Insurers have a near-constant stream of unstructured data at their disposal that can be used to drive growth by improving policyholder retention and identifying cross-sell and upsell opportunities. One of the challenges for insurers is sorting through this mountain of unstructured data quickly to gain an accurate understanding of the sentiment of their customers in real time.
Enterprise applications belong to a vibrant ecosystem and consequently the data they generate is large and varied. Enterprises both benefit and suffer from this nature of application and data.Whenever a new application is to be deployed in an enterprise that integrates with the applications in the ecosystem, the precondition is an 'expansive data definition with referential value' on day 1 to start integration. Traditionally, this approach to data integration involves identifying a target data structure, and force fitting data from all sources into it. This is done to ensure a 'seamless' integration - never mind the loss of data considered irrelevant.
According to the Coalition Against Insurance Fraud, at least $80 billion in fraudulent claims are made annually in the U.S across all lines of insurance. This translates more than $400-$700 per year in increased premiums for each American family.
Bajaj Allianz Life Insurance Company recently hosted a unique insurance summit focused on putting customer experience first. The event titled Future Perfect-Customer First Insurance Industry Summit 2018 was a full-day event that was attended by 21 of the 24 insurance companies that operate in India. While the focus was life insurance, the summit also saw participation from a few general insurance carriers.
2017 has been a year when ML/AI technologies have become mainstream, and businesses are conversant with their application and possible use cases. 2018 will, however, be the year when trends from 2015-17 will finally come into maturity and we will be able to see results. Commercial application of blockchain (beyond Bitcoin), wider acceptance of enterprise SaaS solutions, and optimization of investments in data lakes – are just some of the examples we can think of. Customers too, are now beginning to understand more about data privacy and security, and want to be more in control of their own data. It seems that finally in 2018, technology and business will move ahead together.
Big data analytics find immense application across the entire business. One area which can have direct, measurable and visible impact is the area of customer service. Data has been used since the beginning of time to improve customer service, but it is only recently that the full power of predictive analytics is being applied in this function. Organizations, both large and small, are using big data analytics to deliver superior customer service and build strong customer loyalties. See below as we look at 6 ways big data analytics is driving smarter customer service.
The industries we work in – insurance and banking – are probably the most process-driven and metric-focused businesses of today. Every action must result in a quantifiable metric, which are then collated and presented day in and day out to understand the business performance and direction.