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. 

With the inception of the cell phone, and the steady adoption of smartphones in the decades since, we’ve seen a revolution in customers' expectations and how they interact with brands. With the world in their pocket, the customer expects their experience to be both digitized and easy. 

This expectation isn’t confined to one industry. It has transformed the way nearly all businesses interact with their customers and has created a competitive, fast-paced digital marketplace. 

The insurance industry has not been immune to this upending of traditional processes. To not only survive but excel in this evolving digital landscape, it’s become imperative for companies to leverage the right technology and resources to address the consumer's pain points and create the most effective and convenient experiences possible. 

Digital adoption became a fast priority for the auto insurance industry, where digital channels bring in more auto insurance customers than agents and call centers, according to J.D. Power. In fact, more than half (53%) of first-time customers begin their auto insurance experience via digital channels, and 42% of policyholders who switch insurers and 46% of retained customers list digital as their preferred choice. 

That same study also finds that satisfaction rates are highest when customers have digital-only interactions with their insurer. 

See also: Can AI Serve as a Concierge?

Creating Digital Touch Points 

With proof that policyholders prefer digital-first experiences, companies like ours must continually ask, “Where are the touch points the customer wants along the journey?” 

At the highest level, our customers want the claims process to be fast and efficient. A majority of our policyholders prefer this happen through self-service options, like our app. Let’s call this type of customer “Pool A.” But there’s still that other pool of our customers, “Pool B,” that value human interaction within their insurance experience. So now, we have the challenge – or the opportunity if you’re a “glass-half-full” kind of person – of striking the balance between these two types of customers so we are accommodating their needs and exceeding their expectations. 

To achieve this balance, we’re targeting significant, long-term technological implementations. As our tech continues to advance and evolve, the “bots” servicing our Pool A customers will become more sophisticated. As this trend continues, we’ll see our Pool B customers organically acclimate to this service method and lean more into digital interactions to resolve their queries. 

Leaning Into CX ‘Superpowers’ 

You may be wondering where that leaves customer service leaders like my team and me. What we have found is that these experts are exactly who you need when designing digital experiences and re-thinking traditional solutions. Customer service experts understand that the collective CX superpower is “active listening,” allowing us to tackle highly nuanced or complex interactions with ease. We know what customers need and expect, and how to deliver in the digital landscape. We are also adept at leveraging the operational expertise of our leadership team, which is key to scaling a digital-first operation. Working with our technology partners, we've been able to balance both worlds, trust our customers, and open new avenues for accomplishing our business goals. 

Implementing AI-powered Solutions. 

According to studies, ChatGPT reached 100 million users in just two months. By comparison, it took 16 years for the cell phone and seven years for the World Wide Web to achieve that same user adoption. Clearly, it’s not a matter of “if,” companies will implement AI solutions to supplement their customer journey, but “when.” 

Our customers now have access to things they did not before thanks to our new Gen AI solutions, such as 24/7 support and more options to self-serve via calls or online interactions. This ensures that our policyholders receive prompt and efficient service while allowing our customer service team to focus on different, more complex responsibilities. Since we rolled out this chat product in April, over 40% of our customer-facing chats no longer require human interaction. 

Our customer service team was also vital in the creation and development of another Gen AI solution, which streamlines our claims process even further by using large language models (LLMs) immediately following the submission of a claim (first notice of loss, or FNOL). This has been beneficial to both the customer and our CX team, as it results in fewer back-and-forth conversations and quicker resolutions.

Three things have changed for us since launching our Gen AI solutions: 

1. Our knowledge base process has evolved. Our content sources look different than they did when solely designed for our human employees. They are tailored for the bot to easily source from, so they are shorter and easier to digest. 

2. We are rethinking solutions to customer problems. Instead of applying the usual tools to improve efficiency, we can now better support our customers through the creative use of new technologies and pathways that we’re developing and integrating to meet their needs. 

3. Our early containment/resolution rate surpasses most in the industry. The insurance industry has been slower to achieve high levels of contact containment to date, but generative AI is changing the norm. 

See also: A Reality Check for Generative AI

Empowering and Complementing Our Human Workforce 

By appropriately leveraging these tools to streamline processes, we’re empowering our human experts to focus on the nuances of each case to prioritize delivering empathetic, effective experiences. 

The truth? AI-based machines are fast, accurate, and consistently becoming more and more rational. But they aren’t as intuitive, emotional, or culturally sensitive as humans. 

These human characteristics will always be staples of exemplary customer service, and when that is coupled with best-in-class technology, you’re only enhancing that experience. The narrative about the future of work, in CX and otherwise, should shift from AI “replacing” us to AI “enhancing” our roles to the benefit of everyone involved– especially our customers.

Millions of people enroll in diverse coverage policies daily to safeguard their futures, causing the insurance sector to evolve rapidly. To optimize their benefits, customers are soliciting an increasing number of individualized, real-time interactions with insurers.

Even as automation within the insurance sector has been sluggish and has often failed to stay at par with customer expectancies, innovation is now critical to preserving a competitive edge. Insurance organizations need to pick out high-quantity methods for robotic system automation (RPA), artificial intelligence (AI), and machine learning (ML).

Insurance industry leaders are greatly increasing workflow performance by using RPA to automate time-consuming operations like data access, document maintenance, and transaction administration. 

The Value of RPA in Insurance

RPA automates routine business tasks that burn up personnel's time, energy, and morale. According to case research, the ROI of RPA implementation in economic services can be as much as 200% within the first 12 months.

Insurance underwriting software can also increase accuracy and efficiency by using RPA bots throughout numerous platforms. Insurance underwriting software uses various systems and packages. RPA can help link those structures without requiring code so insurers can conduct operations faster, decrease labor costs, and discover new areas of innovation. 

How RPA Works in the Insurance Sector

RPA integrates with insurance workflows by interacting with legacy systems to automate repetitive responsibilities. It can automate procedures across multiple departments without needing system overhauls.

Example: RPA bots can extract data from coverage paperwork, update legacy systems, and interact with web applications without requiring adjustments to the underlying structures.

Some key tasks that RPA automates in insurance underwriting software are:

• Data extraction and entry: Bots can retrieve and enter information from purchaser statistics or policy applications.
• Claims processing: From declaration initiation to the agreement, RPA speeds the process by automating documentation and evaluation tasks.
• Customer facts control: RPA guarantees up-to-date and correct customer information by synchronizing information across special systems.
• Policy control: RPA handles responsibilities like policy renewals, settlement generation, and file preservation with minimal human intervention.

See also: Why Hasn't Insurance Automated More?

Comparing RPA With AI: Suitable Technologies

AI evaluates data, generates predictions, and provides insights, while RPA excels at automating repetitive, rule-based operations. Together, these technologies form an ecosystem of intelligent automation:

• RPA automates processes like process management and data extraction.
• Predictive analysis and trend detection using aggregated data are applications of AI.
• For instance, RPA can gather information from client interactions, which AI can then use to forecast consumer behavior or identify fraudulent underwriting activity.

Principal Advantages of Insurance Robotic Process Automation

A. Increasing Business Scalability

Insurance organizations can scale operations more effectively because RPA reduces the need for manual involvement in repeated activities. For example, insurers can use RPA to manage a spike in claims, enhancing operational effectiveness and customer service following natural catastrophes.

B. Lowering the Cost of Operations

By automating routine responsibilities, RPA speeds operations and decreases the need for massive manual workforces. 

C. Mitigating Human Error

Human errors in data access or claims processing can result in monetary losses and non-compliance troubles. RPA eliminates such errors by automating records entry tasks and ensuring information consistency. This reduces risk and enhances regulatory compliance.

D. Improving Customer Satisfaction

RPA lets insurers deliver quicker services, reducing consumer churn and enhancing loyalty. For example, RPA can shorten claims processing times from days to hours.

E. Creating the Foundation for Analytics

RPA is essential for collecting, standardizing, and aggregating data for further analysis by AI-driven systems. By automating data activities, insurance companies can concentrate on extracting relevant insights from the data instead of laboriously preparing it.

Case Study: Revolutionizing Premium Advice Processing With RPA

Client: A leading insurance company

Challenge:

The insurer used a time-consuming method for handling premium recommendation notes. On average, the company took two days to manually enter 500 pieces of premium information. This procedure involved numerous steps and was susceptible to delays and mistakes due to incomplete or unstructured data.

Process Overview:

Insurance brokers submitted enterprise transactions, which were controlled through a relevant repository. An operator had to manually structure the data to fill in each top-rate recommendation note, ensuring they were in standardized form. Often, records were incomplete, requiring the operator to either contact the dealer for missing data or retrieve it from the enterprise's back-office systems. Once completed, the note might be signed off and saved inside the repository.

RPA Implementation:

The employer realized that most of the repetitive obligations in this method could be computerized, although not all aspects were appropriate for RPA. Human intervention was important for obligations, including structuring unformatted information and dealing with exceptions. An RPA engineer concentrated on responsibilities like information population, record submission, and retrieval of office records.

Automation initially managed as much as 90% of the requests, leaving the most complicated exceptions to human operators. Over time, RPA took on more responsibilities, reducing the manual workload.

See also: Balancing Technology and Empathy in Claims

Results:

Implementing RPA modified the company's processing of top-class recommendations. Processing 500 premium advocate notes took barely 30 minutes, in comparison to two days. The insurer could reallocate individuals to higher-price obligations, accelerating overall productivity and operational agility.

Important lessons learned:

• RPA considerably shortened the time spent dealing with files and manually entering statistics.
• Up to 95% of the process is completed by automation,

This case shows how thoughtful RPA implementation may reduce expenses and save time by transforming labor-intensive coverage commercial enterprise approaches.

RPA Uses

Insurance businesses are already using RPA to enhance quite a few data processing tasks:

A. Claims control: From the First Notice of Loss (FNOL) through adjustment and agreement, RPA bots can expedite the entire claims system. Insurance agencies can automate their high-volume claims submitting procedures. Ordinary disputes are resolved in a count of minutes, permitting staff to concentrate on more vital areas for the enterprise.

B. Underwriting: RPA bots can mechanically accumulate unstructured information from internal and external resources and show it on a unified dashboard, for quicker decisions.

C. Policy Management

From contract generation to policy renewals, RPA automates repetitive duties, ensuring seamless policy documentation and update management.

D. Regulatory Compliance

RPA ensures that information entry is correct and that reports are generated in compliance with regulatory requirements. This minimizes the risk of non-compliance.

E. Sales and Distribution

RPA streamlines sales tactics and reporting for insurance marketers. It automates routine checks and compliance verifications, permitting sellers to focus on clients.

F. Analytics for Business and Processes

RPA facilitates information preparation for evaluation by automating data aggregation, enabling insights.

G. Assistance to Customers

Chatbots with RPA competencies can answer client questions immediately. 

H. Legacy System Management

By automating duties, RPA increases the operating life of legacy systems and lessens the need for pricey system replacements.

Summary

Robotic procedure automation lowers expenses, streamlines repetitive operations, and increases customer satisfaction. RPA presents insurers with an adaptable, scalable, and inexpensive way to improve everything from underwriting to claims management, setting the stage for continuing advances in AI.

Cyber criminals are always looking for new methods to gain access to funds and critical information. That’s largely because organizations have caught on to the phishing schemes and sneaky ransomware attacks of the past and have implemented tools and educated employees to keep cyber thieves at bay.

But with the rapid evolution of artificial intelligence, especially generative AI, and public demand for easy access to the new technology, cyber crime is primed to make a big leap forward.

Deepfakes are the latest weapons in the cyber criminals’ arsenal. There has been a notable rise in the use of deepfakes to commit cyber crime, and the only things slowing the progress are the sophistication, expertise and effort required to effectively use the technology.

But as the technology becomes more accessible and more user-friendly, deepfakes will likely play a big role in the future of cyber crime. Combatting this emerging threat will require staying one step ahead of the criminals through the use of technology and strict protocols to block access to funds or critical information and, most importantly, educating employees about how to spot a deepfake.

See also: The Evolving Landscape of Cybersecurity

Why are deepfakes so effective?

Deepfakes, as we know them today, are videos, images or audio that are created to look realistic with the use of artificial intelligence. In a broader sense, deepfake technology has been around for decades. Early motion pictures manipulated images long before computers and AI were available. And fake audio recordings are easy to create even without modern technology – a good celebrity impersonator can be just as effective.

Deepfake technology has been widely employed in politics. Official campaigns typically avoid using such tactics, but a candidate’s supporters have been known to create unflattering and realistic images of political opponents to share on social media.

The technology has some legitimate uses, particularly in the world of cinema. The motion picture, “Forrest Gump,” could not have been made without the blending of Tom Hanks’ main character with historical footage. More recently, AI technology was used to fascinating and realistic effect in the Netflix documentary, “Dirty Pop,” about boy-band impresario Lou Pearlman. The producers combined dialogue from Pearlman’s memoir with a video of him speaking to the camera and a “mouth actor” who moved his lips to match the words, using some AI effects to bring it all together. If the program didn’t warned viewers beforehand that it used AI-generated trickery to make Pearlman a narrator of his own documentary, it would be hard to tell.

Deepfakes can be an effective tool for cyber crime because of social engineering, which is the psychological manipulation of people into performing actions or divulging confidential information, such as passwords or access to financial accounts. Social engineering is often one of many steps in a more complex fraud scheme.

If you’ve ever received a realistic-looking email that appears to be from your bank or cable TV company but is actually from an unfamiliar email account, that’s a scam using the concept of social engineering. Deepfakes are similar to a fake email scam, but taken to a new, more sophisticated level.

Most cyberattacks that employ social engineering play on the victim’s emotions and create a sense of urgency, because the cyber criminals want to put the victim in an emotional state and get them to make a decision quickly before they have time to think about it critically.

See also: The Future of Insurance Fraud

Deepfake detection and protection

As quickly as deepfake technology has evolved, so have the methods to detect when an image, video or audio file is an AI-generated fake. There are several software tools available that can help detect a fake video. It’s like using AI for good against those who would use it to commit crimes.

Aside from letting AI do all the detective work, humans well trained in identifying deepfakes can uncover the truth by simply analyzing the quality and consistency of the video or image. Distortions, blurriness and mismatched colors or objects can raise suspicions. Also, look for unusual behavior in those speaking in the video, like awkward motions, unnatural positions and lip movement that doesn’t sync with the words being heard. Finally, it’s critical to verify the source and the origin of the video or image.

To protect your organization against deepfake cyber threats, continue to follow the same tried-and-true cybersecurity protocols you have in place. Deepfakes by themselves are not a security threat. But they can be a means to an end for nefarious types to get past security protocols. Deepfakes, therefore, are really a variation of an existing threat that can make social engineering scams harder to detect.

The human element continues to be one of the biggest dangers organizations face when it comes to cybersecurity.

Organizations should continue to use multi-factor authentication (MFA), an electronic authentication method in which a user is granted access to a website or application only after successfully presenting two or more pieces of evidence to an authentication mechanism. Think of signing on to a website with a login, a password and, finally, a six-digit code that was sent as a text to your smart phone.

As new threats appear on the horizon, such as deepfakes, organizations should continually review and update employee cyber training.

Deepfakes require a level of sophistication, training and effort that most cybercriminals have not yet mastered, but they are a real and emerging risk. Employees should be trained now about how to identify a deepfake threat and protect critical information from cybercriminals.