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Data has been at the heart of the insurance industry since its beginnings. The explosion of data globally, along with exponential increases in computing power, is driving advances in artificial intelligence (AI). This in turn is creating unprecedented opportunities, especially for insurers where AI has already started to revolutionize the industry.
Advanced analytics and AI are expected to become the foundation of new insurance products, allowing for the hyper-personalization of new products and offerings. This iterative process allows features to be continuously added to a product or service. By viewing additional features with this modular mindset, a flexible building-block foundation is created that evolves with new inputs. Any data that future customers might provide from health and fitness trackers could be leveraged for more customized insurance offerings.
Pricing & Underwriting
With customized insurance products based on hundreds of thousands of customer personas, the pricing and underwriting of these complex insurance policies become equally complicated. Yet traditional insurance pricing models are often still based on simple matrix systems that take a small number of variables into account, leading to high exposure risk and large loss ratios. This static approach to risk modelling will be replaced by a bottom-up approach in which internal data is combined with a growing pool of external data assets. This data lake, together with a machine learning framework, will enable more dynamic and forward-looking risk models.
A particularly interesting data source in the property and casualty insurance sector is geospatial data, previously known as geographic information systems (GIS). Insurers now have the ability to perform a detailed analysis of a property using machine learning and computer vision solutions for more accurate and quicker pricing.
The claims process is complex as different claim types follow different processes and involve different departments. From the time a customer submits a claim until the time it is resolved, insurers have the opportunity to prove themselves. This starts with the options the customer is given to submit a claim or to have internet of things (IoT) devices and sensor solutions automatically trigger the filing of the claim.
Data generated in this first step alone will create a library of events that can be used to optimally route claims to the proper department or complete the claims process automatically based on the claim’s severity and complexity. With fraud making up 5–10% of insurers’ claim costs, intelligent automation tools also help reduce fraudulent payouts and help investigators focus on only the most likely fraudulent claims.
Data is key to all of these topics, and every company across every industry needs to become a data company. Insurance companies that successfully become data-driven will innovate, simplify and improve their customers’ experience while streamlining internal processes. To do this, they will need to rethink their approach to data; archaic designs from the past need to be replaced with agile methodologies that put data at the heart of decision-making.
Companies need to understand that this journey will take time. Key is to have an agile approach that allows quick wins but also quick failures both of which will likely occur. It is important to keep the customer experience and customer perspectives in mind when developing new strategies, and to reinforce a proper data culture throughout the organization. Otherwise, these initiatives have limited chances of success.
Huawei and CPIC
CPIC is a Fortune Global 500 insurance company. Founded in 1991 and Huawei are deepening cooperation in multiple fields, including data center and enterprise cloud construction, Big Data, artificial intelligence, and digital security. The partners are constructing CPIC’s cloud data center infrastructure, creating innovative industry applications, and improving digital security. They plan to explore and research further innovations for the CPIC cloud, smart customer services, and digital risk control.