One way to think about the application of data science and machine learning is that it is a tool to help convert information (data) into action. In this context, machine learning is applied to enable better and more efficient decisions, as well as to identify previously hidden risks and opportunities. Essentially, data science helps an insurer perform significantly better, regardless of their goals.
The application of advanced analytics is already well entrenched in the world of insurance pricing and underwriting. However, it has only more recently begun to exert more influence in claims operations.
Across the entire insurance value chain, substantial resources and effort have been expended to better understand a customer’s risk and buying behaviors to help charge the most appropriate price. New advantages yet to be exploited in the area of pricing and underwriting are relatively rare. In contrast, huge untapped value is waiting to be realized by insurers by reducing their claims spend or by better understanding and optimizing their claims processes.
While machine learning is increasingly recognized as a tool to reduce claims costs and deliver significant value to an insurer, it remains an area that many have yet to realize the value. This means that there are plenty of low-hanging fruit to be reaped in the complaints space, such as the benefits to be realized by providing better, more personalized and faster service to the customer. These benefits are reflected, for example, in the speed with which claims are settled and the way in which an insurer’s Net Promoter Score (NPS), the global benchmark for customer satisfaction, can be improved.
Claims processing already uses a lot of external data, including integration into third-party sources such as auto sales market operators for vehicle values, demographic and socio-demographic information, and various other vehicle information to inform the repair costs. Machine learning can tie together all of these separate threads and help insurance companies more accurately predict future outcomes and identify past changing experience.
There is also the positive impact on the internal organization which has the potential to be equally transformational. Machine learning can be seen as a tool, a superpower to help claims handlers and claims teams make better decisions. Individuals can improve their skills, new roles will be created, all helping to deliver measurable improvements to customers and dramatically improved profitability.
At the same time, it is important to understand that machine learning will not provide the perfect answer to all questions. Each individual algorithm built will have both strengths and weaknesses. That being said, it is always possible to build and improve models based on an understanding of these strengths and weaknesses. More importantly, understanding how to make the most of what an insurer has available, as well as how this can best be applied and integrated, will determine earned value.
Collaborate or fail
This is especially true when it comes to using data science to leverage unstructured data. Using an insurer’s deep claims expertise is key to shedding light on unstructured data and translating it into something that actually makes sense. When it comes to the application of data science in claims operations, by far the greatest risk to success and failure is the ability of both parties to collaborate effectively. By bringing together an insurer’s in-house claims expertise with its data science and machine learning experts, it becomes much easier to approach problems in a way that leads to a successful joint solution.
It can be very tempting to focus on the short term and do whatever it takes to make a solution work once. But one thing to keep in mind is the end state, where one insurer’s loss models will compete with another insurer’s models. In a world where hundreds of models are competing, the ability to move quickly, scale for efficiency, and be the most sophisticated will be needed to succeed.
Data science is not the absolute magic bullet for every problem an organization will face. Instead, being able to take full advantage of machine learning means bringing together a multidisciplinary team, which combines an insurer’s existing in-house claims knowledge with cutting-edge analytics and data capabilities to deliver next-generation claims handling that optimizes costs and transforms the customer experience. .