International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Real-Time Risk Assessment in Insurance: A Deep Learning Approach to Predictive Modeling

Author(s) Padmaja Dhanekulla
Country United States
Abstract The insurance industry is experiencing a paradigm shift driven by the integration of big data analytics and machine learning technologies. This article presents a comprehensive framework for implementing advanced analytics in insurance operations, focusing on risk assessment optimization and predictive
modeling applications. Building upon established actuarial methods, this article demonstrates how
modern machine learning algorithms and real-time data processing enhance underwriting accuracy and
claim processing efficiency. This article examines the integration of multiple data sources, including IoT
sensors, telematics, and external databases, highlighting their collective impact on risk assessment
precision. This analysis indicates significant improvements in underwriting accuracy and claims
processing speed through the implementation of automated analytics workflows. This article also
addresses critical challenges in data quality, regulatory compliance, and integration complexity, providing
strategic recommendations for insurers pursuing digital transformation. This article contributes to the
growing body of knowledge in insurance analytics by establishing a scalable framework for big data
integration while maintaining regulatory compliance and operational efficiency
Keywords Big Data Analytics, Machine Learning Implementation, Predictive Modeling, Insurance Digital Transformation, Insurance Risk Assessment.
Field Computer
Published In Volume 6, Issue 6, November-December 2024
Published On 2024-11-30
Cite This Real-Time Risk Assessment in Insurance: A Deep Learning Approach to Predictive Modeling - Padmaja Dhanekulla - IJFMR Volume 6, Issue 6, November-December 2024. DOI 10.36948/ijfmr.2024.v06i06.31710
DOI https://doi.org/10.36948/ijfmr.2024.v06i06.31710
Short DOI https://doi.org/g8sg6t
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