If you’re not completely happy with your pricing, you’re not alone.
Maybe execution takes too long. Maybe you can’t trust the numbers. Or perhaps the market’s moving faster than your pricing can keep up. Each issue points to different parts of the process – and calls for a different fix.
Based on insights from pricing experts, this follow-up to Part Two looks at where these struggles show up. And how leading insurers are fixing them with speed and confidence.
Pricing Challenges: Is Pace or Accuracy Your Bigger Concern?
By “pace,” we mean how quickly rates are reviewed and pricing changes go into production. Reducing the lag between identifying optimal rates and putting them into play is critical for staying competitive.
Some pricing leaders say speed is the bigger issue. They trust their models, but can’t act on them fast enough. Others are more concerned with whether those models are producing the right rates in the first place.
Pace is an issue if:
- You trust your pricing process, but can’t move fast enough to adjust
- Good rates become outdated before they’re live
- You’re skipping steps, like documentation and checking, just to keep things moving
Accuracy, however, is an issue if:
- You’re unsure if your premiums are right
- You worry about loss ratios trending upward or competitiveness in your markets
- You don’t have a clear view of your market position
Naturally, pace and accuracy can be related. After all, it’s hard to trust the accuracy of production rates that are old. But thinking of these as different issues makes it easier to understand where the real problem lies. From here, we’ll explore how each plays out. And what’s actually slowing things down.
Identifying the Real Cause of Slow Pricing Adjustments
Insurance companies tend to fall into one of two categories: either they find it takes a long time (a year or more!) to create a predictive model for pricing, or they find they create more predictive models than the company can actually use. One’s an analytics problem. The other’s an execution problem.
What’s Slowing Down Your Analytics: Limited Data or Outdated Tools?
Long, drawn-out analytics projects tend to result from one or both of two issues:
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Lack of Access to Data
Many companies underestimate what analytics teams actually need. Most analytical methods rely on tabular datasets, but that structure doesn’t typically match how data is stored in day-to-day systems. As a result, analytics teams end up scrambling to pull incomplete data or waiting for IT support – sometimes for weeks. Acquisitions only make it harder, scattering data across systems that don’t speak the same language. Fixing this starts with a real commitment to supporting analytics. Dedicated data extraction tools and IT resources are key. Instead of expecting actuaries to double as IT experts, invest in multi-disciplinary teams that bring both skillsets together. -
Challenges in Pricing Analysis
Even with good data, the analytics projects that lead to pricing plans can be tough to automate. Some methods (like GLMs) require a lot of hands-on work, leading to endless trial and error. On top of that, peer review, documentation, and governance, while important, just add to the complexity. Still, they shouldn’t be ignored because they help avoid blind reliance on single experts, which can destroy trust in the models. While open-source tools like R and Python give flexibility, they don’t address – and can worsen – the need for collaboration and a team approach. Insurance-specific analytics tools are customized to make pricing analytics easier, often including automation, collaboration, workflow management, and governance in one platform.
What’s Stalling Your Execution: Slow Implementation or Decision-Making?
The execution challenge usually comes down to deciding what rates to charge and actually getting them out the door. But that can play out differently, depending on the line of business. In personal lines, the decision-making process gets held up by overly complex models. In commercial lines, implementation is slower, dragged down by manual tools.
Let’s take a closer look at each.
How to Strengthen Decision-Making For Personal Lines Pricing
In personal lines, the pricing process often runs into delays after the analytics are complete. Analytical teams produce a set of proposed rates based on data. Then, business leaders review, question, and revise those rates before they’re implemented.
To build trust and avoid delays, prioritize open conversations between analytics teams and business stakeholders.
If the decisioning process isn’t structured or efficient, it can lead to hesitation – or worse, mistrust. Poorly vetted rates may be pushed through, or good rates might get stuck in review limbo. While today’s predictive models are generally strong, real-world application still requires real-world judgment.
To build trust and avoid delays, prioritize open conversations between analytics teams and business stakeholders. Invest in decision-support platforms that support fast, structured collaboration – and come to a shared understanding of when a rate is “ready enough” to move forward. When teams align on process and accountability, it becomes easier to make quick, confident decisions about new rates.
Overcoming Workflow Hurdles in Commercial Lines Pricing
In commercial lines, the pricing process is shaped by the underwriter, not just the model. Predictive models provide input, but underwriters ultimately decide what factors to consider and how to price the risk. This makes the process harder to standardize – and often, very hands-on.
Many underwriters rely on Excel-based ecosystems across market segments or use custom tools that vary widely between teams. On top of that, they’re balancing pricing with submission workflow management.
To keep pace, insurers are turning to more connected systems. Some of the most promising improvements include underwriting workbenches that centralize pricing activities, integrated platforms that connect pricing and workflow, and AI-powered tools that help sort and prioritize submissions faster. In addition, spreadsheets are being placed with more reliable, connected systems.
The goal is to replace slow, fragmented tools with smarter, all-in-one solutions that don’t disrupt the expertise underwriters bring to the table.
Unlocking Faster Rate Adjustments for a Competitive Edge
Once the decision to put rates into production is made, many companies run into trouble when actually making those changes in the rating engine. For any given risk, a rating engine calculates an initial price, either as a direct quote or a starting point for underwriters.
Sounds simple, right? But here’s the catch: That rating engine also needs to connect directly to your policy administration system to reflect any underwriting adjustments when the policy is bound. The problem is that updating those rates usually isn’t simple. Or fast.
Most insurers are still dealing with rating engines that were built for speed (like quick quoting), but not for flexibility. Rate changes by business users were never the priority. So today, even small updates often turn into full-blown IT projects. And while analytics tools like Python are great for modeling, they’re not built to power a rating engine or connect with a policy admin system.
To move faster, you need rating plans that are easy to update, without waiting on IT. They also need to be structured enough for both analytics and automation, yet flexible enough to scale across commercial and personal lines. When you’re evaluating your setup, ask: How easy is it to make changes? Who controls the rate updates – business users or IT? Can quoting and binding keep up with the pace of the market?
Aim for more broad-based solutions that work together, rather than patchwork fixes that constantly need maintenance.
Let’s switch from pacing concerns to accuracy.
Pricing Accuracy Struggles: Trust Issues or Rising Loss Ratios?
If you aren’t sure whether your premiums are too high to be competitive or too low to be sustainable, the problem could be real – or simply a matter of getting better information. If it’s a matter of confidence, think about what would increase your comfort level. But if your company’s loss ratios are rising faster than your peers, there’s a deeper problem worth looking into.
Building Pricing Confidence Through Better Data and Insights
If business leaders don’t fully trust their pricing function, especially without verifiable metrics to back it up, it’s often because they just haven’t had enough visibility into how it works. While analytics can seem intimidating, practitioners should be able to clearly explain important insights.
Good questions for leaders to ask their analytics teams: How do you know? Why do you think that? Could something else explain what you’re seeing? What’s the dollar impact of ignoring or modifying your recommendation? In return, they should expect understandable answers.
If business leaders don’t fully trust their pricing function, especially without verifiable metrics to back it up, it’s often because they just haven’t had enough visibility into how it works.
Engaged business leaders provide vital feedback and context to analytics teams. At the same time, experienced underwriters and insurance professionals help shape pricing to account for age-old, known issues. But even the most engaged leaders can hit a wall if they’re not getting the insights they need. If reasonable questions go unanswered or critical information is missing, there’s a disconnect.
To fix these gaps, make sure analytics teams have the tools, data access, and visibility they need to deliver clear, useful answers. That’s what turns good models into confident decisions.
Rate Adequacy vs. Adverse Selection: The True Cost of Mispricing
When concerns about pricing accuracy come with unstable performance and rising loss ratios, it’s time to start asking more questions. Start by thinking about premium in two parts: the average rate level (does it cover losses and expenses?) and the segmentation strategy (are you charging the right price to the right customer?).
If your average rate is too low – perhaps due to regulatory constraints or inaccurate trend estimates – you’ll see it in your loss ratios. And if it takes months to implement new rates, even the best pricing assumptions can become stale before they go live. In this case, focus on speed to market and efficiency to react to market conditions as quickly as possible.
That’s one part of the problem, but segmentation brings its own risks.
Strong segmentation is both a defense against adverse selection and a competitive edge. It lets you grow confidently, knowing every customer is getting the right price.
Insurers don’t charge everyone the average rate. Instead, they segment prices for fairness and competitiveness. That means higher risk policyholders pay more and lower-risk policyholders pay less. When segmentation is off, it leads to subsidies, where some customers overpay and others underpay. This opens the door for competitors to offer better rates to your lower-risk customers.
The result is adverse selection – when low-risk customers leave, you’re left with a riskier, more expensive book. Loss ratios climb, not because your average rate is wrong, but because the mix of customers has shifted. In this case, focus on your analytics to create better models to compete in a sophisticated market.
Strong segmentation is both a defense against adverse selection and a competitive edge. It lets you grow confidently, knowing every customer is getting the right price.
Insurance Pricing That Evolves and Performs
The insurance pricing function is multidisciplinary, involving far more than just actuaries. It runs on multiple timelines – daily, as new customers receive quotes, and periodically, as prices are adjusted to keep up with changing economic and competitive conditions. To be effective, these insights must connect back to the company’s broader goals, shaping strategy while also being guided by it.
With so many moving parts, it’s no surprise that pricing inefficiencies can show up in different ways, from misaligned stakeholders to outdated tools or limited data access. Whatever the case, the best way forward is to ask the right questions. A clear diagnosis leads to smarter decisions, turning pricing into a well-oiled machine that runs smoothly, adapts fast, and gives you the confidence to price with precision.