Automated, intelligent monitoring takes insurance to new heights
An Earnix survey reveals that most insurance leaders plan to use machine learning in pricing and underwriting.
As recently as a decade ago, the notion of having a semi-automated system for intelligent monitoring seemed outrageous. Even today, with the rise of artificial intelligence (AI) and machine learning (ML), we still have barely scratched the surface of what can be accomplished in terms of performance monitoring in the insurance industry.
But we’re getting closer.
Consider Lucy, a fictional senior business manager at a large insurance carrier. She’s headed out on vacation, but her anxiety is building. She rarely takes time off because she’s nervous something will go wrong while she’s away. The last time she returned from some fun in the sun, she was shocked to see a 15 percent degradation in business performance in sales in one specific region that was overlooked. She spent a week uncovering the source.
Every day begins with Lucy drowning in numbers from the daily KPI reports. It’s an hour-long, often-frustrating experience, and that isn’t even the full story. Fresh insights are revealed once the data science teams digs through the numbers in detail and thoroughly analyzes data processes, underwriting and pricing models, and system performance.
Little does Lucy know that her life at work is about to change dramatically. The organization has implemented a new tool that automates intelligent monitoring. Using AI, the tool has eliminated a significant source of Lucy’s stress by managing all data, KPIs, and models through the centralized system. The tool — no longer Lucy — is the system of record for the entire organization with full governance and transparency.
The system has eliminated much of the manual time Lucy had to spend on monitoring. Not only does it identify problems as they unfold, but it also provides solutions that can be dynamically implemented in minutes, not hours or days. With this load lifted from Lucy’s shoulders, her attention has moved from manual inspection and constant worry to focusing on upgrading the company’s business strategy. For the first time in years, Lucy is excited about her upcoming vacation.
This is all possible today with AI and ML, which are now required capabilities for insurance carriers. Not implementing these technologies will result in a firm falling behind its competitors.
Today, insurers can see the benefits this offers their growth metrics while improving the daily lives of their employees. Workers can eliminate hours of manual procedures by automating processes that alert them to any errors in data pipelines or any missed business targets with specific details on what they are, the root cause behind them, and how to resolve them.
Intelligent monitoring eliminates manual guesswork
ML-driven intelligent monitoring is a necessity for insurers; in fact, in an independent survey commissioned by Earnix in 2023, every insurance leader surveyed said they plan to use machine learning models in their pricing and underwriting processes this year. However, only 20% of the survey’s 400 respondents said they do so today. Furthermore, another Earnix survey found that only 8% were doing intelligent continuous monitoring.
A key concept of intelligent monitoring is that it continuously monitors KPIs, concurrently checking for changes in the data and tracking the performance of the various models behind the KPIs. When data errors occur or business hurdles arise, it’s often late in the process. Through continuous monitoring, an intelligent monitoring system prevents those errors by alerting the business of any changes it detects.
For example, there are some insurance companies with quote volumes for an auto insurance policy in the hundreds of thousands per day or even in the millions. Imagine the chaos without proper continuous monitoring.
KPIs have always been measured but deep analysis is only done infrequently or as needed, meaning “drifts” may be uncovered late, or even worse, not at all. This is a major concern for business executives and insurance leaders; the fear and anxiety of learning too late that they have a significant problem.
Using intelligent monitoring, companies can create very specific reports for stakeholders at all levels that deliver meaningful calls to action. For more technical users, the system can include detailed reports that feature various charts and statistical tests behind all the alerts that enable them to take a deeper look and investigate. Meaningful and timely alerts are a must so the business can act immediately and not wait for data scientists to analyze the situation.
The future of intelligent monitoring
Artificial intelligence and machine learning are the cornerstones to modernizing insurance and banking. In a full visionary mode, the future monitoring solution will include an auto pilot mode with embedded on-line/self-learning ML capabilities such that actions are taken automatically, when relevant and appropriate, to close the loop and remedy drifts observed in data, models or KPIs. Intelligent monitoring is just one example of how these advanced automation technologies are furthering what insurers can do.
As these technologies evolve, new sub-technologies like generative AI and large language models (LLMs) are emerging that will ultimately be more accessible and transparent. With these advancements, insurers will be able to utilize any model that they can dream up, leading to heightened levels of innovation, accuracy, speed to market and efficiency.
Reuven Shnaps, Ph.D., is chief analytics officer at Earnix. Any opinions expressed here are the author’s own.
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