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Helping insurers compete in the age of disruption – KPMG


Article Synopsis :

Insurance has always been about risk transfer driven by data and analytics as carriers were conducting in-depth analyses long before computers or big data. Likewise insurance has always been about trust, risk mitigation and protection. Over the past decade the relationship between data, analytics and trust has changed, posing significant opportunities and challenges for mainly incumbent carriers.

 The Digital Insurer reviews KPMG’s Report on Helping insurers compete in the age of disruption

IoT-enabled risk prevention (as opposed to protection) requires a leap of faith for incumbent carriers 

In the Trusted Analytics article series “Helping Insurers Compete in the Age of Disruption”, KPMG’s Gary Richardson and William Howe explore how the rise of connected devices, IoT, InsurTechs, and sophisticated data and analytics are disrupting the insurance industry.

Per the paper, IoT combined with big data and analytics is driving a shift from insurance being a matter of trust and risk mitigation to trust and risk prevention.

It used to be trust was defined as being committed to the customer at claim time, with fair treatment and, in some cases, wider support after a major insurance event. But customer expectations are changing. InsurTech and IoT are major disruptors opening customers to direct-to-consumer business models. While it’s true these disruptive forces hold the potential to drive operational efficiencies for insurers, the on-ground reality is often frustratingly complex, with legacy systems impeding progress on new technologies.

Mitigation strategies being key, the report outlines these major themes:

  1. From macro to micro: Insurers are thinking small – targeting targeting micro-insurance to micro-customers at micro timescales. Not only insurers, but InsurTechs such as Trove and Slice are moving customers towards micro-insurance models. Customers are increasingly willing to exchange data for lower premiums and discounts. This, however, has the potential to create an “arm race” with both insurers and customers trying to manipulate the game to their advantage. With increased usage of IoT comes a need to make customers confident that their data is safe and that it will be used only in their best interests.
  2. Prevention: Prevention-based models are gaining importance. If insurers are able to predict the risk well in advance, then the question arises what can be done to prevent it altogether? How will insurers bridge the gap between legal responsibilities and ethical expectations?
  3. Transparency: Increased transparency allows customers greater access to the risk equation. This in turn provides customers an opportunity to negotiate the risk pool into which they are placed. This significantly shifts the power from insurer to consumer – and requires insurers to offer greater clarity around pricing.
  4. Ecosystems: What is the interdependence of insurers and InsurTechs around data? It’s still not clear who will manage the data for the full lifecycle from product development through customer claims.
  5. Developing and managing trust in algorithms: Algorithms help better predict certain risks. They also create worry amongst employees whose skills might be replaced. It’s necessary to establish proper controls and methodologies ensuring algorithmic integrity, not only today but tomorrow as conditions (e.g. regulation) change. This should be a continuous process.

Gaining the trust of internal and external stakeholders will take time. Insurers should not delay rethinking relationships with InsurTechs, customers, and customer data toward potentially new analytics-driven business models.

Data and analytics hold the power to both create and destroy value. But it’s an immature field which still lacks a comprehensive framework for practical action. In order to benefit from the changing trust relationship and the rise of InsurTechs, insurers should consider the following at a minimum:

  1. Consider where it may be better to collect new customer data gradually over time and evolve the relationship.
  2. Introduce a data usage transparency policy that’s clearly worded so customers can understand how their data will be used.
  3. Move toward greater transparency in pricing, underwriting and claims interactions with customers.
  4. Understand that the border between Personal Lines and Commercial Lines is blurring, and start evolving products and services accordingly.
  5. Consider developing application programming interfaces (APIs) for back-office functions which will help in easy integration with InsurTechs and aid in improving customer trust.

Link to Full Article:: click here

Digital Insurer's Comments

Insurance has always been a trust business, as in: ‘Trust us to do you right by paying your claim.’ The new Digital version is: ‘Trust us to do you right by preventing a claim at all.’

Prevention-based business models put customers in a new role — as data partners willing to provide richer data capable of generating more value through reduced loss and lower risk. These models also impose new burdens on carriers in terms of data stewardship and inevitably lower premiums.

Customer technology adoption is outpacing insurer technology deployment. But customers will not be denied and someone will give them what they want, eventually. Carriers embracing the potential of analytics are, in a changing world, assuming courageous ownership of their own fate.

Link to Source:: click here


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