March 10, 2021
Covering for a Gap in Workers Comp Data
by John Henry
OSHA inspections provide key data for workers' comp underwriters -- but are down 48% in the pandemic. What to do?
What happens when a key data source becomes less available, reducing carriers’ ability to evaluate risk? This has happened during the pandemic in workers’ compensation.
In workers’ compensation, OSHA is one of the top data sources that underwriters use. In particular, underwriters will look at OSHA inspections and violations to measure some aspects of the risk.
Here at Convr, our focus has been to help carriers with the right insights at the right time for better decision-making, and we found, using a vast data pool, that planned inspections dropped 48% in 2020.
One reason is fewer claims; as operational capacity was reduced or suspended for many industries in 2020, workers’ compensation claims dropped by over 20%. As accidents declined, inspections that normally would have followed weren’t needed. In addition, OSHA reduced the number of planned inspections for the safety of their inspectors.
The reduction in inspections has led to a lack of reliable information for workers’ compensation carriers to evaluate businesses — but this is where technology comes in. With the help of artificial intelligence and advanced analytics, carriers can still determine the risk of a business by looking at past patterns.
These past patterns include types of structured and unstructured information that data scientists refer to as “features” in machine learning models. Often, significant features are high-dimensional nonlinear combinations of company and property characteristics, such as the size of the business, the year it was established and prior violations. Other features include social media information and product and services data.
See also: 9 Months on: COVID and Workers’ Comp
Applying AI to our data lake, which is informed by over 2,000 data sources, Convr has determined that, in place of the normal volume of OSHA inspections, carriers can use a workplace safety model to accurately quantify risk. A workplace safety model consists of a machine learning model that predicts how safe a workplace will be based on OSHA data and the different data sources mentioned above.
Companies labeled as the riskiest 10% by Convr’s proprietary workplace safety risk scoring model observed three times as many future violations as those labeled as the median risk.
COVID-19 has proven that circumstances can change unexpectedly, and carriers have to become adaptable and flexible enough to implement alternative solutions to minimize the impact. Advanced AI models, like the one Convr has created to quantify workplace safety, hold tremendous value for carriers, enabling them to better understand risks even when traditional sources of information are limited or unavailable.
When armed with technological advancements such as these, carriers are equipped with the right tools for better decision-making and optimal underwriting results.