Author: Christian Mottas; Publisher: University of Zurich; Publication Year: 2019. The following article discusses how insurers should not use data that is not related to the insured risk because such behavior will damage the customer’s trust in the insurance industry. Regulators should implement provisions specific to the insurance industry to deter unwanted forms of personalization using Big Data…
Author: Michelle Seng Ah Lee; Publisher: Medium; Publication Year: 2022. The following article discusses the ethical concerns associated with the implementation of artificial intelligence (AI) programs in the insurance industry. Beyond privacy issues arising from the collection and use of sensitive insurance-resulted data, concerns regarding fairness and equality have also been brought up concerning these AI…
Author: N/A; Publisher: Institute of Chartered Accountants in England and Wales. Publication Year: N/A. The following article introduced 3 groups of principles and respective examples that could potentially identify, prevent and resolve ethical tensions in the financial sector. A finance company needs to have a systems and data architecture that include data sources, how it is ingested and stored, how it is processed, streamed and published and where it is…
Author: Harriet Jones; Publisher: Marketplace; Publication Year: 2021. The following article describes how the pricing model has become more sophisticated over the years, the factors that decide the insurance rates have become more numerous and complex. However, the data that insurance companies use these days are inherently discriminatory such as the neighborhood unemployment rate and distance to the…
Author: N/A; Publisher: Cision PR Newswire; Publication Year: 2022. The following article discusses how FairPlay is the world’s first “Fairness-as-a-Service” solution for algorithmic decision-making. The company provides lenders with easy-to-use tools that assess and reduce bias in credit algorithms. FairPlay was found to combat the threats algorithmic systems pose to the safety of consumers and society, by building…
Author: Duncan Minty; Publisher: Scandinavian Insurance Quarterly (SIQ); Publication Year: 2017. The following article discusses how the insurance industry has long been using historical data to underwrite, price, and manage risks. The advanced algorithms that insurance firms rely on to profile customers underwrite and price premiums are built upon such data, and recent studies revealed that they are biased and can produce discriminatory…
Author: Daniel Schreiber; Publisher: Lemonade; Publication Year: N/A. The following article discusses how the availability of big data and improvement in computing power now allows insurance companies to price more fairly in terms of premium per risk covered, but that does not mean they are not discriminatory. The author suggest using what is called a ‘uniform loss ratio’ test to check for bias. Simply put, if certain…
Author: Abhishek Lall; Publisher: Verisk; Publication Year: 2021. The following article identifies 3 ways that AI can go wrong in the insurance industry: biased and hidden data, incomplete data, and gaps in subject matter expertise. To ensure an ethical approach to AI, the author recommends building a proper framework, quality assurance process, analytics governance, and…