AI-Based Intelligent Insurance Agent Using Retrieval-Augmented Generation

Sakshi Alamkhane; Aditi Anarse; Mahek Shaikh; Nandini Chavan; Rahul Korke

doi:10.65521/ijeecs.v15i1S.2957

Authors

Sakshi Alamkhane Department of Computer Engineering, Genba Sopanrao Moze College Of Engineering, Balewadi, Pune-45
Aditi Anarse Department of Computer Engineering, Genba Sopanrao Moze College Of Engineering, Balewadi, Pune-45
Mahek Shaikh Department of Computer Engineering, Genba Sopanrao Moze College Of Engineering, Balewadi, Pune-45
Nandini Chavan Department of Computer Engineering, Genba Sopanrao Moze College Of Engineering, Balewadi, Pune-45
Rahul Korke Department of Computer Engineering, Genba Sopanrao Moze College Of Engineering, Balewadi, Pune-45

DOI:

https://doi.org/10.65521/ijeecs.v15i1S.2957

Keywords:

AI Agent Insurance Technology RAG Chatbot NLP

Abstract

The insurance industry faces significant challenges in providing efficient, accurate, and personalized customer support due to the complexity of policies and reliance on human agents. This research presents an AI-Based Intelligent Insurance Agent that utilizes Retrieval-Augmented Generation (RAG) to deliver context-aware and reliable responses. The system integrates natural language processing, machine learning, and database-driven retrieval to assist users in understanding insurance policies, recommending suitable plans, and supporting claim-related queries. It offers both text and voice interaction, ensuring 24/7 accessibility and improved user engagement. The methodology involves retrieving relevant data from a structured database and generating responses using an AI model. The proposed system reduces operational costs, enhances accuracy, and improves customer satisfaction. The results demonstrate that the system provides faster and more consistent support compared to traditional methods. This research highlights the potential of AI in transforming the insurance sector through automation and intelligent decision-making.

AI-Based Intelligent Insurance Agent Using Retrieval-Augmented Generation

Authors

DOI:

Keywords:

Abstract

Downloads

Published

How to Cite

Issue

Section

Most read articles by the same author(s)

Similar Articles

Quick Links

For Authors

For Reviewers

Contact Us

Similar Articles

A Multi-Feature AI Framework for Sentiment Analysis and Business Intelligence

A Comprehensive Review of Trusted Cloud-Enabled IoT Networks Using Blockchain and Siamese Heterogeneous Convolutional Neural Networks

A Survey of Methods and Architectures for Segmentation and Classification of White Blood Cancer Cells in Bone Marrow Microscopic Images Using Deep Kronecker Neural Networks

Deep Learning and Optimization Approaches in Brain MRI Image Classification for Cancer Detection Using Transformer and Group Parallel Axial Attention with Quantum Self-Attention: A Review

A Comprehensive Review of Segmentation and Classification of Renal Tumors Using EfficientNet-Based U-Net and Epistemic Neural Networks

Artificial Intelligence Techniques for Optimized Graph Transformer with Alpine Skiing Optimization: Improving Initiative IoT in Human Resource Management by Predicting Workers’ Stress: Trends and Challenges

A Survey of Methods and Architectures for Multi-Attack Detection using Forensics and Coherent Integrated Photonic Neural Networks-Based Prevention for Secure IoT-MANETs

A Comprehensive Review of Hardware Efficiency of CNN Architecture Design using Decoder-Based Low Power Approximate Multiplier and Error Reduced Carry Prediction Approximate Adder for MNIST Dataset Classification

The Neural Revolution: A Review of Brain-Machine Interfaces and Neuralink’s Contribution

Artificial Intelligence Techniques for Enhancing Air Pollution Detection Accuracy and Quality Monitoring Using Pyramidal Convolution Split-Attention Networks and IoT: Trends and Challenges