When looking at disruptive trends across financial services over the last decade, none have been as transformative as the rise of the use of artificial intelligence (AI), and the adoption of machine learning (ML) technologies, to automate and redesign many business processes. This has been especially prevalent in the insurance industry, where AI-powered applications are expected to automate up to 60% of manual processes by the middle of the decade, according to McKinsey.
This digitisation will be driven not only by the development of more sophisticated and accessible modelling techniques, but also by the exponential growth expected in the number of devices connected to the Internet of Things (IoT), sharing real-time and open-source data and analytics. In healthcare, being an active participant in this revolution will be key for any provider.
The benefits of adopting the data-centric business model that comes with the ‘fourth industrial revolution’, will be wide ranging, and will include significant changes in key areas of functionality, such as:
- Distribution: Recommendation systems and the expansion of accessible distribution networks will continue to speed up the purchasing process. Alongside this, broker access to enhanced analytics will allow them to better target their product offerings and evaluate and track performance, yielding improved opportunities for strategic transformation in the methods and manners in which products are developed and sold.
- Claims: Almost all traditionally manual processes regarding the instantiation, capture, verification, and payment of claims will undergo some form of digitisation and automation. Direct data links between healthcare providers and insurers will allow for faster and more accurate claims processing, where simple claims may be fully assessed and paid by autonomous systems and ‘robo-assessors’.
- Fraud detection: ML systems are particularly robust when it comes to detecting outliers; or identifying when observations do not ‘fit the norm’. This feature has useful applications in detecting abnormal claims and identifying those that have the potential to be fraudulent and is already deployed in this regard extensively across the industry.
- Underwriting and pricing: Underwriting will be almost wholly transformed by advancements in machine and deep learning models. The use of ‘Big Data’ to fuel predictive models will allow the creation of more targeted and flexible products, and pricing that more accurately reflects a member’s risk profile.
The speed at which these advancements are being developed and implemented inevitably means that even more areas of interest will be discovered as the rollout of these technologies progresses. The world of AI is dynamic, and innovation is often built on top of innovation.
Therefore, it is important for insurance organisations to position themselves correctly – the benefits given by these advancements can be swiftly built upon, and it can be all too easy to be left behind. A focus on developing a data culture, where the correct importance is placed on obtaining, storing, and analysing accurate and relevant data is paramount, with resources allocated to specialised data and analytics teams as well as the infrastructure they require.
Jason Dunk, Head of data science