InetSoft Webinar: How Predictive Analytics Software Can Help Transform the Insurance Industry

Below is the continuation of thetranscript of a Webinar hosted by InetSoft in May 2018 on the topic of Data Analytics in the Insurance Industry. The presenter is Christopher Wren, principal at TFI Consulting.

Well, good morning everybody my name is Natalie Chan. I'm delighted to be here today to talk to you about how predictive analytics can help transform the insurance industry. My background is in mathematical modeling and statistics, I have over 15 years experience of working in areas where these skills are applied in a practical setting.

I'm extremely passionate about using data to try to support evidence based decision making, and I've been working with organizations across a wide variety of sectors insurance, retail, banking and telcos to basically build and execute analytical strategies to ensure the success implementation of data driven and analytics.

I'm here today to talk to you about how predictive analytics can improve your customer's experience, increasing customer satisfaction and reduce cost. You might wonder why I want to focus on the customer experience. Well most of the analytics employed in the insurance industry is focused on identifying or reducing fraud, estimating and managing risk, and on improving customer retention.

However, reports from the insurance industry consistency highlight that the quality of customer experience remains the biggest factor driving customers to remain loyal or to switch to another insurance provider. We should focus on how to improve the quality of the customer experience rather than focusing solely on fraud.

#1 Ranking: Read how InetSoft was rated #1 for user adoption in G2's user survey-based index Read More

Here on the slide before you I am presenting a report which essentially states that those companies who excel in delivering a quality customer journey tend to win in the marketplace. Those with higher journey satisfaction scores of between an 8.8 and 9.2 reflect a much greater revenue growth compared to those with scores of between 6 and 8 present. I think it speaks for itself in that particular case.

Essentially we're talking about predictive analytics today because it works. It drives return on investment, and it's effective when measured across different areas of insurance organization. On the screen before you here you will see some examples of success stories associated with the use of predictive analytics in the insurance industry. For example, there in the bottom left you can see that Infinity Insurance increased separation recovery by 12 million dollars. In fact they actually also manage to increase their fast track rates from 2% to 22%.

What is predictive analytics? Very often in business we can ask ourselves the following questions, we can kind of consider what happened, why did it happen, what will happen and what should I do next. We would essentially consider answering the questions of what happened and why did it happened to be business intelligence. Many of you are probably doing some of that already. We would then consider answering the questions of what will happen, or what should I do next, to constitute advanced analytics. This is where leading insurance companies have a focus essentially.

view gallery
View live interactive examples in InetSoft's dashboard and visualization gallery.

This might sound a bit similar to the reference earlier to the fourth stage of analytics. What we have here is a view from a data implementation perspective. What does the predictive analytics insurance company looks like? The predictive analytics insurance company ensures that analytics is driven at an enterprise level. In this way predictive analytics can be embedded across the organization in a variety of ways. Now, earlier it was mentioned that the analytic insurers use analytics throughout the organization, so essentially that's what we're talking about.

For any one customer we can obtain data from a variety of sources, online, telephone, face-to-face among others. This information can be descriptive information which might include demographic information or claim history. It can be attitudinal information in the form of complaints or surveys, or it can be some sort of external data like weather data or social media data. There can be a variety of sources of data and then each data source can essentially be mashed up to give a 360 degree view of the customer. Using predictive analytics we can enhance a range of engagements from the claims process to customer retention, to supplier analysis among others.

Previous: The Concept of the Analytical Enterprise