Prior to the Insurance Act 2015, an insurer could refuse payment by relying on terms unrelated to the manner in which the loss occurred, or with the assured's default. Section 11 of the Insurance Act 2015 reverses that and requires ‘the punishment to fit the crime’ by introducing a ‘could have increased the risk’ test, which is said to be a requirement for correlation between an assured's non-compliance with a risk clause and the actual occurrence of the loss. The new test effectively raises more profound causation issues from a practical point of view as regards insurance recoveries and also from a theoretical point of view of causation in the law. It is submitted that the test ‘could have increased the risk’ introduced by section 11 of the Insurance Act 2015 should be interpreted by an approach to recognising a nexus – a general causal relevance (general causation) – and adopting interpretation as a restricting tool, to achieve alignment with the legislative intent. In presenting this argument, this paper explores the difference between correlation and causation in the law, expounding a wider understanding of legal causation than causa proxima in the insurance context.

Insurers are increasingly using big data analytics and artificial intelligence in rating risks and customising insurance products particularly in the context of consumer insurance. The primary aim of this article is to elaborate the extent to which the legal rules in force could ensure that consumers are not treated unfairly as a result of the use of such disruptive technologies. Relevant insurance law principles and doctrines are also considered as part of this analysis. The article concludes that despite the protection provided to consumers by data and consumer protection legislation, unregulated and unlimited use of data analytics and algorithms in the risk assessment process could create significant difficulties for consumers. It is argued that further regulation, especially making regular audits essential for insurers employing such technologies in risk assessment process, is required. The article also finds that the use of artificial intelligence in customising insurance products does not present similar degree of difficulties for consumers.