Insurance companies are under pressure to modernize operations while managing costs and mitigating risk. This guide demonstrates how predictive analytics in insurance delivers measurable ROI across underwriting, claims processing, and customer management.

In brief:

• Predictive analytics in insurance drives measurable ROI by reducing risk, cutting costs, and improving underwriting, claims, and customer management.
• Insurers boost model reliability and ROI by investing in data hygiene and partnering with vendors to fill data gaps.
• Teams can achieve better results when they embed predictive models into daily workflows and prioritize change management and training.
• Insurers maintain relevant recommendations by monitoring models continuously and updating them regularly to prevent model drift.
• Companies can maximize ROI by tracking model performance and user adoption, comparing outcomes from model-driven decisions with human adjustments.

According to a 2022 FRISS report, an estimated 20 percent of insurance claims are fraudulent. If your insurance firm isn’t using predictive analytics, you’re missing out on opportunities to reduce risks and cut costs.

Predictive analytics has become a fundamental cost of doing business. As Centric Consulting National Insurance Practice Lead Sean Neben explains, “The cost of not having predictive analytics means you definitely notice if they’re missing.”

However, the tricky part isn’t creating the predictive models. It’s integrating them into daily operations to get real results instead of letting data sit unused in a dashboard.

With real-time insurance analytics platforms, it’s easy to use actionable data to improve core processes like underwriting and claims. This access to useful data makes it easier for insurance agencies to achieve a measurable return on investment (ROI) and gain a genuine competitive edge.

We’ll discuss:

• Practical steps for how to implement predictive insurance analytics
• Common mistakes
• How to measure success

Measuring the ROI of Predictive Analytics

Slashdot writes, “Data is very valuable — just don’t ask us to measure it.”

Twenty-five percent of leaders say it’s difficult to prove the ROI on artificial intelligence (AI)-driven data projects, and research on dark data suggests that companies only use about half of the data that they generate. Unlike a traditional project with a clear before and after, the value of predictive analytics shows up in less obvious ways.

Data analysis is always multifaceted, and it’s not easy to silo success into one department or another. Look at trends and patterns across your business in core areas to identify patterns and potential improvements.

• Underwriting: Track improvements in loss ratio and analyze pricing accuracy. Take the time to create and draft an underwriting process.
• Claims: Look at any reductions of cost per claim, fraud detection identification times, and how fast simple claims are approved. The goal is to prioritize high-risk cases early to maximize value.
• Customer Retention: Look for a decrease in churn rates and an increase in upsells. It could be a leading indicator that personalized offers from predictive analytics are increasing customer satisfaction.

It’s also crucial to track the performance of the models themselves. Neben emphasizes the importance of tracking outcomes without underwriter adjustments to really understand what your ROI is from the models.

Before you can prove your model is working, you need a starting point and baseline. Try launching a small-scale pilot program to gather concrete results before a full rollout, which helps justify the investment.

Common Pitfalls That Prevent ROI From Predictive Analytics

Even the greatest model falls flat if implementation lacks direction, data quality is poor, or models aren’t updated. Here are the most common mistakes we see insurance companies make when implementing predictive analytics and tips about how to avoid them.

Poor Data Quality and Availability

The most significant operational challenge is data quality. Predictive models rely heavily on accurate, clean, and granular data to produce results. Neben says the biggest challenge he sees with predictive analytics is that many insurers don’t have good quality data. This pollutes the entire system and makes it difficult to see any real return.

For example, smaller insurers might lack sufficient data volume to build a reliable model. As a solution, a small insurance firm could partner with a vendor that has a contributory database, which provides an anonymous pool of data sourced from many different insurers.

How to Fix: Perform basic data hygiene and cleansing before launching predictive analytics. Partner with a vendor to fill in data gaps or hit the necessary volume.

Lack of User Adoption and Change Management

User adoption is another major hurdle. Underwriters and claims adjusters are used to relying on their professional intuition. Introducing a model as an optional tool often leads to selective use, where it’s only consulted to confirm a gut feeling.

Neben highlights the challenge of change management: “You can tell an underwriter that you know the model is 90 percent accurate, right? But every underwriter is going to think they’re the 10 percent that’s better than the model.”

This resistance often stems from a lack of transparency. If your team doesn’t understand how the model works or why it’s making certain suggestions, they’re not going to want to use it. Making a project like this successful is more than just giving your team a new tool. You have to make them feel like it will make their day easier and more productive.

How to Fix: Explore change management best practices, like using a test user group, actively listening to feedback, and leading with authenticity and transparency. Involve leadership to encourage adoption and change, providing background on why predictive analytics matters and how it affects the future of the company.

Models Don’t Receive Updates and Quickly Become Outdated

Outdated tools are another challenge. A great model can fail if the old data no longer matches what’s happening now. By not updating a model consistently, it slowly becomes less accurate over time. The idea that performance slowly degrades over time due to changes in the real-world environment is called “model drift.”

“The term’ model drift’ gives you this idea that it’s kind of a slow thing. The reality is it can happen pretty quickly,” warns Neben.

How to Fix: Continuous monitoring is essential to detect model drift early. Insurers need processes to track model performance over time and retrain models as new patterns emerge. Without monitoring, decisions will be based on outdated information, damaging any potential ROI.

Now that you’re clear on the potential pitfalls, here’s a more step-by-step process for operationalizing predictive analytics for ROI.

How Insurance Firms Can Operationalize Predictive Analytics for ROI

It feels overwhelming, but the actual process is straightforward. For a high-impact project like predictive analytics, create a simple framework for implementation, such as the one below:

1. Make Predictive Analytics Models Easy to Understand

Predictive analytics models shouldn’t only give a final score. Instead, they should explain why they made each recommendation. When an underwriter can see the factors behind a risk assessment, they will be more confident when using the tool. Transparency builds trust and encourages your team to combine their expertise with the power of the technology.

2. Embed Models Into Existing Workflows

Don’t treat predictive analytics as an optional side project. Integrate them into the systems your team uses every day. For example, you can have a model that automatically provides pricing recommendations within your rating engine. That makes the model an integral part of the daily process, ensuring your team uses it consistently and making tracking results easier.

3. Invest in People and Change Management

The biggest challenge isn’t the technology. It’s the people using it. Investing in training helps your underwriters and adjusters understand how the models work and how to use them to improve their work. Encourage feedback and take a collaborative approach.

Neben suggests creating a feedback loop: “If they did something different, then maybe that’s worth a discussion about why? What did someone see that the model didn’t?”

This continuous feedback loop brings employees into the models and encourages them to actively participate in building the program.

4. Track Usage and Monitor Performance

Track how, when, and where employees are using models and the impact they have (or don’t have) in your everyday workflow. Monitor how often teams use the recommendations and when they seem to override them.

Crucially, compare the performance of your book based on the model’s pure recommendations versus the final written business after human adjustments. By comparing the outcome of the model’s recommendations with the outcome of your team members’ final decision, you can see which approach was more successful. Is the model’s advice more effective than your team’s intuition?

Turning Untapped Data Into ROI for Your Insurance Firm

Instead of just building models, insurers can turn predictive analytics into a powerful tool for growth and profit. Investing in good data, ensuring teams understand and trust the model, and making it all part of their daily tasks helps create positive ROI on predictive analytics. Just like with any new technology like AI, ROI can feel elusive, but once you capture it, it feels like a switch flips across your entire organization.

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