AI-POWERED INSURANCE CLAIM PREDICTION

This project presents an intelligent Insurance Claim Prediction System that leverages artificial intelligence to enhance the efficiency and accuracy of claim processing. Traditional insurance systems are often slow, manual, and prone to errors, leading to delays and poor user experience. To address these challenges, the proposed system integrates a chatbot interface with voice-to-text technology, Natural Language Processing (NLP), and a Long Short-Term Memory (LSTM) deep learning model. Users can submit claim details through voice or text, which are processed and transformed into structured data using NLP techniques. The LSTM model then analyzes the data to predict claim approval probability, assess risk levels, and detect potential fraud. The chatbot provides real-time responses, improving user interaction and accessibility. Overall, the system automates the claim process, reduces processing time, enhances prediction accuracy, and offers a scalable, userfriendly solution for modern insurance services. This project focuses on building an AI-based Insurance Premium Claim Prediction system. Normally, insurance policies are provided for fixed periods like 5, 10, or 20 years. Customers can claim the full insured amount only after completing the policy term. However, sometimes customers may need to claim the insurance amount before the maturity period due to financial emergencies. Our system predicts the estimated claim amount if a customer wants to withdraw early. By using machine learning models, the system analyzes policy duration, paid premiums, age, risk factors, and historical data to calculate a fair settlement amount. This helps customers understand their early claim value and helps insurance companies make accurate and risk-controlled decisions.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-04-07
DOI
https://doi.org/10.5281/zenodo.19451982
Primary Topic
Imbalanced Data Classification Techniques
Type
article
Field-Weighted Citation Impact
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article

AI-POWERED INSURANCE CLAIM PREDICTION

IJDIM
Zenodo (CERN European Organization for Nuclear Research)
Imbalanced Data Classification Techniques
article

AI-POWERED INSURANCE CLAIM PREDICTION

IJDIM
article en

Abstract

This project presents an intelligent Insurance Claim Prediction System that leverages artificial intelligence to enhance the efficiency and accuracy of claim processing. Traditional insurance systems are often slow, manual, and prone to errors, leading to delays and poor user experience. To address these challenges, the proposed system integrates a chatbot interface with voice-to-text technology, Natural Language Processing (NLP), and a Long Short-Term Memory (LSTM) deep learning model. Users can submit claim details through voice or text, which are processed and transformed into structured data using NLP techniques. The LSTM model then analyzes the data to predict claim approval probability, assess risk levels, and detect potential fraud. The chatbot provides real-time responses, improving user interaction and accessibility. Overall, the system automates the claim process, reduces processing time, enhances prediction accuracy, and offers a scalable, userfriendly solution for modern insurance services. This project focuses on building an AI-based Insurance Premium Claim Prediction system. Normally, insurance policies are provided for fixed periods like 5, 10, or 20 years. Customers can claim the full insured amount only after completing the policy term. However, sometimes customers may need to claim the insurance amount before the maturity period due to financial emergencies. Our system predicts the estimated claim amount if a customer wants to withdraw early. By using machine learning models, the system analyzes policy duration, paid premiums, age, risk factors, and historical data to calculate a fair settlement amount. This helps customers understand their early claim value and helps insurance companies make accurate and risk-controlled decisions.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 5%
Imbalanced Data Classification Techniques
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