Digital disruption in insurance
26 August 2021
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Written by Rahmi Pramesti, Senior Product Manager

Digital disruption in Insurance

Traditional insurers, including in Indonesia, are seeing growing competition from digital players. The pandemic, market changes, and needs to reduce prices, are pushing the industry to embrace digitalization much more quickly, and one of the big levers is revamping their own business process. These market changes, including changing customer behavior or the increasing access to external data and technology-is the reason why insurers are pushed to re-innovate their business practice. It’s a way for them to protect themselves from disruption to their core business and to meet these shifts in customer behavior and expectations. Most importantly, it’s also a way for them to open up completely to provide better customer experience and revenue streams.

Driven by spurs of new technology and big data, make it possible for insurance company to transform not just the core underwriting activity but also alter policy distribution and management, claim management, and customer experience in new ways that could lower risk, increase customer engagement, and ensure more profitable pricing. In another angle, as the whole industry - both life, health, and asset-based insurance – is based on having data and running analytics on it to calculate risk an create value proposition for customers, enriching data reach to alternative data into their assessment is essentials. By incorporating alternative data into current set of traditional data, will enable them to:

  • Accurately classify all customers based on risk

  • Set appropriate risk-based rates

  • Offer a more personalized customer experience

  • Better market understanding allowing potential increase in revenue

Leveraging alternative data effectively while extremely powerful, as mentioned above, does not come without its challenges. Numerous analytic technologies coupled with machine learning approached has been implemented to deal with large unstructured data set. Yet, the model produced need stringent quality assurance processes to guarantee the accuracy of the output, that the patterns identified are strong, relevant, and explainable in the perspective of both customers and regulator. Thus, needs and ability in identifying partners that can answer requirements of broad ethical-legal alternative data along with capability to provide insights with state-of-the-art technologies and algorithm are paramount.


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