Embracing the power of predictive analytics in insurance: Are your underwriters ready for change?

Predictive models can transform the underwriting role, but successful implementation hinges on underwriter adoption.

In brief

• Predictive modelling and analytics unlock new business growth, increase profitability, improve loss ratios and help ensure future success in the context of our rapidly changing world.

• Predictive analytics present significant data and regulatory complexities to contend with, and the right advanced technological solutions are vital to overcome these hurdles and establish a well-functioning, user-friendly platform.

• Successful implementation requires underwriter buy-in, which can be achieved with thoughtful, consistent change management practices.

Data-driven risk assessment through predictive analytics leads to optimized underwriting speed and pricing accuracy. According to Capgemini’s World Property and Casualty Insurance Report 2024, 83% of insurance executives believe predictive models are very critical for underwriting’s future.

In the past, underwriters relied on historical data to predict future losses, but now we can glean insights from an abundance of real-time data that can unlock new business growth and increase profitability in three key ways:

• Refine risk selection from unstructured data for better insights and fraud detection.
• Pinpoint insurable risks by deriving granular details from previously siloed large data sets. This becomes integral in areas with catastrophic exposures and allows for intra-territorial risk splitting through gaining a better understanding of the individual risk nuances.

• Enable superior pricing by accurately predicting potential losses. Armed with more information, carriers can confidently price risks they would have otherwise declined, widening their markets, writing more business and generating new revenue streams. Underwriters can also initiate pricing changes quicker instead of waiting until the company sustains a significant loss to make adjustments.

The overall predictive analytics market is projected to increase at a 24% compound annual growth rate from 2024 to 2029. With access to advanced technology and a wealth of data, insurers are evolving the underwriter’s role to keep up in today’s volatile risk landscape and ultimately thrive in our rapidly changing world.

The current outlook on predictive models 

Predictive analytics has emerged as a critical tool for P&C carriers but only 27% of insurers currently possess the advanced technology needed to leverage predictive analytics in their underwriting models, according to the Capgemini report. There’s ample room for growth, but implementing predictive analytics isn’t easy and there are significant hurdles to contend with:

• Data challenges
  Predictive analytics require clean, high quality data sources and substantially more data than insurers have had access to in the past. An increased use of third-party data sources to obtain this data is unavoidable, so carriers need to assure the data is diverse, accurate and comprehensive. Data lakes and integrations will be required, and it’s imperative to establish proper security measures to protect private and proprietary information.

• Regulatory complexities
  Predictive analytics for rating purposes could mean more regulatory challenges. Some states may have restrictions, and increased use of artificial intelligence (AI) in models could present longer approval times. Developing relationships with regulatory partners is key to reducing friction and time spent through this process.

• More governance
  A lack of regulatory structure around AI and other new technological capabilities that support predictive analytic implementation hinders adoption due to data security, transparency and interpretability concerns, so stay current on the rapidly changing rules in this new frontier and develop an effective AI governance framework.

Predictive analytics are made possible by an advanced, innovative technological infrastructure that supports an agile platform designed to deliver a wealth of data in an organized manner. To leverage data ecosystems for predictive models, the platform must connect to the data ecosystem to achieve real-time data, have access to secured and scalable unified data lakes and incorporate AI and machine learning (ML) models that generate human-centric insights. Digital underwriting processes also must be streamlined to augment human judgements and enhance cross-function collaboration.

Global professional services firm Aon, for example, partnered with data and analytics provider AbsoluteClimo in 2023 to advance their capabilities for climate and catastrophe predictive modeling. This type of collaboration helps facilitate forward-thinking underwriting and pricing strategies for organizations navigating climate-based perils.

Change management and predictive model implementation

Beyond the external obstacles, insurers will also face internal employee resistance to a more digital way of working. Underwriters may be skeptical of predictive analytics models. While it’s true that predictive modeling can be transformative, a more nuanced approach to change management is necessary.

New predictive models are exceedingly complex, informing decisions through the interpretation of tens of thousands of data points. Carriers will need to meet underwriters where they are by showing them the value of the models and by helping them communicate the modeled decisions. The more transparency insurers provide their underwriters around the models, the better they will earn their confidence.  

Thoughtful, consistent change management will help achieve underwriter buy-in. Underwriters need to understand how their roles will positively evolve with the implementation of predictive analytics. Illustrate how instant access to more robust data will boost workflow efficiency and allow underwriters to make a greater impact to the bottom line.  

Seasoned underwriters will need to move from weighing gut feelings and professional experience to making risk decisions based on their trust in predictive models. Carriers must mindfully introduce predictive modeling at a pace that empowers underwriters to consume the details, understand how it will enhance their roles and use it to their benefit. Predictive analytics models are not meant to replace underwriters, they are developed to inform their decision making. 

Here are three ways to practice impactful change management and foster underwriter buy-in:

1. Involve underwriters early on in the development of models. Their first-hand experience will naturally pave the way to successful models — and their adoption.
   
2. Present data in a consumable, visual way by leveraging an underwriting workbench to support underwriters with only the data they need to make a decision at hand. User-friendly functionalities like red, yellow and green labels or using a 1-5 risk score can help an underwriter quickly triage and prioritize work, determine straight through opportunities or auto decline.  
   
3. Conduct regular meetings with both the analytics teams and the underwriting teams together. Review the data and uncover how it is affecting underwriter workflow. Take every opportunity to highlight how predictive models are impacting the profitability of their portfolios. Monitor underwriter model adoption and provide specialized AI and ML trainings when required.

In conclusion

Predictive models enhance underwriting by combining data insights for a more personalized, efficient and seamless experience. Better data enables carriers to more effectively price risk, maintain favorable loss ratios and generate new revenue streams by being able to accept risks that would’ve otherwise been denied previously.  

Change management is key for adoption, enhanced underwriter efficiency and operational excellence. Faster and more accurate underwriters empowered by predictive analytics are pivotal to a carriers’ future, and the underwriter transformation deserves the technology, time, energy and resources needed to ensure its success. Are you ready to leap into the future of underwriting?