry must focus on evolving to meet the needs of today’s consumers. Historically, a lack of strong education and transparency around these products has required agents to serve as the primary source of information. However, as we adopt more technology-driven, self-serve solutions — similar to those in banking and wealth management — we can empower consumers with the tools they need to make informed decisions independently.

To get there, we must work toward the development of industry best practices and standards that enable more interoperability across the L&A value chain. This shift is not about incremental improvements, but about fundamental transformation — enabling carriers to launch products faster, streamline operations, and deliver superior customer experiences.

Millennial market potential 

As millennials continue into their peak earning years and prepare to inherit an estimated $90 trillion over the next two decades, their need for an enhanced level of financial security and stability will only increase. A recent Zinnia survey found that while only 9% of Americans own an annuity, 61% of millennial consumers (and 69% of Gen Z) said they were likely to consider one as part of their retirement savings plan, and 93% of millennials said a guaranteed monthly income in retirement was “important” for them. 

Having grown up immersed in digital technology, millennials have been trained to have higher expectations for how technology can help them in their day-to-day lives — including financial transactions. An example of this is the digitization of banking, retail brokerage and wealth management platforms, taxes, and many other technology-driven innovations that improve user productivity and ultimately lead to higher product satisfaction.

By developing predictable, repeatable, and scalable solutions and user-friendly platforms, the L&A industry will be better equipped to capitalize on growth opportunities while making the products better for the consumer.

See also: Revolutionizing Life Insurance Uptake in Younger Markets

Overcoming legacy systems for a customer-centric approach

When I joined Zinnia in 2020, it was not uncommon for carriers to spend 12 to 18 months bringing a product to market — an eternity in today's rapidly evolving financial landscape. 

This lengthy product launch cycle was a symptom of a larger problem: the industry's heavy reliance on bespoke solutions that served individual carriers but failed to address the need for interoperability and standardization across the industry. By investing heavily in emerging technologies, modern data infrastructure, and scalable platforms, carriers can significantly reduce product development timelines. That means being able to react to market trends and offer up products that potential customers are looking for.

Adopting a digital-first approach is good for consumers, too. The Zinnia survey also found that while 82% of annuity holders said it was important for them to be able to manage their financial accounts on a website or mobile app, less than half (49%) were satisfied with the digital tools used to manage their annuity accounts. 

This underscores the importance of insurers taking a truly consumer-centric approach — one that seamlessly integrates insurance and annuities into consumers’ broader financial strategies.  

Unlocking efficiency through automation and digital innovation

Embracing advanced technologies such as AI, blockchain, and smart contract technology has the potential to transform the L&A industry. These tools not only improve operational efficiency but also enhance customer service by providing customers with more personalized experience and greater transparency.

Blockchain and smart contracts can also play a pivotal role in the L&A space by enhancing the security of transactions and simplifying complex processes. Zahara, Zinnia's cloud-based system of record, provides a single source of truth for policy data and leverages real-time processing to accelerate transactions, improve data accuracy, and create a more transparent and auditable process. 

These automations have already had a significant impact on operational efficiency. In January 2023, just 7% of electronic annuity applications were automated, and it took around 30 days to issue a contract to an applicant, based on Zinnia’s internal data. As of October 2024, around 50% of these transactions are automated, from agent and app validation to payment and transfer processing, while the average time to issue contracts has been reduced to just six days — a monumental improvement over 20 months earlier.

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

Transforming the L&A industry for the present and the future

The future of the L&A industry lies in reshaping age-old systems and processes and ultimately putting the customer first. The opportunity to address consumer expectations, lay the foundation for long-lasting relationships, and capture market share is no longer just a pipe dream — it’s right there for the taking.