Introduction

The insurance industry is undergoing a significant transformation, with the advent of advanced data-processing and analytic techniques such as machine learning. The supply of huge quantities of data has enabled the exploitation of these techniques, which have the potential to completely rewrite the basic mechanics of insurance. However, this transformation also raises concerns about the sharing of big data. In this article, we will explore the opportunities and concerns associated with the sharing of big data in insurance.

Opportunities

The sharing of big data in insurance can lead to several opportunities. For instance, it can help insurers to better understand their customers’ needs and preferences. By analyzing customer data, insurers can identify patterns and trends that can inform product development and marketing strategies. This can lead to more personalized products that meet customers’ specific needs.
Another opportunity is that big data can help insurers to better manage risk. By analyzing large datasets, insurers can identify patterns that indicate high-risk behaviors or situations. This can help them to develop more effective risk management strategies that reduce their exposure to risk.

Case Study: Progressive Insurance

Progressive Insurance is one company that has leveraged big data to improve its risk management strategies. The company uses telematics devices to collect data on its customers’ driving habits. This data is then analyzed to identify high-risk behaviors such as speeding or hard braking. Based on this analysis, Progressive is able to offer personalized insurance rates that reflect each customer’s individual risk profile.

Concerns

While there are many opportunities associated with the sharing of big data in insurance, there are also several concerns. One concern is that the sharing of big data could lead to privacy violations. Customers may be uncomfortable with the idea of their personal information being shared with third parties without their consent.
Another concern is that the use of big data could lead to discrimination. For instance, if insurers use certain types of data to assess risk, they may inadvertently discriminate against certain groups of people. This could result in unfair treatment for some customers.

Case Study: Amazon

Amazon is one company that has faced criticism for its use of big data in hiring practices. The company used an algorithm to screen job applicants, but it was found that the algorithm was biased against women. This highlights the potential for discrimination when using big data.

Conclusion

In conclusion, the sharing of big data in insurance presents both opportunities and concerns. While big data can help insurers to better understand their customers’ needs and preferences, it also raises concerns about privacy violations and discrimination. To ensure that the benefits of big data are realized while minimizing its risks, it is important for insurers to be transparent about their use of customer data and to take steps to protect customer privacy.