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Future In.Sight: Insurance evolution through technological revolution

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Article Synopsis :

Traditional insurers face a growing array of digital competitors armed with new tools capable of delivering more agile, customer‑focused solutions. This report from KPMG outlines how incumbents can get on a path to co-opting these tools to build winning digital strategies themselves.

 The Digital Insurer reviews KPMG’s Report on Future In.Sight: Insurance evolution through technological revolution

A keen understanding of the forces driving disruption is key to plotting digital strategy 

The first step is fully understanding the disruptive forces active in the insurance sector today. Technology is a major factor, but there is also sector convergence, new and emerging risks (e.g., cyber) which demand new insurance products and solutions, and customer demand for preventative solutions over traditional protection products.

The challenge is that all these forces come at a time when insurers are struggling with the headwinds of a soft market; a high cost base; and legacy supply chains and systems.

Insurers should start by asking themselves three questions:

  • What will we be famous for?
  • What role will we play in our customers’ lives?
  • Where will we play and how will we win?

Disruptive insurance businesses, with a founding and core focus on data, are positioned to reap rewards. As are insurers able to expose their business applications to third parties via application programming interfaces (APIs). Insurers living on rigid legacy technologies will suffer for their inability to leverage otherwise valuable proprietary customer data; they’ll also lose out on potentially valuable partnerships with InsurTechs and other players due to their inability to ‘share’ valuable information.

The report explores three specific technologies in-depth. Here’s a summary:

Robotics and digital labour: Robotics refers to any replacement of human activity with digital technology to improve business and customer outcomes. Insurers are always looking to reduce organisational costs, and digital labour can take them to the next level. The report predicts significant impact on offshoring back offices and call centres.

Blockchain: Blockchain is gaining traction in the broader financial world as a seemingly hack-proof method of ordering and verifying transactions in a distributed ledger via a network of computers maintaining and validating a record of the transactions with a cryptographic audit trail. Ultra-efficient claims handling with in-built fraud detection are obvious insurance use cases. The time is now to invest in proofs-of-concept.

Machine Learning: Machine learning is software that learns by doing (rather than crunching only what it has been explicitly programmed to compute). In an insurance context, for example, statistical algorithms could model the relationships between data – such as location or claim frequency – and then refine and evolve that model over time to maintain optimum pricing or servicing levels. Machine learning relies on massive amounts of data to ‘train’ systems; insurers have the data, but it’s not always available in usable formats.

Whereas Robotics and Blockchain are likely to positively impact operations and back- and mid-office productivity, Machine Learning figures to enhance product personalization, drawing insurers much closer to end customers. Machine Learning may also facilitate the fast recognition of fraudulent claims.

Link to Full Article:: click here

Digital Insurer's Comments

KPMG’s Jameel Khokhar says in the report, ‘If you’re not thinking about digital labour now, your ability to operate in the future will be challenged by new entrants to the marketplace.’ We agree—and expand the thought to include blockchain and machine learning as well.

The lead time to monetize these technologies is significant. You can’t just bolt them onto your existing infrastructure and architecture stacks. Internal changes must be made and those changes take time. Start now—if you haven’t already.

Link to Source:: click here

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