Enhancing Fine-Tuning Techniques of Large Language Models (LLMs) in the Insurance Industry

The landscape of the insurance industry has undergone rapid transformation in recent decades. A driving force behind this transformation is the exponential growth of data volumes, necessitating effective processing to unlock their full potential. This surge has led to the development of advanced processing technologies, much like how AI is harnessed to transform the insurance sector, leading to improvements in customer experiences, risk assessment, operations, and fraud detection. Insurance companies can leverage large language models to employ big data analytics, gathering and analyzing structured and unstructured data from diverse sources. This process enhances risk assessment accuracy and enables personalized offerings. By comprehending customer behavior, insurers create customized policies and targeted marketing, elevating customer relationships and loyalty. The adeptness of big data in swiftly spotting unusual patterns aids fraud detection and loss prevention. Predictive analytics anticipates customer needs, enhancing service quality and satisfaction. Additionally, big data optimizes claims processing, leading to quicker response times. The proposed research is aimed at developing automated RL-based innovative fine-tuning strategies tailored to address the above distinctive challenges and the one within the insurance industry.

Faculty Supervisor:

Hadis Karimipour

Student:

Partner:

Munich Re

Discipline:

Engineering

Sector:

Finance and Insurance

University:

University of Calgary

Program: