Leveraging Artificial Intelligence for Anticipatory Risk Control in Workers' Compensation
In the rapidly evolving insurance landscape, the use of generative AI is making a significant impact. One such example can be seen at Midwest Employers Casualty, where the Assistant Vice President of Data Science hails from W. R. Berkley Corporation.
Generative AI, a powerful tool, is revolutionising the way insurers process claims. By effectively handling unstructured data, insurers can uncover patterns and trends that were previously hidden. This leads to a reduction in the time to insight and an enhancement of worker safety protocols.
Unstructured data, such as claim notes, accident descriptions, scanned documents, images, audio, and video, are often overlooked yet valuable sources of insurance data. Generative AI processes this data quickly and cost-effectively, leading to key insights much faster than traditional methods.
For instance, a retail account benefited from this technology, as specific equipment that employees were repeatedly tripping over was identified and shared with the risk management team.
However, it's crucial to exercise caution when working with unstructured insurance data. Regulatory, data privacy, and information security policies must be strictly followed to ensure ethical and compliant practices.
The use of generative AI also addresses the issue of inaccurate injury coding. A study found that more than 30% of claims did not have valid job class coding, and 25% lacked a free-text occupational description. Generative AI models can significantly reduce the number of claims with missing or inadequate injury coding.
In a remarkable case, the task of manually correcting injury coding for 1,000 claims, which would have taken several days spread across weeks, was completed in a matter of hours for less than $100.
Moreover, generative AI models excel at processing short passages of text and following simple instructions. They were used to examine claim notes to identify key attributes related to the injury, such as whether it was avoidable or could have been made less severe with improved safety measures.
A study found that 15% of all causes of injury in a dataset of nearly 4 million claims are missing or coded as miscellaneous. Generative AI helped uncover a previously unknown seasonality in a specific category of injuries.
While AI-generated insights are valuable, they must be carefully reviewed and validated to avoid misguided conclusions and poor decisions. A balanced approach that combines AI efficiency with expert judgment is essential for optimising claims handling and risk management strategies.
In conclusion, the insurance industry has undergone a profound transformation over the past decade, driven by advancements in machine learning, artificial intelligence, and generative AI technologies. The adoption of these technologies is not only making processes more efficient but also enabling insurers to make data-driven decisions that enhance worker safety and overall business operations.
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