This article is by Anuj Pradhan, Global Chief Product Officer, Prima, and it looks at how AI can help when pricing risk.
While the implementation of Artificial Intelligence (AI) is steadily taking root across enterprises and industry globally, its impact on the insurance sector – one governed by swathes of data – is truly coming to the fore. Holding the ability to analyse vast amounts of data with greater speed and accuracy – be that when it comes to risk assessment, pricing, claims processing or customer engagement – AI is on track to significantly reshape the entire industry.
An industry shake-up
According to McKinsey, AI technologies including Generative AI could add up to $1.1 trillion in annual value for the global insurance industry, with circa $400 billion coming from pricing, underwriting, and promotion technology upgrades, and $300 billion coming from AI-powered customer service and bespoke solutions.
Intelligent data processing AI (IDP AI) plays a crucial role in unlocking this value. IDP AI describes techniques used to process and analyse data in an intelligent way. These techniques can be applied across the insurance value chain, with use cases ranging from analysing claims data to detecting fraud, assessing risks for different customers, and personalising insurance offerings.
Indeed, the potential impacts on the end customer and their experience are seismic – be that in terms of the product bundles or add-ons they are offered, or the smoothness and efficiency of their purchase journey. And on top of this, of course, are all of the behind-the-scenes impacts that the customer benefits from but isn’t even aware of.
AI can be leveraged for efficient and streamlined product customer support, i.e., ensuring customer queries are answered quickly and accurately; fraud detection, by connecting seemingly unrelated information to identify ‘bad actors’ while simultaneously expediting claims applications of genuine customers; claims management, by using ‘Natural Language Understanding’ in order to extract information from documents quickly and accurately; and ‘Computer Vision’, which analyses the content of images and photos attached at the dispute stages as evidentiary documents, and can quickly trace any evidence of counterfeiting.
As we move forwards, we can expect even more applications in areas that are largely human-driven – such as product definition and design.
Predicting wants and needs
By harnessing the data collection and analysis capabilities of AI tools, the industry will be able to accurately anticipate customer requests before they arise; moving from a “detect and repair” approach to a predictive one based on prevention.
In practice, this means having the ability to offer the end customer more effective, and more satisfactory responses – both in terms of customer service and product customisation, and ultimately bolstering their satisfaction and loyalty. Using this intelligence, companies are able to move away from reactive sales, towards addressing bespoke wants and needs via tailored product bundles.
AI is also playing a key role in accelerating waiting and problem-solving times. Search Engine Optimization (SEO) ensures customers are able to reach the information they need easily; chat bots are able to answer even the most complex questions, guiding customers through a series of predetermined content, guides and Q&As; while virtual assistants are able to read the context, interpret the user’s mood and recognise and ignore typos, for instance.
It’s important to note that the success of these solutions is always realised and enhanced by a human touch. Firms should have a specialist adviser overseeing and managing the processes in order to ensure they are being used in the right way. Thanks to AI, however, these individuals no longer have to spend time on low-value-add operations such as collecting master data or preferences but can devote themselves exclusively to high-judgement tasks which the AI is not equipped to handle.
Customised products
In addition to streamlining touchpoints with the end customer, AI’s ability to process large amounts of data also makes more accurate product personalisation possible.
Traditionally, people have been the ones designing insurance products and policies, while data and algorithms have only been used to configure these products. However, AI is now able to bring together thousands of data points – which are either publicly available or shared by customers via explicit consent in full compliance with the regulations dictated by the Privacy Guarantor – together, including claims history, customer background, behaviour, and other product data, to make data-oriented business decisions – going so far as to propose policies to its customers designed to meet specific needs.
Here, the prospects for development are endless. This level of data collection can extend to detecting real time factors such as road conditions, weather conditions, visibility, and so on, giving them the right cover.
Companies which want to remain ahead of the curve in an increasingly competitive market must leverage these evolving technologies to best serve their customers; enabling them to obtain ever-personalised insurance coverage in real time, increasing security, while reducing costs.
