Authors : Jayanth Kande

Volume/Issue : RISEM–2025

Google Scholar : https://tinyurl.com/4222dazk

Scribd : https://tinyurl.com/3k3hw2kk

DOI : https://doi.org/10.38124/ijisrt/25jun179

Abstract : The insurance industry encounters multiple challenges, including inefficiencies in risk assessment, fraudulent claims, and delays in policy processing. To address these issues, this paper proposes a machine learning-driven predictive analytics model that enhances decision-making and operational efficiency. The model utilizes historical data to detect patterns, optimize workflow automation, and improve claim evaluation. Supervised machine learning models such as decision trees, random forests, and deep learning techniques are applied for fraud detection, streamlined claim processing, and enhanced customer risk assessment. By leveraging data-driven insights, the proposed approach minimizes manual intervention, accelerates decision-making, and improves the accuracy of risk predictions. Experimental results indicate significant improvements in fraud identification, reduced claim processing time, and increased decision reliability. The integration of advanced predictive techniques enables proactive risk mitigation, leading to more efficient insurance operations. Additionally, the model’s adaptability allows for scalability across various insurance domains, further enhancing its applicability. The findings underscore the potential of machine learning in transforming insurance workflows by reducing operational bottlenecks and strengthening fraud prevention mechanisms (Doe & Smith, 2023) [1].

Keywords : Predictive Analytics, Machine Learning, Insurance Workflow, Fraud Detection, Decision-Making.