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EXL’s Insurance LLM transforms claims and underwriting
As insurance companies embrace generative AI (genAI) to address longstanding operational inefficiencies, they’re discovering that general-purpose large language models (LLMs) often fall short in solving their unique challenges.
Claims adjudication, for example, is an intensive manual process that bogs down insurers. Medical professionals can spend long hours reading upwards of 1,000 pages of medical records and other documents for a single claim. Then they have to synthesize and interpret all this complex information to facilitate a determination.
Understandably, lapses in concentration are common, and they can compromise the quality of the settlement. In addition, the quality of this overall work can vary significantly based on an employee’s experience.
The sheer volume of data, and the amount of time it takes to absorb it all, makes for an inconsistent, error-prone process. And while generic LLMs are powerful, they lack the precision, domain expertise, and privacy assurances needed to tackle the problem completely.
Recognizing this gap, EXL launched its EXL Insurance LLM, whose industry-specific AI capabilities empower insurers to streamline claims adjudication, enhance underwriting processes, and more. Leveraging NVIDIA’s AI platform, the EXL Insurance LLM is a purpose-built solution to the industry’s unique problems around claims adjudication and underwriting. Because it’s trained on proprietary industry data, the model provides specific, accurate, and concise responses that enhance insurers’ efficiency and improve the customer experience.
How the EXL Insurance LLM works
The EXL Insurance LLM eliminates much of the heavy lifting for practitioners by ingesting all of the claim documentation and providing a summary of the specific information needed to adjudicate. This can only happen because the model is powered by NVIDIA’s AI technology and fine-tuned with proprietary insurance data.
NVIDIA’s technology reduces training time from months to days, filters out junk data to improve accuracy, and enhances security by preventing the unauthorized transmission of sensitive information. This allows the model to accurately and efficiently handle industry-specific language patterns, terminologies, and processes in a way that general-purpose models simply can’t—because they don’t have access to that proprietary data. And it does so without the errors and variances in quality that can happen in manual reviews.
Real-world examples and benefits
The EXL Insurance LLM is transforming the industry in other ways as well. The model aggregates and reconciles hundreds of thousands of de-identified medical records, claims histories, call logs, and more to help underwriters make more informed decisions. It also:
- Improves regulatory adherence by performing compliance checks
- Identifies errors, inconsistencies, and insights buried in lengthy documents
- Provides accurate, repeatable results without the variation that is common among human reviewers.
The return on investment is real: The EXL Insurance LLM lowers claim indemnity costs, reduces claims leakage, and leads to faster settlements. Practitioners, clinical professionals, and legal staff have used it to increase their efficiency by 30% in the near term and up to 75% in the medium term. In internal studies, the model achieved a 30% improvement in accuracy on insurance tasks over top general-purpose models, and it offers 30% lower costs.
Register for the upcoming virtual event, AI in Action: Driving the Shift to Scalable AI, to learn how the EXL Insurance LLM can transform your business.
