Citation:

Devidas Kanchetti, 2022. "The Ethics of Data Science in Insurance: Balancing Innovation with Privacy and Fairness", ESP Journal of Engineering & Technology Advancements, 2(1): 86-99.

Abstract:

Data science has greatly impacted insurance and has enabled insurance companies to handle risk and fraud and manage individual attention. Nevertheless, insurance businesses using innovative technology such as machine learning and artificial intelligence (AI) raise ethical privacy, fairness, and transparency issues. This paper aims to discuss the ethical issues concerning data science in insurance, emphasizing how innovation can be achieved without compromising the customer's rights to privacy and fairness in decision-making. Insurance companies depend on personal data to issue premiums, assess claims and decide on coverage. These days, big data is available from which insurers can derive information, namely, social media behavior, biometrics, and genes. This helps make better-developed risk profiles; however, it raises issues of this information being analytics used in the wrong way. However, there are possibilities of discrimination, more so when the systems themselves are biased, and this is evident when algorithms used by these systems are trained on the data, which will also be discriminative. In addition, the general role of data science in insurance to enhance its service delivery also raises questions related to transparency and accountability. This lack of transparency as a result of the increased use of AI in decision-making raises concerns with the consumer since he or she is unable to know and question how his/her data is being used or processed by the insurers. One major challenge that the insurance industry has to overcome is the challenge of ensuring that these technologies do not erode trust in consumer-provider relationships. In the following paper, we review the timespan literature on ethical issues in risk and insurance data science and offer a method to deal with these problems. In addition, the paper presents success and failure stories of data utilization in the financial industry context. The method involves the identification of relevant scholarship business sources and other documents, as well as determining the regulatory approaches to tackle such issues. Lastly, some suggestions are given in the paper on how insurance companies can incorporate ethical models in the data science solutions they are implementing to build consumer trust while promoting innovation.

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Keywords:

Data Science, Insurance, Ethics, Privacy, Artificial Intelligence.