Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Jangid, Harsh; Kannan, M.; Dua, Arush; Jaiswal, Kush; Singh, Gurinder; Agrawal, Rayan Title A name given to the resource Prediction of health insurance premium using bidirectional long short-term memory network with local interpretable model-agnostic explanations Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Artificial Intelligence, Computational Intelligence, and Inclusive Technologies: Proceedings of International Conference on Artificial Intelligence, Computational Intelligence, and Inclusive Technologies (ICRAIC2IT 2025);pp.621-630 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003740100-77" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003740100-77</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105030796312?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105030796312?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Jangid H., Department of Computer Science, CHRIST University, Bengaluru, India; Kannan M., Department of Computer Science, CHRIST University, Bengaluru, India; Dua A., Department of Computer Science, CHRIST University, Bengaluru, India; Jaiswal K., Department of Computer Science, CHRIST University, Bengaluru, India; Singh G., Department of Computer Science, CHRIST University, Bengaluru, India; Agrawal R., Department of Computer Science, CHRIST University, Bengaluru, India Description An account of the resource This research proposes an application of deep learning techniques towards the prediction of insurance premiums using ConvLSTM, BI-LSTM, and CNN-LSTM models. Nowadays, Insurance is becoming more sophisticated, there is a need for better models that predict premiums so that risk factors that can be properly valued. The aim of this study is to improve the accuracy and reliability of insurance premium prediction using deep learning methods. The main challenge is the shallow traditional models, whose capturing of temporal dependencies is ineffective and results are not explainable resulting in very few stakeholders having any trust to the predictions. To solve this, this study compared three models: ConvLSTM model, BI-LSTM and CNN LSTM. Of these, the BI-LSTM model was the most effective because it was able to learn bidirectional sequential patterns. These patterns were enhanced using L2 regularization, dropout and dense layers to improve generalization. The dataset used comes from a Kaggle repository, which contained actual insurance data incorporating age, BMI, region and smoking as attributes. Results showed that BI-LSTM had performed the best as compare to other models in terms of accuracy and loss minimization. Important findings highlighted features such as age, smoking, and BMI as pivotal to estimating premiums. Also, to make the model explainable, we incorporated Explainable AI using LIME which delivers interpretable explanations by showing and visualizing the most important features for single predictions. 2026 selection and editorial matter, K. V. Sambasivarao, and Anasuya Sesha Roopa Devi Bhima; individual chapters, the contributors. All rights reserved. Subject The topic of the resource Deep learning; Health insurance; LSTM; Machine learning; XAI Publisher An entity responsible for making the resource available Taylor and Francis Relation A related resource ISBN: 978-104089003-5; 978-104124091-4; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online