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Considerations when adding ride-sharing to a healthcare benefit plan
| February 20, 2019
Insurance solutions with the future in mind. Novus Risk Services provides tailored insurance solutions to our partnered brokers.
Article | February 21, 2020
The vibrant combination of insurance services and technical innovation takes a swing at the insurance sector. Let’s look at the current state of the market and how it pushes insurers toward rethinking risk scoring and underwriting procedures. In a global sense, the insurance industry is safe and sound. The sector is growing steadily, showing overall profitability.
Technology has changed the way we live our lives and, as a consequence, the way we do business. Within the span of barely a decade, the smartphone has become almost part of us and the use of mobile apps and digital interactions have contributed to many industries transforming their operations.
The losses and costs resulting from the outbreak of coronavirus are estimated to be in the enormous sum of four trillion US dollars – but we don’t see this as reason to panic. Thanks to a mobile-first approach, technical infrastructure and direct sales, insurtechs are protected against the crisis and, right now, they are one of the most promising business models, in one of the largest markets in the world. The developments surrounding coronavirus could therefore even be described as a catalyst for insurtechs. Crises reveal how strong a digital business model is. And coronavirus in particular demonstrates the extent of the technological lead over traditional insurers.
Job automation has shaped cultures and economies since before the agricultural revolution, throughout industrial revolutions and into the current digital age. The insurance industry is not immune, with automation and innovation continuing to drive the scope for significant change. Traditional automation has been transformative in automating simple, repeatable tasks in back-end processes. Robotic process automation (RPA) combined with artificial intelligence and machine learning capabilities can be, and are being, used to automate high volume and high frequency tasks that have traditionally required human intervention.
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