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Enhancing Claim Processing Efficiency with Generative AI

Author(s) Goutham Bilakanti
Country India
Abstract The use of Generative AI in claim processing, via diversified intake channels, including emails, faxed submissions, and intake channels that are call-center in nature. Under traditional claim processing, there is always a great amount of manual labor in obtaining, validating, and processing information vis-a-vis claims, which results in inefficiencies and delays. Advanced AI models such as NLP and GANs are used to automate data extraction, detection of anomalies, and decision-making, thereby reducing the processing time and the operational cost of processing claims. Automated intelligence increases precision with fewer human errors, enhanced detection of fraud, and quicker approvals. Not only does the process optimize efficiency but also enhance customer satisfaction through quicker claims settlement. Employing machine learning techniques enables ongoing model enhancement, responding to new claim behaviors and regulatory requirements. Insurance companies and banks can greatly enhance compliance, mitigate risk, and enhance transparency by using real-time AI-powered insights. The article illustrates the transformative effect of Generative AI on making claim processing activities streamlined, scalable, and efficient in many industries.
Keywords AI, claim processing automation, NLP, GANs, fraud prevention, AI workflow, data extraction, machine learning, insurance technology, intelligent document processing.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 3, Issue 1, January 2022
Published On 2022-01-06
Cite This Enhancing Claim Processing Efficiency with Generative AI - Goutham Bilakanti - IJLRP Volume 3, Issue 1, January 2022. DOI 10.5281/zenodo.15196823
DOI https://doi.org/10.5281/zenodo.15196823
Short DOI https://doi.org/g9d64w
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