Most insurers’ view of their policyholders is in isolation - one policyholder at a time, with the possibility that more than one individual in a household may have different or multiple policies from the same insurer. As such, the premium impact of the household is larger than that of the individuals.
Today, customers expect a personalized, unique experience. Millennials not only expect a superior experience but also expect their service provider to know in advance about the kind of treatment they prefer to receive. A critical step in delivering a unique experience is to know what your existing customers think about you and your services.
Virtual assistants like Siri, Cortana and Alexa as well as other speech synthesis techniques have solved many customer use cases by offloading repetitive and mundane searches or activities. Customer-oriented businesses leverage this technique to provide better operational efficiency and improve customer experience. They can then run analytics over the voice/audio content to derive predictions.
If a life insurer wants to build a predictive model, how should they go about it? In this article, we explore the factors that need to be considered before beginning actual model development. We will do this by using the example of predictive models for improving persistency. (Improving persistency for a life insurer means increasing the volume of business they retain.)
Companies design application processes to provide the best possible experience for their customers. These processes rely on application and customer-originated events to function. These events and their outcome form the basis of the customer’s experience. Therefore, event-driven philosophy is an ideal way for companies to measure customer experience.
I recently saw a tweet from Mat Velloso - “If it is written in Python, it’s probably machine learning. If it is written in PowerPoint, it’s probably AI.” This quote is probably the most accurate summarization of what has happened in AI over the past couple of years. A few months back, The Economist shared the chart below that shows the number of CEOs who mentioned AI in their Earnings calls. Towards the end of 2017, even Vladimir Putin said: “The nation that leads in AI ‘will be the ruler of the world.” Beyond all this hype, there is a lot of real technology that is being built. So how is 2019 going to look for all of us in the insurance world?
The field of artificial intelligence has always envisioned machines being able to mimic the functioning and abilities of the human mind. Language is considered as one of the most significant achievements of humans that has accelerated the progress of humanity. So, it is not a surprise that there is plenty of work being done to integrate language into the field of artificial intelligence in the form of Natural Language Processing (NLP). Today we see the work being manifested in likes of Alexa and Siri.
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 been using data to improve the customer experience of their policyholders for quite a while now. This data is typically explicit data that is gathered by asking policyholder specific questions. By gathering implicit data, insurers can now understand the sentiment of their customers at any given point of time during their customer journey, without even asking them.
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.