Actuarial Post September 2019

Page 18


More than most industries, general insurers use data and analytics to run their business efficiently, sustain profitability, and create competitive advantages. Significant investments have been made over decades to collect, organise, and analyse the massive volume of data that insurers hold. Given the spend, hype, and promise of big data, machine learning, and AI, many are asking the question, “What impact is the investment having on the challenges of capturing business value?” While the insurance industry is one that has always used data extensively, this has often only been regarding to pricing and risk. Nowadays, thanks to the use of machine learning and artificial intelligence, insurers are adopting predictive analytics in their claims processes, particularly when it comes to identifying fraudulent claims.

However, integrating predictive analytics with business processes is not an entirely straightforward process. Prior to any modelling activities, data science teams, business teams and IT teams have to understand fully the business needs and related technology needed to deploy the models into their core systems. Otherwise, problems will arise when insurers move to operationalize their analytics and the expected business value is often never realised. Moreover, even artificial intelligence deep-learning techniques – that is, artificially intelligent systems capable of learning unsupervised by looking at unstructured data – may struggle to understand patterns they have never seen before. Depending on the insurer’s needs, and the nature of the data they are using (structured or unstructured) variable amounts of data are

required to create models. Fundamental to the whole process is that the data should be high quality. Only then will insurers be able to build an AI capable of handling the complexities of the largest claims, whilst expediting the process for those smaller and more easily automatable ones. Within the insurance industry there are clear variations in the ability of insurers to capture measurable value. Insurers whose analytical strategy is focused around the final objective - delivering business value - tend to execute more effectively and deliver more value when compared to organizations who are not aligned with their business counterparts or perhaps overly focused on highly ambitious, if not somewhat speculative, objectives. Successful strategists consider not only the data needed for modelling

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