Artificial Intelligence and Big Data in Insurance Risk Assessment
Affiliation
| DOI | https://doi.org/10.67313/stanzaleafbookchapter.2026.16 Registered |
| Published In | INSURING EMERGING RISKS ADDRESSING PANDEMICS CYBER ATTACKS AND POLITICAL ISSUES |
| Book Editors | Ramya A J. Deepak Kumar K.M. Anitha R. Srikala J. Chandhini |
| ISBN | 978-81-687720-2-1 |
| Pages | 101-104 |
| Publication Date | 17 July 2026 |
| Publisher | Stanzaleaf Publication |
Abstract
The insurance industry is undergoing a significant transformation with the adoption of Artificial
Intelligence (AI) and Big Data Analytics in risk assessment and decision-making. Traditional risk
assessment methods often rely on historical data and manual evaluation, which may be time
consuming and less effective in identifying emerging risks. AI, combined with Big Data
technologies, enables insurers to process vast amounts of structured and unstructured data from
multiple sources, including customer records, telematics, wearable devices, social media, Internet
of Things (IoT) sensors, and financial transactions. These advanced technologies facilitate
accurate risk prediction, personalized premium pricing, fraud detection, efficient claims
management, and enhanced customer service. This paper examines the role of Artificial
Intelligence and Big Data in modern insurance risk assessment by exploring their applications,
benefits, challenges, and future prospects. The study adopts a descriptive research methodology
based on an extensive review of recent literature, industry reports, and case studies. The findings
reveal that AI-powered predictive analytics and machine learning algorithms significantly improve
underwriting accuracy, reduce operational costs, detect fraudulent claims in real time, and enable
insurers to offer customized insurance products based on individual risk profiles. However, the
widespread implementation of these technologies also raises concerns regarding data privacy,
cybersecurity, algorithmic bias, ethical decision-making, and regulatory compliance. The study
concludes that Artificial Intelligence and Big Data have become indispensable tools for the
insurance sector, enhancing operational efficiency, improving risk management, and strengthening
customer trust. To maximize their potential, insurance companies should invest in robust data
governance frameworks, transparent AI models, skilled human resources, and strong cybersecurity
measures while ensuring compliance with evolving regulatory standards. The integration of AI and
Big Data is expected to redefine insurance risk assessment, making it more predictive, customer
centric, and resilient in the digital era.
Keywords
Artificial Intelligence, Big Data Analytics, Insurance, Risk Assessment, Machine Learning, Predictive Analytics, Underwriting, Fraud Detection, InsurTech, Digital Transformation.