In 2022, the insurance industry made a whopping USD 6 trillion globally—more than the entire economy of big countries like Japan and Germany. A new study , published in The Journal of Finance and Data Science , looked at how technology, especially big data, is shaking things up in insurance. Big data means using a lot of information to make better decisions.
The study found that by using big data, insurance companies can understand risks better, offer fair prices and keep customers happier.
“What's surprising is how fast insurance companies are jumping on the big data bandwagon,” says first author of the study, Nejla Ellili. “They're investing a lot of money in it—around $3.6 billion by 2021! And guess what? It's paying off! Big data helps them save money, offer better insurance deals, and catch more fraud. But it's not all sunshine; there are some problems too.”
The study found that these is a need to be careful about privacy and ethics when using all this data. The findings also revealed that while much is known about how big data is helping insurance right now, there's still a lot to be elucidated in terms of the long-term effects.
“This means researchers and people in charge of insurance rules need to keep studying to make sure big data is used the right way,” adds Ellili. “Our findings give us a roadmap, like a guide, for future research, telling us what we should look at next.”
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Contact the author: Professor Haitham Nobanee
College of Business, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates
Oxford Centre for Islamic Studies, University of Oxford, Marston Rd, Headington, Oxford OX3 0EE, UK
The University of Liverpool Management School, The University of Liverpool, Liverpool, Lancashire, United Kingdom
Emails: ( haitham.nobanee@liverpool.ac.uk ; haitham.nobanee@adu.ac.ae ; haitham.nobanee@oxcis.ac.uk ; nobanee@gmail.com ).
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The Journal of Finance and Data Science
Systematic review
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The applications of big data in the insurance industry: A bibliometric and systematic review of relevant literature
1-Nov-2023
The authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript.