Pye, F. and Olajide, F. 2023. Utilising Big Data Analytics in the Insurance Industry for Optimal Business Insight: A Literature Review. International Journal for Infonomics. 16 (1), pp. 2172-2176. https://doi.org/10.20533/iji.1742.4712.2023.0228
| Title | Utilising Big Data Analytics in the Insurance Industry for Optimal Business Insight: A Literature Review |
|---|---|
| Type | Journal article |
| Authors | Pye, F. and Olajide, F. |
| Abstract | Organisations and insurance companies are not utilising business data and analytical methods to gain full advantage of accurate representation of risk, which can lead to further risk such as underinsurance, overpriced policies, and inaccurate claims assessment. Many companies organise their own insurance and risk management internally and rely on external experts such as insurance companies, brokers, and underwriters to help determine requirements. As part of this set up, there is a reliance on data derived from the organisation to inform policy placement, pricing of insurance policies and to accurately represent risk. The nature for enhancing the operational efficiency of insurance products using Big Data Analytics (BDA) is found to be a common concept in the insurance industry. The research work relating to insurance risks and cyber security is still in its infancy. Therefore, the research aim is to investigate how best to derive business insight from the insurance and risk division of an organisation using Big Data Analytics (BDA). The research considered security metrics for risk, control and mitigation based on the organisation’s reaction to cyber risk for data accuracy. This paper reviews research previously conducted in the area with the aim of understanding which Big Data Analytics (BDA) methods are currently used and in which areas of the insurance industry. The findings show that Big Data Analytics (BDA) methods such as predictive analytics, artificial intelligence, machine learning and random forest regression are utilised in areas of insurance such as underwriting, claims and pricing. |
| Keywords | Analytics |
| Big | |
| Data | |
| Insurance | |
| Risk | |
| Journal | International Journal for Infonomics |
| Journal citation | 16 (1), pp. 2172-2176 |
| ISSN | 1742-4712 |
| Year | 2023 |
| Publisher | Infonomics Society |
| Publisher's version | License CC BY 4.0 File Access Level Open (open metadata and files) |
| Digital Object Identifier (DOI) | https://doi.org/10.20533/iji.1742.4712.2023.0228 |
| Publication dates | |
| Published in print | 2023 |
| Published online | 2024 |
| Supplemental file | File Access Level Open (open metadata and files) |