Gartner’s 2019 report, Insurance 2030 Scenarios Update: CIOs Need Greater Adaptability to Survive and Thrive in an Era of Ongoing Industry Transformation, is a forward-looking roadmap for insurance business and IT leaders facing an era of increasingly unpredictable and highly challenging operating conditions.
A study by Forrester revealed that firms with the lowest tolerance for downtime and data loss, and the strongest requirements for continuous auditing and independent security certification, are the most likely to run top applications on low-code.
Today's insurers and financial services institutions need solutions that run quickly and deliver high impact to their businesses with speed. Here are three ways enterprises can achieve these goals with a low-code platform:
If you don’t think you’ve got a problem with software development, this blog isn’t for you. But that’s okay. On the other hand, if your organization is among the many out there that are struggling to keep up with rising demand for business applications, you’re in the right place.
Speed is critical for innovation. And today, insurers are innovating with the customer in mind: focusing on the need to be on top of emerging trends, stay ahead of the competition, and reduce costs while still offering the highest level of customer service.
As we look forward to 2020 and beyond, I wanted to break down the 5 things that insurers need to think about as they build their technology ecosystem.
Digital is happening. Insurers are keenly aware that they need rapid development cycles, and that they also must remain flexible and nimble. With digitalization high on the agenda, operations and IT have moved to the forefront of the business. Historically, insurers focused primarily on the underwriting and actuarial aspects of the business, but IT and Operations are now coming to the forefront. They are not only focused on ways to reduce costs and optimize processes, but on improving customer service, creating optimal customer experiences and maximizing internal effectiveness. But now the systems landscape has to follow suit.
Many years ago, when I was a young student of philosophy, I was presented with Plato’s “Allegory of the Cave”. And like a lot of students, it was something that fascinated me and that I’ve used since, although probably not in the way it was intended (which was really about education). If you don’t know the allegory of the cave, it’s roughly as follows. Imagine some people are shackled in a cave and this is all they’ve ever known. Behind them are people or puppets that are lit from behind with fire but can never be directly seen by the cave people. The shadows are all the cave people have ever known and so they are their reality. The cave people don’t know that they’re shadows. The lesson here is that we don’t always know what reality is — and we don’t always know what we don’t know.
Given their inherent similarities, it’s entirely logical that robotic process automation and artificial intelligence would have crossover in a variety of different contexts. Both of these technologies are contingent upon bringing order to processes – usually workflows, in either the physical or digital realm – that might otherwise run the risk of falling into disorder for any number of reasons, most notably human error. RPA and AI are perfectly capable of operating on their own, and in fact often do. Yet recent evidence suggests systems in which the two automation-focused methods work together may become increasingly common in the not-too-distant future.