Data Science and Machine Learning: Unlocking new frontiers in Claims

Data science and machine learning are powerful tools that can help convert data into actionable insights. By leveraging these technologies, insurers can make more informed decisions, identify potential risks and opportunities, and ultimately improve their performance.

Advanced analytics have been used in the insurance industry for some time, particularly in pricing and underwriting. However, its application in claims operations is relatively new.

Insurers have invested heavily in understanding customer risk and purchasing behaviors to ensure they are charging the right price. While there is still some value to be gained in this area, the potential to reduce claims spending and optimize claims processes is immense.

Low hanging fruit

Although machine learning is increasingly recognized as a powerful tool to reduce claims costs and deliver significant value to insurers, many have yet to realize its full potential. This presents a unique opportunity for insurers to capitalize on the low-hanging fruit in the claims space, such as providing a more tailored, faster service to customers. The benefits of this approach are evident in the speed of claims settlement and the potential to improve an insurer's Net Promoter Score (NPS), the global benchmark for customer satisfaction.

Claims processing already utilizes a variety of external data sources, such as automotive sales market operators for vehicle values, demographics, and other vehicle information to inform repair costs. Machine learning makes it possible to link all these disparate threads together and help insurance companies more accurately predict future outcomes and identify changing experiences earlier.

Internal impact

Machine learning can be thought of as a powerful tool that can help claims handlers and claims teams make more informed decisions. This can lead to a number of positive impacts on the internal organization, such as upskilling of individuals, the creation of new roles, and improved customer satisfaction and profitability.?

However, it is important to remember that machine learning is not a panacea. Each algorithm has its own strengths and weaknesses, and it is only by understanding how to best leverage these strengths and weaknesses that insurers can maximize the value they gain from machine learning.

Collaborate or fail

When it comes to leveraging unstructured data with data science, it is essential to utilize an insurer's deep domain claims expertise to make sense of the data. The greatest challenge in terms of success and failure in applying data science to claims operations is the ability of both sides to collaborate effectively. By combining an insurer's in-house claims expertise with their data science and machine learning experts, it is much easier to approach problems in a way that leads to a mutually beneficial solution.

Near future

It can be easy to get caught up in the short term and focus on making a single solution work. However, it is important to keep the end goal in mind, where insurers must compete against each other with hundreds of models. To succeed in this competitive environment, speed, scalability, and sophistication are essential.?

Data science is not a one-size-fits-all solution to every problem an organization may face. To make the most of machine learning, a multidisciplinary team must be assembled, combining an insurer's existing claims knowledge with advanced analytical and data capabilities to create next-generation claims processing that optimizes costs and enhances the customer experience.