What are the best ways to use predictive modeling for workers' compensation underwriting?

1 Data sources and quality

The first step to use predictive modeling effectively is to gather and process high-quality data from various sources. Data sources can include internal data, such as historical loss and premium information, policyholder characteristics, and claims experience, as well as external data, such as industry benchmarks, economic indicators, and social media sentiment. Data quality is crucial for ensuring the accuracy and reliability of the models, so underwriters need to check for errors, inconsistencies, and outliers, and clean, standardize, and enrich the data as needed.

2 Model development and validation

The next step is to develop and validate the predictive models that will help underwriters evaluate and price the risks. There are different types of models that can be used, such as linear regression, logistic regression, decision trees, neural networks, and machine learning. The choice of model depends on the objective, the data availability, and the complexity of the problem. Underwriters need to test and compare different models to find the best fit for their data and goals, and validate the models using techniques such as cross-validation, back-testing, and sensitivity analysis.

3 Model implementation and integration

The final step is to implement and integrate the predictive models into the underwriting workflow and system. This means that underwriters need to use the models to generate risk scores, ratings, and recommendations for each policy or account, and incorporate them into their decision-making process. The models should also be integrated with the existing underwriting software and platforms, so that underwriters can access and update the data and models easily and securely. The models should also be monitored and reviewed regularly, to ensure that they are performing well and reflecting the changing market conditions and customer needs.

4 Benefits for insurers

Predictive modeling for workers' compensation underwriting can bring numerous benefits to insurers, such as enhancing risk segmentation and differentiation, improving pricing accuracy and competitiveness, reducing underwriting costs and time, and increasing customer retention and satisfaction. Through predictive modeling, insurers can identify the most profitable and least profitable segments, estimate expected losses and claims more accurately, automate data collection and analysis, offer more customized products and services, and deliver better value and outcomes.

6 Challenges and best practices

Using predictive modeling for workers' compensation underwriting is not without challenges, such as data availability and privacy, model complexity and explainability, and regulatory compliance and ethical issues. To overcome these challenges, underwriters need to collaborate with data scientists, actuaries, and other experts, engage with customers and stakeholders, and update and refine the models. This will help leverage their skills and knowledge, ensure the quality and validity of the models, understand customer needs and expectations, provide relevant information and insights, keep models current and relevant, and incorporate new data and feedback.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?