Earnix Blog > AI
3 Top Takeaways from the 2019 CAS RPM Seminar
Jonathan Moran
April 10, 2019
- AI
- Customer Centricity
Earnix recently attended the Casualty Actuarial Society Ratemaking, Product, and Modeling Seminar (CAS – RPM) in Boston, Massachusetts. The conference covered many innovative topics and trends in insurance – from telematics, to mobile and driverless vehicles, to usage-based insurance, to company culture, communication, and regulation. However, Earnix was attending to network and present our point of view on some of the topics we know best – personalization, multi product optimization, and real time rate deployment. Looking back on the conference, I would consider several learnings from the conference as key takeaways:
Personalization. The first session Earnix delivered featured Lucy Kadets of Earnix and Toby Alfred of Digital Transformation Strategies, LLC and was focused on personalizing product packages and offers for insurance consumers. As the session progressed, it was clear that carriers are very much interested in both streamlining and improving the policy bundle and package delivery process from where it stands today. Lucy explained how using analytics to better personalize the coverages, riders, and other variables associated with an auto or home package or bundle would shorten the application process, improve conversions for the brand, and improve the overall consumer experience. Lucy then showed a live demonstration of Earnix’s newest product, Earnix Personalize-it, which helps address the need for analytical personalization in Insurance. Toby then brought Lucy’s portion of the presentation to life by sharing her experience while at Progressive Insurance, where she assisted in creating the “Name your Own Price” tool. This was one of the first instances of early personalization in Insurance and was extremely successful some ten years ago.
New Technologies and Processes. While smartphone usage-based insurance, drone claims, and embedded home insurance were among the topics discussed at CAS RPM this year, Earnix set its sights on having meaningful conversations around the work we are doing in advanced predictive analytics and machine learning. One common technique in insurance that leverages advanced analytics is the performing of multi-product optimization. Bundling home, auto, and other insurance coverages together for the purpose of customer loyalty and retention has been done for a while in insurance, and Earnix offered some very practical advice on how to improve this process even further. During our session on multi-product optimization, Drew Lawyer from Country Financial and I uncovered some interesting insights from our audience.
We began this presentation by performing a multi-product optimization “state of the union” of sorts – understanding where our audience resided via live polling from a multi-product perspective.
The first question we asked – “Which statement most accurately describes your company?”
Surprisingly, half of the audience has some measurement of multiproduct price impact, while roughly the other half isn’t using analytics at all to determine these impacts. This demonstrates right away that there is a lot of room for advancement within the area of multi-product optimization.
We then wanted to know if organizations felt they had the necessary data to perform multi-product optimization. The second question was – “How would you classify your data readiness for delivering multi-product analytics?”
This answer illustrates that there are common products that are bundled together, for instance home and auto, but other products that are consistently omitted. Only 7% of all respondents feel their data is in prime shape for techniques like multi product optimization. This illuminates the age-old issue of bad data equaling bad insight.
Our third question focused on enabling technologies. Performing any job can be difficult if you don’t have the correct tools, technologies, and processes in place. We asked, “How well does your technology (data management, predictive modeling / analytics, price deployment) support your multi-product pricing objectives?”
Roughly half of the audience provided an average rating of 3 stars, with only 18% providing an above average rating (4 or 5 stars), but 38% providing a below average rating of 1 or 2 stars. This indicates that adopting new technology to better enable the multi-product optimization process is something that US carriers may consider.
Finally, we rounded out the polling by asking about challenges. Specifically, the question was – “What do you believe is your organization’s’ biggest obstacle to performing multi-product optimization?”
Once again, the data monster rears its ugly head. While implementation challenges came as a strong contender, data challenges ended up taking the lead spot. This speaks to the fact that data integration, data management, and data quality are all prominent issues that large carriers still face today.
Earnix then went on to discuss the customer journey, the importance of data householding and analytics, renewal timing, and what carriers need to consider on their multi product optimization checklist. We then discussed the different optimization options – whether parallel, contingent, or joint – as well as their respective benefits, and wrapped with how carriers who weren’t performing multi product optimization could get started today. Attendees hopefully left with the sense that they take tangible steps from a data, analytics, and strategy perspective to get started with multi product optimization.
Real Time Rate Deployment with Governance and Control.
Earnix’s last session focused on facilitating real time rate deployment and featured Dror Pockard of Earnix along with Neal SIlbert from DataRobot. DataRobot is a strong partner of Earnix’s that provides an automated machine learning platform. The session opened with Dror explaining the benefits of real time rate deployment, the importance of governance and control in the rate deployment process, and what an end-to-end pricing and product personalization suite should contain to enable better real-time rate deployment. Neal then spoke to how DataRobot can augment Earnix from a pricing and underwriting perspective, by detailing marketing, underwriting, claims handling, and engagement use cases that feature the automated machine learning that DataRobot provides. Both Dror and Neal wrapped the session by explaining the real time rate deployment workflow and exactly how the two providers work together.
Earnix spoke to over 350 attendees at the conference, delivered three outstanding sessions, gave away lots of cool prizes, and even had some fun while doing it! If you were able to join us at the conference – thank you – and if you weren’t, please visit our website to check out all of our newest content and ideas! If you would like copies of our session presentations, feel free to contact us at marketing@earnix.com.
- Insurance carriers and consumers crave personalization.
- Insurance carriers are speeding their adoption of new technologies and processes, such as multi product optimization, but classic issues still remain.
- Deploying rates in real-time with governance and control is crucial to carrier success.
Personalization. The first session Earnix delivered featured Lucy Kadets of Earnix and Toby Alfred of Digital Transformation Strategies, LLC and was focused on personalizing product packages and offers for insurance consumers. As the session progressed, it was clear that carriers are very much interested in both streamlining and improving the policy bundle and package delivery process from where it stands today. Lucy explained how using analytics to better personalize the coverages, riders, and other variables associated with an auto or home package or bundle would shorten the application process, improve conversions for the brand, and improve the overall consumer experience. Lucy then showed a live demonstration of Earnix’s newest product, Earnix Personalize-it, which helps address the need for analytical personalization in Insurance. Toby then brought Lucy’s portion of the presentation to life by sharing her experience while at Progressive Insurance, where she assisted in creating the “Name your Own Price” tool. This was one of the first instances of early personalization in Insurance and was extremely successful some ten years ago.
New Technologies and Processes. While smartphone usage-based insurance, drone claims, and embedded home insurance were among the topics discussed at CAS RPM this year, Earnix set its sights on having meaningful conversations around the work we are doing in advanced predictive analytics and machine learning. One common technique in insurance that leverages advanced analytics is the performing of multi-product optimization. Bundling home, auto, and other insurance coverages together for the purpose of customer loyalty and retention has been done for a while in insurance, and Earnix offered some very practical advice on how to improve this process even further. During our session on multi-product optimization, Drew Lawyer from Country Financial and I uncovered some interesting insights from our audience.
We began this presentation by performing a multi-product optimization “state of the union” of sorts – understanding where our audience resided via live polling from a multi-product perspective.
The first question we asked – “Which statement most accurately describes your company?”
Surprisingly, half of the audience has some measurement of multiproduct price impact, while roughly the other half isn’t using analytics at all to determine these impacts. This demonstrates right away that there is a lot of room for advancement within the area of multi-product optimization.
We then wanted to know if organizations felt they had the necessary data to perform multi-product optimization. The second question was – “How would you classify your data readiness for delivering multi-product analytics?”
This answer illustrates that there are common products that are bundled together, for instance home and auto, but other products that are consistently omitted. Only 7% of all respondents feel their data is in prime shape for techniques like multi product optimization. This illuminates the age-old issue of bad data equaling bad insight.
Our third question focused on enabling technologies. Performing any job can be difficult if you don’t have the correct tools, technologies, and processes in place. We asked, “How well does your technology (data management, predictive modeling / analytics, price deployment) support your multi-product pricing objectives?”
Roughly half of the audience provided an average rating of 3 stars, with only 18% providing an above average rating (4 or 5 stars), but 38% providing a below average rating of 1 or 2 stars. This indicates that adopting new technology to better enable the multi-product optimization process is something that US carriers may consider.
Finally, we rounded out the polling by asking about challenges. Specifically, the question was – “What do you believe is your organization’s’ biggest obstacle to performing multi-product optimization?”
Once again, the data monster rears its ugly head. While implementation challenges came as a strong contender, data challenges ended up taking the lead spot. This speaks to the fact that data integration, data management, and data quality are all prominent issues that large carriers still face today.
Earnix then went on to discuss the customer journey, the importance of data householding and analytics, renewal timing, and what carriers need to consider on their multi product optimization checklist. We then discussed the different optimization options – whether parallel, contingent, or joint – as well as their respective benefits, and wrapped with how carriers who weren’t performing multi product optimization could get started today. Attendees hopefully left with the sense that they take tangible steps from a data, analytics, and strategy perspective to get started with multi product optimization.
Real Time Rate Deployment with Governance and Control.
Earnix’s last session focused on facilitating real time rate deployment and featured Dror Pockard of Earnix along with Neal SIlbert from DataRobot. DataRobot is a strong partner of Earnix’s that provides an automated machine learning platform. The session opened with Dror explaining the benefits of real time rate deployment, the importance of governance and control in the rate deployment process, and what an end-to-end pricing and product personalization suite should contain to enable better real-time rate deployment. Neal then spoke to how DataRobot can augment Earnix from a pricing and underwriting perspective, by detailing marketing, underwriting, claims handling, and engagement use cases that feature the automated machine learning that DataRobot provides. Both Dror and Neal wrapped the session by explaining the real time rate deployment workflow and exactly how the two providers work together.
Earnix spoke to over 350 attendees at the conference, delivered three outstanding sessions, gave away lots of cool prizes, and even had some fun while doing it! If you were able to join us at the conference – thank you – and if you weren’t, please visit our website to check out all of our newest content and ideas! If you would like copies of our session presentations, feel free to contact us at marketing@earnix.com.
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