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8 innovation trends for digital insurance in 2016

Kevin Pool, Chief Technology Officer, TIBCO Asia | March 30, 2016
Kevin Pool of TIBCO Asia talks about the business and technology trends for the next wave of digital insurance.

3. Embrace and extend legacy insurance IT infrastructure 

The techniques to embrace and extend the decades of technology debt require self-innovation. Technologies exist to extend the life of legacy IT assets and increase their return on investments. There is no longer a need to pursue high risk, costly, resource distracting and multi-year journeys to replace these systems, to benefit from digital transformation. For example, one popular technology trend is the use of in-memory data grids to move and cache back office data for new, innovative, digital applications. Data grids reduce load on existing systems and can cut costs significantly in some cases, while reducing the need to buy additional storage.

4. The rise of digital customer relationship management and digital integration 

Customer relationship used to be a purely human practice, but moving forward, relationship management must be digital. For example, modern insurance call centres must have a seamless, real-time, 360-degree view of social media engagement, mobile application interaction and geo-awareness from IoT sensors. This relationship management scenario is hard to accomplish with legacy systems. Modernised integration infrastructure with up-to-date web API, cloud-based services, IoT aware connectivity, as well as traditional integration infrastructure and data integration is required.

5. IoT increases the need for streaming analytics to innovate

Insurance companies are now capturing new data from the "Internet of Everything." For example, wearable device data allow insurers to offer discounts for healthy activity-based behaviour. But activity-based products require activity-based data and systems, and require the ability to process massive amounts of streaming information from devices. Hence, new technical challenges arise. These range from efficiently analysing streaming data, archiving particularly streams, maintaining privacy and handling the volume of data streaming from such devices. These technology challenges did not exist in the last era of insurance and pose formidable challenges on how to apply streaming analytics technology, historical analytics, security and big data fabrics.

6. Increased focus on algorithmic risk assessment

Digital insurance data is moving fast, similar to streaming market data in the capital markets. In 2016, insurance companies will increasingly apply real-time algorithmic computing technologies born on Wall Street to the onslaught of streaming data from GPS, mobile and wearable devices, to make algorithmic decisions about subscriber conditions. But unlike Wall Street, insurance companies are not building low-latency automation. Instead, they are using continuous algorithmic analysis to monitor streaming data to manage risk, manage subscriber loyalty and search for sales opportunities.

7. Driverless cars pose new forms of risk

As Elon Musk and Google continue to lead driverless car innovation, the insurance industry must respond. Risk management will not be the same in the digital era, not only in terms of forecasting risk, but also in managing and mitigating risk in real-time. An example is attempting to ascertain who is at fault if an automated driver feature in a car causes an accident. The possibilities lie from the Tesla software engineer who wrote the bug in the software, the "driver" who did not touch the wheel, the driver of the vehicle who may have been hit by the autonomic car, who was also partially in the wrong. Subsequently, insurance companies are obligated to gather and analyse massive amounts of streaming forensic data from insured, automated assets, to make a decision by monitoring streaming data, in an effort to mitigate risk in real-time. The digital insurance company must invest in capturing these new forms of data, and using in data science to analyse it for forensics, patterns and predictive actions, to decide how to respond to these regulatory pressures.


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