Nagendra, Narayan Prasad, Narayanamurthy, Gopalakrishnan 
ORCID: 0000-0002-3119-5248, Moser, Roger, Hartmann, Evi and Sengupta, Tuhin
(2022)
Technology Assessment Using Satellite Big Data Analytics for India's Agri-Insurance Sector.
IEEE Transactions on Engineering Management, 70 (3).
pp. 1-10.
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Abstract
Over half of India's employment is attached to the agriculture sector and their survival is dependent on the performance of farms. The uncertainty in the performance of farms due to weather fluctuations and other risks is tackled by providing insurance cover. However, policymakers' choice of administrative measures for estimating crop loss has resulted in inaccurate data collection, opened vulnerability to the politicization of the process, and created bottlenecks to operate at scale. These problems have led to skewed timelines for data collation, lack of confidence in the data produced by the agri-insurance providers, and caused long-drawn delays in settling claims made by farmers. In this article, we present a case study on the assessment of using satellite big data as a technology deployed in Northern India to solve the aforementioned problems between the stakeholders in the agri-insurance claim settlement process. Satellite big data based analytics provides an independent data source and decision-making platform for the agri-insurers to conduct an assessment for calculating the indemnity payments. The results showcase how transparency brought in by the satellite big data analytics curbs the plausible exploitation of the claim settlement process and leads to increased efficiency and efficacy in settling farmer claims.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Big Data, Crops, Insurance, Satellites, Stakeholders, Agriculture, Supply chains, Agriculture, big data analytics, digitalization, satellite imagery, India, insurance |
| Divisions: | Faculty of Humanities and Social Sciences > School of Management |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 03 May 2022 07:45 |
| Last Modified: | 15 Mar 2024 14:55 |
| DOI: | 10.1109/tem.2022.3159451 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3154187 |
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