A Large Language Model (LLM) built specifically for the insurance industry outperforms leading pre-trained models on accuracy across wide range of claims and underwriting related tasks, according to developer EXL.

According to Gartner, more than 50% of the GenAI models that enterprises use will be specific to either an industry or business function by 2027 - up from approximately 1% in 2023.

In internal studies, the EXL Insurance LLM achieved a 30% improvement in accuracy on insurance tasks, surpassing top pre-trained models, such as OpenAI GPT4, Claude and Gemini.

It was built by EXL AI Labs, using the full-stack NVIDIA AI platform, to support critical claims and underwriting-related tasks, such as claims reconciliation, data extraction and interpretation, question-answering, anomaly detection and chronology summarization. 

EXL Insurance says the LLM was developed to address the highly specialized needs of the insurance industry, which has struggled to leverage off-the-shelf, general LLMs that lack fine-tuning of private insurance data and domain-specific understanding of business process operations.

Generic LLMs also fail to address the nuanced challenges faced by insurance companies during claim adjudication, leading to inefficiencies, high indemnity costs, claims leakage, longer settlement timelines, and increased compliance risks. By focusing exclusively on insurance-related tasks, EXL has incorporated its deep knowledge of the insurance industry and highly tailored proprietary data. 

This level of specialization has become critical for ensuring accuracy, reducing cost and improving consistency in industry-specific AI applications.

“With 25 years of expertise in processing medical records data for bodily injury, workers' compensation, and general liability claims, EXL has developed curated data sets with domain-specific tagging, labelling, and question and answer pair creation for claims adjudication to fine-tune our models,” said Anand “Andy” Logani, EXL’s executive vice president and chief digital officer.

Specific tasks supported by the EXL Insurance LLM include the following:

Structured and Unstructured Data Ingestion: EXL Insurance LLM is able to aggregate and reconcile hundreds of thousands of de-identified medical records, claims histories, hand-written notes, call logs, and other claims and underwriting-related information.

Contextual Classification and Triaging: Data and insights extracted using the LLM are automatically categorized and fed into a wide range of core functions, ranging from claims adjudication to provider engagement to payment integrity to customer service functions.

Conversations and Insights from Data: Insights, question-answering and summary data drawn from the LLM empower faster, more accurate negotiations with providers, more robust assessment of anomalies and inaccurate payments and more personalized, real-time conversations with customers.

For more information about the EXL LLM for Insurance, visit here.

www.exlservice.com