One of the challenges insurers face when implementing any new cloud-based application into their workflow is the integration of both internal and external data. Gaining access and permission to use internal data can be the first hurdle. Adding the requirement to format the data in a specific format can be a show-stopper.

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.

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.

Cross selling is a natural extension of trying to get a larger share of the customer's wallet. After acquiring a new customer, the sales and account management teams put their entire effort into: 1. Ensuring that the customer spends more with them; and 2. The customer buys another product or a service from them.

'Retention' refers to the ability of a company to retain its customers over some specified period. High customer retention means customers of the product or business tend to return to, continue to buy or in some other way not defect to another product or business ‘Persistency’, during a period may be defined as the proportion of policies remaining in force at the end of the period out of the total policies in force at the beginning of the period. In other words, persistency is the percentage of business retained without lapsing or being surrendered. Low lapsation means high persistency and vice versa.