The traditional workers’ compensation underwriting process involves numerous manual processes, siloed data, and complex risk assessments. However, advancements in Generative AI, predictive modeling, and large language models (LLMs) can be leveraged to significantly streamline this process, reducing pain points and increasing efficiency.

Frictionless Data Collection and Integration

 
One of the biggest challenges in workers’ compensation underwriting is gathering and analyzing diverse sources of data, such as industry classifications, OSHA reports, SOS checks, and business entity information. AI-powered platforms and LLMs can almost instantly pull data from:

• OSHA (Occupational Safety and Health Administration) records to assess safety violations and incident reports
• NAICS (North American Industry Classification System) codes to classify businesses accurately
• Relevant legal information to ensure compliance
• Company websites for insights on workplace policies, safety protocols, and business operations
• Claims histories to analyze past trends and identify high-risk patterns
• Social media websites to see social sentiments about a business

By automating this data collection and integration process, underwriters can spend less time on data mining and more time on strategic decision-making.

Predictive Modeling for Risk Assessment

 
AI-powered predictive models can analyze large datasets to predict claims propensity, claims severity, loss amounts, and suggested premiums. By using historical claims data, workplace injury trends, and payroll information, predictive models can forecast future risk more accurately.

LLM-Powered Risk Insights

 
Large language models (LLMs) are capable of processing and understanding vast amounts of both structured and unstructured data. This allows LLMs to be leveraged to generate insights from multiple sources simultaneously and put business entity information and risk insights at underwriters’ fingertips, instantly. An LLM that pulls information from OSHA, NAICS, legal frameworks, company websites, and claims data can:

• Automatically identify inconsistencies in a company’s reported safety measures (e.g., OSHA violations)
• Provide underwriters with contextualized insights into the specific risks associated with a business

By automating this analysis, underwriters can make data-driven decisions faster, reducing the risk of human error, while improving profitability.

Faster, More Efficient Underwriting Processes

 
Gen-AI, predictive modeling, and LLMs can be leveraged to complement underwriters’ professional judgement and streamline decision making. Instead of manually searching the web and transcribing physical documents, these next-gen systems can read both structured and unstructured data to arm underwriters with all the information they need in a single platform, in real-time. This means:

• Faster underwriting processes, reducing the time it takes to issue policies
• Less time spent on mundane manual data mining and data entry
• More accurate risk predictions based on real-time data
• Faster policy issuance, leading to greater customer satisfaction

Transforming Workers’ Compensation Underwriting with Technology

 
The integration of AI, predictive modeling, and LLMs marks a turning point in workers’ compensation underwriting. These technologies address many of the long-standing challenges that underwriters face, such as employee misclassification, inconsistent data, and claims fraud. By leveraging these tools, underwriters can make more informed decisions, price policies more accurately, and improve overall efficiency without requiring any additional resources.

At Mulberri, we are leading the charge in insurtech innovation by incorporating AI and LLM capabilities into our underwriting solutions. By automating data collection, risk assessment, and claims analysis, we help insurers and businesses navigate the complexities of workers’ compensation with greater ease. Ready to see how our tech-driven solutions can help you? Get in touch with us today!