07/01/2021 | Written by: Think Blog Editor and Koos Quak
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Improve pricing and underwriting
When the pricing of an insurer is not in alignment with market conditions or individual behaviors, regardless whether the price is too high or too low, the result tends to be the same – lost revenue and narrowing margins.
Traditionally pricing is based on a cost plus method and methods lack the ability to easily incorporate non-technical pricing factors. Especially in these challenging times, where economic decline will lead to increased competition, pricing is key. Leaders in pricing innovation invest in data infrastructure to better harness data. Sophisticated insurance carriers evaluate more than 30 new external data sources and then select two to four sources each year to embed in their pricing and rating models. Innovative pricing engines can generate quotes that regularly beat the market while still maintaining profitability and avoiding adverse selection. And every quote generates an additional data point, irrespective of a customer’s final purchasing decision.
With regards to underwriting, existing risk pools are shifting, and new ones are emerging. Risks are becoming more volatile, e.g. due to climate change and world-wide events such as COVID-19. Addressing these impacts requires the availability of contextual risk data and an ability to sense and react quickly. The availability of a scalable knowledge platform is a key component to achieve that. Applying artificial intelligence is starting to become more common in insurers but doing this at scale is still challenging.
How it looks in real life? A leading Swiss national insurance company recognized that risk assessment becomes more difficult as companies expand activities in non- traditional areas. At the same time, underwriters spent a significant amount of time responding to various low-value-added questions. Furthermore, accessing information stored in different systems while efficiently serving customers proved challenging.
IBM worked with this client to introduce Watson Underwriting Advisor, which helps underwriters shift their time focus from providing low-value support to more high-value guidance. With machine learning, clustering of a company’s activities against a large set of different risk coverages and with deeper insights from unstructured data, more efficient and consistent decisions on new opportunities will be taken, which contribute to long-term profitability growth.
Looking for sustainably lower your combined ratio through implementing smarter pricing and cognitive underwriting go to this webpage and improve your pricing and underwriting.