There was a day when life insurance agents sat at a customer’s kitchen table and got to know them personally. Sure, a policy was agreed to and sold, but the bond between the agent and the customer was at the heart of the deal.  As the saying goes, insurance isn’t bought – it is sold. 

Today, technology is slowly replacing the human touch, with AI-enabled bots answering phones, responding to chat inquiries, and pushing products to customers over every available communications channel. The industry has, by and large, moved away from the people business and closer to transaction management.

AI solutions abound that sift customer purchase data, social sentiments, and risk profiles to offer fitting products that improve loss ratios and enhance margins. But these solutions are available to all insurers.

See also: How AI Is Changing Insurance

What can an insurance firm do to substantially differentiate from the competition? What can a firm do to cement relationships and turn customers into raving fans?

The answer is to provide customers with a more meaningful human experience. We are all born for human connection. Robots are incapable of providing it. So, what shall we say of AI in light of this reality?

AI can greatly support the promise of personal service. Bots can provide a frictionless experience, elevating and accelerating customers; journey from shopping to buying to ultimately becoming partners on a life insurance journey that covers myriad life events.

AI can liberate team members for more one-on-one, personal interactions. It can eliminate the repetitive tasks that dehumanize employees and free customers from having to input personal information again and again as they shuffle from one contact to the next. It can heighten the experience by leveraging massive stores of data to serve up poignant insights, at the exact moment of truth, when it matters most.

“I hear you’re struggling to make it to rehab, Ms. Jones, because you have three children to care for. Can I suggest some assistance? Share a few links?”

“It looks like your employee is going to be out for longer than a brief absence, Mr. Smith. Have you considered shifting to Social Security disability? It might offer some relief.”

“Mrs. Anderson, we noticed your recent inquiry about long-term care options. Based on your existing life insurance policy, you're eligible for a rider that allows you to use part of your death benefit to cover long-term care expenses. Would you like to discuss this option in more detail? Additionally, we can provide resources on home healthcare services available in your area, as it seems you prefer aging in place based on your recent conversation.”

See also: How AI is Redefining Insurance Pricing Strategies

These human contacts, supported by AI, in context, can transform the way you relate to your customer. Individual or group, it doesn’t matter. Insurers deal with people who experience real-world life events. Imagine what a difference it would make if your people were free to suggest practical ideas and support, at just right moment, to a customer in need.

In the hospitality industry, a concierge is a guest’s most important contact for everything from arranging travel to securing play tickets to running errands. Could it be that AI is the key enabler that launches a new era of “concierge” service in your firm?

Technology alone doesn't create relationships, but when paired with human empathy it can elevate customer experiences to levels never before possible.

Check back in as we continue to explore how you can leverage data-driven insights and prioritize consumer needs, using AI as a pathway to compelling customer service.

Cybersecurity Ventures predicts payouts from cybercrime victims will reach $10.5 trillion by 2025. Cybercrime has become the world’s most profitable non-government enterprise—larger than the illegal drug trade and larger than all but three of the world’s national economies. In this environment, companies large and small are turning to cyber insurance for protection. 

For insurers, though, it’s challenging to gauge each client’s level of risk. Understanding the risk from cyberattacks requires highly accurate assessments of each client’s security defenses, assessments that are increasingly difficult to obtain as cyber-defense technologies evolve and attacks become more complex.

See also: The Evolving Landscape of Cybersecurity

Elements of Attack Prevention

The keys to good protection are visibility, threat detection, and rapid responses to attacks.

Visibility must include all IT and operational technology (OT) infrastructure, including the network, servers, endpoint devices, applications, cloud instances, and user behavior.

Threat detection mechanisms must be able to identify anomalous conditions and also to correlate multiple threat signals to reveal multi-vector attacks. (Most large cyber-attacks use a combination of tactics – tactics that may individually seem innocuous.)

Rapid responses are essential because the more time hackers have inside the infrastructure, the more damage they can do. Many large thefts of personal financial information from businesses like AT&T or Target weren’t discovered for months. Many cybersecurity tools now use AI to improve response time.

Insurers should strongly encourage clients to implement these mechanisms.

See also: As Cybercrime Advances. Cybersecurity Must Keep Up.

Gauging the Risk

For insurers, it’s essential to stay abreast of the evolving cybersecurity landscape (attack types as well as how they’re being defeated),and to have accurate sources of data that reveal each client’s readiness for cyberattacks. 

Larger companies that staff their own security operations (SecOps) facilities can provide readiness metrics that show how and where attacks are occurring and how they’re being stopped. These reports are typically prepared for upper management by the IT managers or directors, but there’s no reason insurers shouldn’t have access to them, as well.

Small and mid-sized companies without the resources to staff a SecOps center may outsource the function to managed security service providers (MSSPs), and these services can also provide client readiness reports to insurers. Ideally, SecOps centers and MSSPs can be a bridge between insurers and prospective clients.

Insurers working with small or mid-sized companies can use MSSPs to open a new channel through which to reach potential customers, access expert regulatory compliance support, and create individually tailored coverage that meets their needs as well as their clients’ needs.

For potential clients, readiness reports and referrals to participating cyber insurance groups streamlines the process of obtaining insurance.

By partnering with cybersecurity providers, insurers gain expert advice about whether potential clients are protected, along with direct access to a new funnel of prospective clients from providers those clients already trust.

Agencies aiming to reach their peak performance must consider the full roundtrip of the insurance lifecycle. A seamlessly connected digital roundtrip that connects each stage of the insurance lifecycle enables agencies to grow, operate, and control their business more effectively.

Put more simply – think of the Digital Roundtrip as a track and the agency as a relay team running to each step of the insurance lifecycle. Agencies need to go from the beginning of the customer relationship, starting with marketing and sales, to searching for appetite and quoting risks, to customer payment, and back around again to providing the customer with their bound policy. 

Connecting all systems, people, and stages of the client journey and policy lifecycle makes the baton handoffs much easier. Let’s take a lap around the track to see how the Digital Roundtrip of Insurance works and how it can help your agency achieve peak performance.

Leading Off With Core Automation

The first leg of the race starts in the system you log in to every morning: your management system. This is your agency's core platform. It should have capabilities that automate operations and allow you to communicate better with insurers and policyholders. Your technology foundation should allow you to leverage essential tools, such as customer relationship management, sales automation, financial accounting, reporting, and policy and benefits administration. 

When choosing your agency management system, it’s important to look for one that has an open and scalable architecture. This will allow you to pass the baton on to your relay partners outside of your core team. You get the choice to integrate with the partners you chose as part of your tech stack, allowing for seamless workflows between applications without ever leaving your core system.

Race to the Market

When a customer brings a risk to an agent, the next leg of the Digital Roundtrip relay is to search for markets and quote the business. This process is incredibly tedious. Searching for markets, completing the necessary paperwork and navigating multiple individual carrier portals is incredibly time-consuming. There is also a lot of potential for errors due to the repeatedly rekeying information. 

This is where a commercial lines quoting tool comes into play. By integrating this tool with your management system, you can have the baton automatically handed off. Information flows from the management system to the commercial lines quoting tool, saving time and errors caused by manual data entry. These tools also give your agents a better view of the coverage available, in real time. They will be able to offer clients the best possible coverage for their risk, without spending as much time searching for markets.

See also: A Data Strategy for Successful AI Adoption

Winning Gold

The next leg of the Digital Roundtrip race is digital payments. While you may not get paid with a gold medal, digital payments helps you get paid faster and allows you to provide a better experience to your customers. Collecting checks and cash has been a steady state process for decades, but it doesn’t come without its challenges. It requires a significant amount of time and expense to your business and slows payment reconciliation. Not to mention the security risk of keeping cash and checks with client bank account information in a desk drawer where anyone can gain access.

Implementing a digital payments solution that integrates into your management system and customer portal will make the payment process seamless for your staff and your clients. This will automate payment collection, processing, and reconciliation. It eliminates the manual application of credits to debits and reduces operational overhead by automatically applying the credit once the payment is processed. The integration with the management system also allows for a paper trail of invoices and notifications to be tied to the client’s account, giving your staff a single view of that client.

On the customer side, digital payments solutions provide the effortless, modern checkout experience that internet-savvy consumers have come to expect. You are able to provide your clients with access to their bills through multiple platforms. Whether it be a payment portal, a secure link, or an invoice, your clients can pay their bill in the way they are most comfortable. They can also use credit cards or ACH – no more relying on checks and cash. Integrating your payments solution to your customer portal means your client’s information will flow seamlessly between platforms, requiring the client to enter minimal information when paying their bill.

See also: Characteristics of an Effective Change Agent

Compete and Win at Digital Speed

Now we’re in the home stretch. We’ve come right back around the track to the management system, where all this information is stored. This will give your team easy access to all the information they need to service and remarket accounts at renewal. 

Creating a seamless end-to-end policy lifecycle for all stakeholders eliminates the frustrating, time-consuming, expensive, and broken processes in insurance today and provides high-value experiences that help agencies find the best markets, effortlessly submit business, and quote and service clients faster. 

With the right tools, your agency can easily embrace the Digital Roundtrip to compete and win at digital speed!

In today's hyper-connected digital landscape, enterprises need to have a robust and cost-effective security service edge (SSE) solution to combat increasingly sophisticated cyber threats and to support digital transformation. While the foundational technologies behind SSE, such as secure web gateways (SWG), cloud access security brokers (CASB), and zero trust network access (ZTNA), have been around for over a decade, their integration into comprehensive SSE solutions has become more prominent and sophisticated over the past five years or so.

But as the market has evolved and expanded to over 30 vendors, some cracks and shortcomings are beginning to show. A number of these SSE offerings and vendors are starting to create technical or business challenges for their customers. So, if you are using a "legacy" SSE product (or even if you just have a couple of standalone point products), how do you know when it's time to upgrade or replace your existing SSE solution?

See also: Top 10 Challenges for Data Security

Here are the top 10 signs it might be time to make a switch if your SSE solution or vendor is falling short in any of these areas:

1. Increased Security Incidents: The rapid evolution of cyber threats means that SSE solutions must continually update and adapt. If your organization experiences a rise in security breaches, malware infections, or other cyber threats, it may indicate that your current SSE solution is no longer effective. This is especially true if you're still using a first-generation virtual private network (VPN) solution for remote access.
2. Latency and Poor User Experience: Many SSE solutions centralize security inspection processes in specific points of presence (PoPs). If these PoPs are not optimally located, it results in increased latency and poor user experience. Persistent slow network performance and frustrated end users are clear signals that your current SSE is insufficient.
3. Fragmented Architecture: Some SSE solutions are built on fragmented architectures, leading to management complexity, data sovereignty concerns, and inconsistent security enforcement. If your vendor's SSE solution is cobbled together from disparate parts, it's time to find a unified platform.
4. High Total Cost of Ownership: Stories abound about inflexible and inflated pricing, hidden fees, complex licensing models that combine per-user and per-bandwidth elements, frequent licensing changes, and unexpected cost increases at renewal time. If the cost of your SSE solution is high but does not deliver proportional value in terms of security, performance, and support, consider switching to a more cost-effective vendor.
5. Inadequate Support and Response Time: Slow or unhelpful customer support during critical incidents suggests that the vendor may not prioritize your needs effectively. If you are having problems with slow response times, insufficient technical guidance, or a lack of responsiveness during critical incidents, it's time to look for other solutions.
6. Weak Backbone/PoP Network: A strong PoP backbone network ensures low latency, high availability, and consistent security enforcement. But PoP coverage is a mixed bag. Some vendors have fewer PoPs than you would expect, some limit the number of PoPs customers can use, and a number of vendors do not run all their services on all their PoPs. If your vendor has a weak PoP network or runs services primarily over public clouds, consider evaluating alternatives.
7. Complex Deployment and Management: If deploying your SSE solution is complex and time-consuming, requiring multiple consoles and significant effort to integrate with existing systems, it may be time for a change. Look for solutions that provide easy deployment and unified management.
8. Lack of Innovation and Modern Features: Most SSE solutions incorporate a basic set of security capabilities, including secure SaaS and private application access, threat protection, and data protection. If your legacy solution lacks features such as sandboxing, advanced data leak prevention (DLP) functionality, digital experience management (DEM), and customizable reporting, it may be time for an upgrade. Advanced capabilities like AI-based threat detection and support for clientless use cases are essential for modern SSE solutions.
9. Insufficient Reporting and Analytics: For effective SSE security, it's important to see the entire security fabric, including users, devices, cloud gateways, and how traffic is traversing the internet. If your vendor struggles to offer detailed visibility and analytics, making it challenging to assess your security posture and respond to threats, it's clear you need to consider alternatives.
10. Weak SASE Road Map: SASE represents the future of SSE, offering a unified, scalable, and flexible approach to securing and managing modern enterprise networks. Some vendors offer simplistic "toy" traffic forwarders that they call SD-WAN but are not enterprise class. Others don't support key connectivity use cases like site-to-site connectivity, zero trust in the branch, lateral movement prevention in the branch, and dynamic protocol security. If your vendor lacks a compelling SASE roadmap, it's time to find a vendor that embraces this concept.

See also: The Evolving Landscape of Cybersecurity

As you see the renewal date for your SSE solution approaching, it's time to think about whether you're getting what you need from your platform. If you see any of the above issues in your current SSE solution, consider looking for your "next generation" solution. There are some good, tightly integrated products on the market to help you best meet your requirements, resources, and budget.

The insurance industry is leveraging artificial intelligence (AI) to overcome operational challenges and enhance customer experiences. As carriers and agencies adapt to hybrid work models, AI-powered tools are becoming increasingly crucial for maintaining efficiency and quality of service.

Revolutionizing Contact Centers

One of the most significant applications of AI in insurance is in contact centers. Real-time coaching and sentiment analysis are revolutionizing how agents interact with customers. By transcribing and analyzing conversations as they happen, AI can provide immediate suggestions to agents, helping them steer difficult conversations in a positive direction. This not only improves customer satisfaction but also aids in agent development, especially in remote or hybrid work environments where traditional “sit-with” coaching methods are no longer feasible.

See also: How AI is Redefining Insurance Pricing Strategies

Enhancing Customer Relationships

The integration of AI with customer relationship management (CRM) systems is creating a more holistic view of policyholders. By connecting various communication channels and automatically updating customer profiles, insurers can provide more personalized experiences. This omnichannel approach ensures that, regardless of how a customer chooses to interact—be it through phone, email, or chat—their information and history are readily available to agents, leading to more efficient and satisfying interactions.

Streamlining Claims Processing

AI is making significant strides in claims processing, particularly in handling first notice of loss (FNOL) during catastrophic events. Intelligent virtual assistants (IVAs) are being deployed to manage the surge in claims during disasters, ensuring that policyholders can initiate claims quickly without long wait times. These AI-powered assistants can gather initial information empathetically, set proper expectations, and seamlessly hand off to human agents when necessary, all while providing a consistent claims experience.

See also: How Gen AI Will Revolutionize Claims

Improving Underwriting and Risk Assessment

The use of AI in insurance operations extends beyond customer-facing roles. It’s also enhancing underwriting processes by triaging incoming documents and providing quick insights to underwriters. This accelerates decision-making while still maintaining human oversight for complex cases. Moreover, AI is being used to analyze large volumes of interaction data to identify trends, potential coverage needs, or underwriting issues that might otherwise go unnoticed.

The Future of AI in Insurance

Looking ahead, the integration of AI with other technologies like telematics and Internet of Things (IoT) sensors promises to make insurance more proactive and predictive. Insurers will be better equipped to help policyholders prevent losses, whether by warning of impending weather events or suggesting safer driving habits. This shift from reactive to proactive insurance not only benefits policyholders but also helps insurers manage risks more effectively.

As the insurance industry continues to evolve, the successful implementation of AI technologies will require careful change management and a focus on both employee and customer experiences. The goal is not to replace human interaction but to augment it, creating more efficient operations and more meaningful customer engagements.

To learn more about how AI is driving insights and overcoming operational challenges in the insurance industry, watch our recent webinar on the subject, sponsored by RingCentral and featuring expert insights and real-world examples.

I'll keep this short this week because I feel like I've been holding my breath for months in advance of Election Day in the U.S., and my lungs are about to explode. Maybe my brain, too. Many of you probably feel the same way.

But I do want to share some thoughts about a smart white paper from BCG that looks at the progress that has — and hasn't — been made on using artificial intelligence in business in the nearly two years since ChatGPT debuted. 

The headline numbers: While 98% of companies are experimenting with AI, only 26% have moved beyond the pilot stage and are generating value. Just 4% are at what the consulting firm  considers to be the forefront of implementation of generative AI.

Let's take a look at who the leaders are, where they're generating value, and how insurers stack up.

The BCG white paper opens with the enormous potential for AI: 

"A financial institution is committed to achieving $1 billion in productivity improvements, in addition to enhanced risk outcomes and better client and employee experiences, by 2030. A biopharma company is chasing $1 billion in value potential (revenues and costs) by 2027. A major automaker expects to cut its cost of goods sold by up to 2% and accelerate product development by 30%."

Based on research into more than 1,000 companies worldwide, BCG found:

"Over the past three years, leaders’ revenue growth has been 50% greater than the overall average. Their total shareholder returns are 60% higher, and they gain 40% higher returns on invested capital. These companies also excel on nonfinancial factors, such as patents filed and employee satisfaction, and they are in pole position to benefit as AI platforms and tools mature."

BCG says six factors distinguish the 26% of companies it identifies as leaders:

• They focus on the core business processes as well as support functions. 
• They are more ambitious. Leaders’ expectations for revenue growth from AI by 2027 are 60% higher than those of other companies, and they expect to reduce costs by almost 50% more.
• They invest strategically in a few high-priority opportunities to scale.
• They integrate AI in efforts both to lower costs and to generate revenue.
• They direct their efforts more toward people and processes than toward technology and algorithms. Leaders follow the rule of putting 10% of their resources into algorithms, 20% into technology and data, and 70% into people and processes.
• They have moved quickly to focus on GenAI. 

How does the insurance industry stack up? 

BCG says insurance is somewhat above-average on the firm's AI maturity curve and adds that the industry does especially well in using generative AI in core processes. On core processes, BCG ranks insurance fourth in a list of 17 industries, behind only software, media, and fintech.

BCG says insurers' biggest challenges "involve people and processes: improving staff AI literacy, prioritizing opportunities over other concerns, and establishing ROI for identified opportunities. They also wrestle with the tasks of integrating AI with existing IT systems and of increasing the accuracy and reliability of AI models."

I'd second what BCG says about insurers using AI in core processes. I've been impressed, in particular, with what I've seen concerning claims and underwriting and, to a slightly lesser extent, with agents and brokers. 

I'd also say that insurers have done well on the final two of BCG's list of six characteristics of leaders. The insurance industry has focused on people and processes, rather than just on the technology, and has moved quickly.

On the other three characteristics, I'm less sanguine. 

I'm not sure that insurers have been as ambitious as they could be. Even with claims, underwriting, and agents and brokers. the emphasis has been on making current processes more efficient — a worthwhile goal, to be sure, and one that can deliver quick benefits — rather than trying to reimagine processes or to plumb AI for insights on risks and customers. 

I hear about fairly diffuse efforts on AI, not the "few, high-priority opportunities" that BCG saw at the leaders. 

And I don't see a lot of focus on using AI to generate revenue, rather than just cutting costs. 

So I'd say insurance is off to a good start on generative AI — but still has an awful lot of opportunities in front of it and could benefit from some more ambition and focus, especially on revenue opportunities.

Along those lines, we're starting an AI newsletter later this month that will highlight not just articles on ITL but from around the Web and from consulting and research firms that provide examples of innovative successes and that explain how the rest of us can emulate the leaders. 

Please keep an eye out for it.

And, in the meantime, if you're eligible, please vote.

Cheers,

Paul

As insurance leaders navigate an increasingly dynamic market, the ability to understand and predict future risk has never been more critical. Predictive modeling offers insurers powerful insights into risk, but implementation often takes a backseat to ongoing IT projects, including the costly modernization of legacy systems and efforts to clean up fragmented data. For insurers, deferring predictive modeling due to IT prioritization bottlenecks can be costly, as competitors that leverage advanced risk assessment capabilities surge ahead. The rise of no-code predictive modeling tools, however, provides an efficient and low-resource alternative, empowering insurance companies to derive immediate value without overburdening IT. 

The Costly Modernization Bottleneck

Most insurance companies are heavily dependent on legacy systems, many of which are decades old. Modernizing these systems is a complex and costly endeavor. For example, the replacement of a core policy administration system could cost an average of $15 million for mid-sized companies, with expenses for large insurers reaching well over $100 million. These transformations also take significant time, often stretching three to five years. According to Bain & Company, insurance transformation projects regularly run at least 25% over budget and far beyond initial timelines.

These large, resource-intensive IT projects are essential, but they consume a disproportionate share of IT bandwidth, leaving little room for new, high-impact initiatives. As a result, high-value solutions like predictive modeling often get sidelined or postponed until “someday”—a day that may never come, given the perpetual backlog of modernization needs. A McKinsey study reports that 60% of insurers see legacy technology as a major barrier to innovation, restricting their ability to respond to market dynamics and address emerging risks effectively. This creates an operational drag that limits agility and hinders competitiveness.

The Data Challenge in Insurance

To make predictive modeling effective, insurers need reliable, high-quality data—a challenge in itself. Insurance companies have access to vast stores of data but struggle to harness it due to fragmentation and lack of integration across legacy systems. McKinsey highlights that insurers only make use of around 15% of their data effectively, which constrains their ability to leverage insights for proactive decision-making. Cleaning up and organizing this data is often viewed as a prerequisite to predictive modeling, leading to years-long delays in implementation.

The intense focus on data cleanup detracts from the more strategic objective of predictive modeling, which could provide actionable insights into future risk, underwriting, and pricing. With the cost of poor data estimated to be around $3.1 trillion in the U.S. alone, insurers stand to benefit greatly from platforms that can leverage existing data while mitigating the need for extensive cleanup. Here again, no-code predictive modeling offers a solution that sidesteps these constraints by simplifying the integration of fragmented data into models that deliver meaningful insights.

Predictive Modeling and Its Strategic Benefits in Understanding Risk

Predictive modeling has proven indispensable in understanding risk and enhancing underwriting precision, but insurers that delay its adoption risk losing their competitive edge. With predictive analytics, insurers can identify patterns and trends that inform decisions on policy pricing, risk exposure, and fraud detection. McKinsey estimates that predictive analytics in underwriting alone could unlock as much as $1.3 trillion in annual value across the insurance industry by 2030. Further research by Deloitte reveals that insurers using advanced predictive analytics in underwriting have improved loss ratios by up to 20% and enhanced customer retention through more accurate pricing and personalized offerings.

Predictive modeling enables insurers to approach risk assessment with increased sophistication, analyzing factors like climate change, shifting demographics, and economic volatility. This approach not only improves the accuracy of pricing models but also equips insurers to anticipate and mitigate potential claims spikes. Companies that implement predictive modeling can rapidly adapt to these changing conditions, setting themselves apart from competitors still reliant on traditional risk assessment methods.

No-Code Predictive Modeling: A High-Impact, Low-Resource Solution

For insurers burdened by IT resource constraints, no-code predictive modeling platforms offer a compelling solution. These platforms empower non-technical teams to build, test, and deploy predictive models with little or no IT support, allowing insurers to harness data science capabilities efficiently without disrupting ongoing IT projects. According to Gartner, by 2025, 70% of new enterprise applications will use no-code or low-code technologies, underscoring the growing preference for rapid, scalable solutions.

The benefits of no-code predictive modeling for insurers include:

1. Reduced IT Dependency: With no-code platforms, actuaries, underwriters, and other insurance professionals can directly engage with predictive models without waiting for IT resources to free up. This accelerates the implementation of high-impact solutions while allowing IT teams to continue focusing on long-term modernization initiatives.

2. Faster Time-to-Insight: Traditional predictive modeling projects often take months, if not years, to complete. No-code platforms enable insurers to build and deploy models in a matter of weeks, providing quick access to insights that can guide decision-making. This agility allows insurers to respond to emerging risks and evolving market conditions more effectively.

3. Cost Savings: No-code platforms eliminate the need for custom coding and specialized data science expertise, reducing the costs associated with developing predictive models. In contrast to the high costs of traditional development, no-code tools are typically more affordable and generate faster returns on investment.

4. Data Accessibility and Utilization: No-code predictive modeling platforms are designed to integrate seamlessly with existing data sources, allowing insurers to work with their current data assets without requiring exhaustive data cleanup efforts. This capability helps insurers to leverage their data more effectively and make informed decisions based on actionable insights.

5. Empowering Business Teams: By enabling business users to work directly with predictive models, insurers decentralize data science capabilities, allowing departments like actuarial and underwriting to lead predictive analytics initiatives. This not only accelerates innovation but also aligns model-building efforts more closely with business needs and objectives.

Staying Ahead of Competitors in Risk Precision

No-code predictive modeling not only accelerates the pace of innovation but also sharpens an insurer’s competitive edge. The insurance landscape is becoming increasingly data-driven, with companies that leverage advanced analytics gaining a distinct advantage. Deloitte’s research shows that insurers with strong data science capabilities are 30% more likely to experience growth in customer acquisition and retention, and McKinsey predicts that companies with advanced analytics capabilities will see a 20–30% improvement in efficiency. 

Insurers that proactively adopt predictive modeling will be better positioned to offer competitive pricing, identify and mitigate risks, and respond to regulatory requirements. This positioning is crucial as emerging risks—from climate change to cyber threats—create new complexities in risk assessment. By leveraging the latest predictive modeling capabilities, insurers can shift from reactive to proactive risk management, differentiating themselves in a market that values risk expertise.

Aligning IT Prioritization with Strategic Goals

For insurers, delaying improving the accuracy of their risk precision due to IT backlogs presents an opportunity cost that can no longer be ignored. Rather than viewing predictive modeling as secondary to system modernization, senior executives can adopt a balanced approach to IT prioritization, enabling short-term high-impact projects to move forward while longer-term initiatives remain on track.

By championing no-code predictive modeling, senior executives can enable their companies to maximize value from their existing data assets without compromising IT goals. No-code solutions bridge the gap between current operational demands and future data-driven objectives, enabling insurers to gain immediate insights and maintain strategic momentum.

The Takeaway For Carriers

In a competitive market, the ability to understand and predict risk is indispensable. For insurance companies, no-code predictive modeling platforms offer a powerful and practical way to harness predictive insights without waiting for the resolution of long-standing IT projects. By incorporating no-code solutions into their digital strategy, insurance executives can avoid costly delays, empower their teams, and stay ahead of competitors in risk understanding and mitigation strategies.

Now is the time for insurers to act. As McKinsey estimates, the potential value of predictive analytics in underwriting and risk assessment is monumental, and turnkey solutions provide a path to capturing that value today. By leveraging this technology, insurance leaders can not only enhance their companies' understanding of future risk but also secure their positions as forward-thinking leaders in an industry poised for transformation.

Shannon is a Tedx speaker and has coached dozens of data and Insurtech startups, advising Fortune 500 clients on analytics strategy as head of Client Management for a national health-tech company and Co-Founder of BetaXAnalytics, a company that pioneered emerging data science techniques using AI to remove the barriers to transparent and actionable data. She also spent 12 years with Amica Insurance running branch sales and service operations across the country. She holds a Master of Science degree in Insurance Management from Boston University.

Sponsored by ITL Partner: Pinpoint Predictive

The hard insurance market has made it difficult for agents trying to find adequate coverage for their clients – but hard markets don’t last forever. In fact, there’s reason to believe this hard market will be ending soon. While this is good news, it does not mean that agents should take things easy right now – quite the opposite. As the market begins to soften, brokers must take action to seize the opportunity, grow their books and position their businesses for a stable future. This window won’t last forever. Agents who manage to win customer loyalty now will build a solid foundation for stable growth for years to come.

 Sponsored by: Smart Choice

By integrating an insurance policy management system with other corporate tools, an insurance agency can further improve policy management processes. This article highlights the six most valuable integrations for insurance policy management software a company should consider setting up.

ERP

Insurance agencies use enterprise resource planning (ERP) systems to connect and centralize disparate business processes such as personnel, contract, finance, and customer relationship management, to name a few. 

ERP can consolidate and store vast amounts of data, including claim and payment histories. Integrating with an insurance policy management solution means agents can quickly access the required data stored within ERP without switching systems, enhancing productivity. 

For instance, an agent can quickly check on customer payment history and on related financial data to ensure the customer's financial credibility before issuing an insurance policy or when processing insurance claims. 

See also: 7 Must-Haves for Insurance Software Platforms

CRM

Customer relationship management (CRM) streamlines customer-facing processes such as marketing, sales, and customer support. What is more, CRM solutions consolidate customer data across diverse data sources and form comprehensive customer profiles.  

Integrating policy management software with CRM allows agents to use the information in the profiles about demographics and preferences to personalize insurance policies, boosting customer satisfaction significantly. Conversely, marketing specialists can use the integration to view data about clients' active and past policies (typically stored in insurance policy management systems) to create more targeted and efficient engagement campaigns.

Claims management

Integrating claims management software tools into the policy management system means agents can easily review the conditions and details of a policy when a customer files a claim.

Underwriting 

Integrating underwriting tools means information stored in insurance policy management software becomes available in the underwriting system. Underwriters can make informed underwriting decisions easily, improving accuracy.

Billing and invoicing

Companies can also integrate insurance policy management solutions with billing and invoicing software, allowing for automation of the calculation of premiums and speeding that process significantly. In addition, integration allows for the increased traceability of invoices, which facilitates the overall financial visibility within organizations.

See also: Underwriters Will Thrive With APIs

Data analytics

It is also important to set up an integration between insurance policy management and data analytics tools and enable smooth information exchange. Such an integration can help an insurer study all-rounded policy and policyholder data and generate insights.

For example, a company can assess business profitability and efficiency by analyzing key performance indicators (KPIs) such as policy renewal rates, customer retention rates, or claim rates. A company can predict future customer demand for its services based on policy data, thus improving financial planning. Insurers can also analyze policyholder demographics and craft more targeted and efficient marketing campaigns.

Final thoughts 

Insurance policy management software enables agencies to administer policy issuance, renewal, and cancellation, enhancing work process efficiency. By making this software an integral part of their entire tech ecosystem, insurers can maintain smooth data exchange and accomplish much more. 

Whether an insurer owns an out-of-the-box insurance policy management software or a bespoke solution, an experienced technological partner can help integrate it with other tools. Third-party consultants can assist an insurer with selecting a suitable integration approach, executing and testing integrations, and providing a continuous optimization plan to make integrations more cost-effective and secure.

As businesses grapple with unpredictable economic conditions, insurance premium rates will provide some relief, remaining steady through year’s end. For lines of business dealing with more complex risk such as auto, general liability, and property, an average increase of 6.9% still hit the middle market across the U.S. in Q3. 

With HUB’s Q3 Rate Report predicting stable rates heading into 2025, businesses across the risk spectrum will have a window of opportunity to tighten their risk management strategies, review coverage gaps, and evaluate disaster preparedness strategies. Many businesses are looking to minimize the effects of the real potential for business interruption, as the looming threat of natural disasters, cyberattacks, and geopolitical events continues to present significant challenges. Alternative coverages like captives and parametric solutions will help businesses do this, as well, as premiums push higher in catastrophe-prone regions next year.

See also: The Long Game of Inflation – Dynamic Portfolio Strategies

Navigating a stabilizing market

Planning will help companies strengthen their risk management strategies and secure more favorable terms. Here’s how to stay ahead:

• Refine your current risk management plan. Leverage this period of stability to thoroughly review your risk management program. Ensure your insurance strategy adequately covers exposure, including catastrophe risks. Your broker can offer the insights and support needed to enhance your preparedness for both current and future challenges.
• Stay informed on market trends. Staying updated on rate trends across different lines will empower you to make informed decisions. Engage your broker to prioritize risks and develop strategies that align with the latest market shifts, ensuring that you remain competitive.
• Evaluate your insurers. Recent insurer collapses in catastrophe-prone states such as California and Florida have left policyholders vulnerable. Work with your broker to identify top-rated carriers that provide reliable coverage, not just the cheapest option.

Industry rate outlook for 2024 

Despite the stabilization of commercial insurance rates, key industries continue to face varying challenges. Below is a summary of the projected rate changes as of Q3 2024, along with the main drivers behind them:

Construction: Flat to +10%

There has been some relief in builder’s risk coverage since Q1, but an active hurricane season could quickly reverse this trend, creating a more difficult insurance environment in catastrophe-prone regions. 

Healthcare: Flat to +10%

Rates are stable overall, although nuclear verdicts and rising claims costs may drive rates higher for certain clients.

Transportation: +7% to 12%

Rates continue to rise, especially for high-risk operations, as high as 25%. Strong safety records may mitigate increases.

Commercial Automobile (1-5 Vehicles): +5% to 10%

Rates are stabilizing for smaller fleets, but businesses with severe losses could face steeper increases. Improving industry competition could ease rates.

Commercial Property: -10% to +5%

Recovery is ongoing, but accounts with CAT exposure or non-renewals may still see significant increases.

Real Estate: +5% to +10%  

Frame residential risks continue to face challenges, while larger portfolios and commercial properties like warehouses and high rises are seeing more competitive rates. 

General Liability: Flat to +10%

The outlook here is relatively stable, with most businesses seeing modest increases. Carrier positions vary based on loss experience, location, and program design.

Umbrella & Excess Liability: Flat to +10% 

Competition is growing in higher layers, but complex risks still face significant increases.

Catastrophic Perils: -10% to +5%

Rates vary by exposure, with severe weather zones still facing premium increases.

Cyber Liability: -10% to Flat

Cyber insurance premiums are declining, but recent high-profile breaches like Crowdstrike may signal tougher market conditions in late 2024 and into 2025.

Cannabis: -5% to Flat

Cannabis operations, including growing, processing, and retail, are seeing slight reductions in rates, particularly in more established markets.

Workers’ Compensation: -3% to +3%

Workers’ compensation remains relatively flat, but concerns about rising claim severity and workplace safety are prompting caution.

See also: How AI is Redefining Insurance Pricing Strategies

HUB’s rate guidance is based on proprietary data and insights from brokers and risk consultants across North America. While rates remain steady in many areas, businesses should capitalize on this period of stability to strengthen their risk management strategies. Early engagement with brokers and a thorough review of coverage options will help mitigate future price increases and ensure comprehensive protection against emerging risks.