Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/120531
Title: Big data analytics application in the Indian insurance sector
Other Titles: Big data analytics in the insurance market
Authors: Dahiya, Manju
Sharma, Shikha
Grima, Simon
Keywords: Insurance -- Data processing
Insurance companies -- Data processing
Big data -- India
Insurance companies -- India
Issue Date: 2022
Publisher: Emerald Publishing Limited
Citation: Dahiya, M., Sharma, S., & Grima, S. (2022). Big data analytics application in the Indian insurance sector. In K. Sood, B. Balusamy, S. Grima, P. Marano (Eds.), Big data analytics in the insurance market (pp. 145-164). United Kingdom: Emerald Publishing Limited
Abstract: Introduction: Big data in the insurance industry can be defined as structured or unstructured data that can affect the rating, marketing, pricing, or underwriting. The five Vs of big data provide insurers with a valuable framework for converting their raw data into actionable information. These five Vs are specifically: (1) Volume: The need to look at the type of data and the internal systems; (2) Velocity: The speed at which big data is generated, collected, and refreshed; (3) Variety: Refers to both the structured and unstructured data; (4) Veracity: Refers to trustworthiness and confidence in data; and (5) Value: Refers to whether the data collected are good or bad.
Purpose: Insurance companies face many data challenges. However, the administration of big data has allowed insurers to acknowledge the demand of their customers and develop more personalised products. In addition, it can be used to make correct decisions about insurance operations such as risk selection and pricing.
Methodology: We do this by conducting a systematic literature review on big data. Our emphasis is on gathering information on the five Vs of the big data and the insurance market. Specifically, how big data can help in data-driven decisions.
Findings: Big data technology has created an endless series of opportunities, which have ensured a surge in its usage. It has helped businesses make the process more systematic, cost-effective, and helped in the reduction in fraud and risk prediction.
URI: https://www.um.edu.mt/library/oar/handle/123456789/120531
Appears in Collections:Scholarly Works - FacEMAIns

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