Underwriting 2.0: Extending Policyholder Insights Beyond the Roof or Vehicle
The evolution in the sophistication of risk assessment has made considerable progress in supporting underwriters in what they do best – assess risk and do so efficiently and more accurately. It’s of no surprise that the insurers who are taking the necessary steps to get ahead in predicting risk more accurately, efficiently, and earlier will be the winners in the long-term as the P&C industry continues to evolve and change.
In 2021, McKinsey reported the P&C insurance sector “long-struggled with challenging fundamentals, where intense competition erodes the value across the board, and only a small number of sector leaders turn a profit. Where some personal lines insurers employed basic risk-segmentation models and underwriting criteria based on rules accumulated over time, but few were implementing advanced techniques, such as machine learning (ML) or generalized linear models (GLM) bolstered by ML insights.”
The top-performing personal lines insurers were those who built advanced data and analytics underwriting capabilities. They were the ones who experienced loss ratios improvement by 3 to 5 points and new business premiums increased 5-10%; all of which were facilitated by digitized underwriting.
Fast-forward to 2024, and the insurance industry has undergone a significant transformation. Carriers no longer need to build advanced technology in-house, thanks to third-party partnerships that have made cutting-edge tools accessible to a broader range of insurers. In just a few short years, many have already experienced the tangible benefits of integrating advanced analytics, driven by the expansion of machine learning, generative AI, and other innovative tools.
However, there remains an opportunity for insurers to take their capabilities even further by leveraging powerful individual-level data, which has the potential to significantly enhance an underwriter’s accuracy and overall effectiveness.
“According to the McKinsey study, companies who incorporated advanced analytics earlier into underwriting processes were those who were experiencing a 20% increase in operational efficiency and 15% reduction in claims costs, while Deloitte discovered 60% of insurance carriers believed advanced analytics was going to be a key driver of marketplace competitive advantage.”
Benefits in Focusing on the Policyholder Rather than Just the Vehicle or House
Behavior reflects choice, and these choices can indicate a person’s likelihood to engage in risky behavior. Traditionally, insurers relied on surveys, historical data, and factors like claims history and credit scores to assess risk, focusing mainly on the property or vehicle. However, these methods only offer a partial view of the individual. Traditional variables fail to account for future choices, which are crucial for accurately predicting risk.
Essentially, the more accurate underwriting becomes, the greater the chance an insurance company can achieve underwriting profitability. Insurers who tap into the “once unimaginable volumes of third-party data” enhance “underwriting excellence with segment-specific data that combines with their knowledge of underlying risks to inform the highest-impact use cases.” (McKinsey, 2021)
Using Behavioral Risk Assessments for Improving Underwriting Workflow
- Straight-through-processing vs. Underwriting Review Workflow: Predicting the future choice for risk assessment empowers underwriters with a new degree of insight even before applicant documentation is received. Pinpoint’s advanced Loss Predictions has the power to cut down on manual processes and enables underwriters to streamline processes to identify upfront the complex risk to those that are the simple risks. This streamlining of processes during triage allows simple risks to shift to straight-through-processing where underwriters never see those applications, so they can focus on what they do best – which is focus on only the most complex risks.
- Application Submission Workflow: Individual-level risk assessment applied at the top of the funnel empowers pre-underwriting and prospect loss modeling, where analytic models help inform key decisions and reduce the necessity for an underwriter to become involved in the number of applicant submissions. Underwriters are then able to focus their craft on a smaller portion of the book of business, reducing team inefficiencies and increasing the ability to achieve underwriting profitability through a reduction on the use of overall underwriting resources.
Powerful Prediction Leads to Precise Underwriting
Incorporating individual-level data into risk underwriting offers significant benefits for P&C insurers. It enables carriers to identify profitable customers early and tailor services to each policyholder. Underwriting teams can focus on complex cases, increasing straight-through-processing rates. Studies, including those by McKinsey, show up to a 95% increase in policies processed without underwriting involvement, highlighting the transformative impact of advanced analytics. As the industry evolves, embracing these tools is crucial for protecting customers’ dreams while boosting underwriting profitability.
Pinpoint Predictive provides P&C insurance carriers with individual-level risk predictions to help identify risk at the earliest possible point so insurers can optimize business processes based in informed decisioning on claims, underwriting, marketing, renewal and more. Contact the team at info@pinpoint.ai to learn more.
