Authors :
Jayanth Kande
Volume/Issue :
RISEM–2025
Google Scholar :
https://tinyurl.com/mr2vweu4
Scribd :
https://tinyurl.com/3jrx4j7j
DOI :
https://doi.org/10.38124/ijisrt/25jun177
Abstract :
In the dynamic landscape of the insurance industry, leveraging data-driven insights has become pivotal for
improving policy management and strategic decision-making. This study introduces innovative forecasting models
specifically designed for insurance management platforms, aimed at optimizing policy performance, refining risk evaluation,
and enhancing customer engagement (Nguyen, 2017) [1]. By integrating machine learning algorithms with sophisticated
statistical methodologies, these models offer robust predictions of policy dynamics, customer behavior patterns, and claim
likelihoods (Gupta et al., 2018) [2]. The research employs comprehensive analyses on real-world insurance datasets,
showcasing notable advancements in predictive accuracy and operational productivity. These models not only streamline
the decision-making process but also support proactive risk mitigation strategies, enabling insurers to respond swiftly to
emerging trends. Furthermore, the application of predictive analytics facilitates personalized policy offerings, fostering
higher customer satisfaction and loyalty (Roy & Verma, 2020) [3]. The study emphasizes the transformative role of data
forecasting in reshaping insurance operations, driving profitability, and reducing uncertainty in risk-prone environments.
Overall, the proposed approach highlights the potential of advanced analytics to revolutionize policy optimization, making
insurance ecosystems more resilient and adaptive in a competitive market (Gupta et al., 2020) [4].
Keywords :
Anomaly Detection, Machine Learning, Financial Applications, Fraud Prevention, Risk Management.
In the dynamic landscape of the insurance industry, leveraging data-driven insights has become pivotal for
improving policy management and strategic decision-making. This study introduces innovative forecasting models
specifically designed for insurance management platforms, aimed at optimizing policy performance, refining risk evaluation,
and enhancing customer engagement (Nguyen, 2017) [1]. By integrating machine learning algorithms with sophisticated
statistical methodologies, these models offer robust predictions of policy dynamics, customer behavior patterns, and claim
likelihoods (Gupta et al., 2018) [2]. The research employs comprehensive analyses on real-world insurance datasets,
showcasing notable advancements in predictive accuracy and operational productivity. These models not only streamline
the decision-making process but also support proactive risk mitigation strategies, enabling insurers to respond swiftly to
emerging trends. Furthermore, the application of predictive analytics facilitates personalized policy offerings, fostering
higher customer satisfaction and loyalty (Roy & Verma, 2020) [3]. The study emphasizes the transformative role of data
forecasting in reshaping insurance operations, driving profitability, and reducing uncertainty in risk-prone environments.
Overall, the proposed approach highlights the potential of advanced analytics to revolutionize policy optimization, making
insurance ecosystems more resilient and adaptive in a competitive market (Gupta et al., 2020) [4].
Keywords :
Anomaly Detection, Machine Learning, Financial Applications, Fraud Prevention, Risk Management.
