Analytics in the Insurance Industry

Analytics in the Insurance Industry
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Analytics is a topic that we cover in some depth in our blog posts here at GAP. In the past, we’ve covered everything from predictive analytics using machine learning to exploring SaaS analytics solutions that you can easily integrate with your existing software applications of systems.

In this blog post, we revisit analytics again, specifically, we look analytics in the insurance industry, how it is being deployed and explore some of the benefits that analytics is bringing to the insurance industry.

Download our Analytics Use-Cases to learn more about the benefits that analytics brings to insurance industry.

Risk Management

One of the most important things an insurer must do is undertake a risk assessment of the person that wants to take the insurance policy.  There are several ways to do this, but they have a common denominator – data.

In recent years, powered by the explosion of Big Data, the cloud and IoT devices, we’ve started to see the emergence of wearable or portable technology that lets insurers monitor policy holder’s lifestyles, health or even how they drive their cars.

Take for example John Hancock, an insurance company encouraging healthcare policyholders to use Fitbits to track how much they exercise each week.  The firm is offering discounts and has effectively gamified their policies by issuing points for activities. 30 points a day are given for physical activity, 200 points are given for dental screening, 400 points are given for getting your annual flu shot and up to 500 points are issued for doing things like 5k runs.

[bctt tweet=”There are several ways to forecast risk, but they all have a common denominator – data.” username=”GAPapps”]

By encouraging its policyholders to live more active lifestyles, the healthcare insurance provider can reduce the risk of unhealthy policyholders constantly claiming against John Hancock’s reserves.  All powered by analytics and big data.

Using AI for Insurance Industry: Retroacvtive Analysis vs Predictive Behavior

Fraud Detection

Unfortunately, money and fraud can often go hand in hand and the insurance industry is no exception to this.  Or policyholders that have been paying into their insurance providers policy for many years may feel they are “owed” something back!

Fraudulent claims most likely can’t be completely eradicated from the insurance industry but there is a way to reduce the likelihood of these types of claim from being made.

Analytics can be deployed to insurance providers data and surface intelligence based on customer information to figure out who is most likely to try and commit fraud before it even happens.  There are quite a few ways to surface these insights.  For example, software can monitor data from social media feeds (in real-time) to confirm the validity of specific events.  A policyholder might have made a claim stating that a tree fell through a window yet he or she has uploaded photos on their social media account and there is no sign of the tree.

A firm, Dataiku, has a product dedicated to this use case alone, named Data Science Studio. It leverages the power of predictive analytics to unearth anomalies, events, and behavior that simply doesn’t “fit” within regular activities.  It’s able to do this by ingesting data from multiple data sources or formats and can automate a lot of traditional tasks such as cleansing, formatting and parsing data and allows insurers to visualize how fraudulent activity might occur.

Download our Analytics Use-Cases to learn more about the benefits that analytics brings to insurance industry.

Consumer Insights

Risk assessment and fraud detection are important to the insurance industry, obviously. Another important dataset to track is that of customer behavior and attributes.  By mining customer data whether it be customer emails, social media channels or user forums.

Insurance providers can build up a picture of specific customer segmentations thereby letting insurance providers surface actionable insights, such as when a group of customers is likely to cancel their policy, which lets insurance providers be proactive in terms of retaining business.

If a customer hasn’t made a claim for a given number of years, the provider can offer a discount on their current policy or offer a different policy altogether.  All of this rich insight is made possible by applying analytics to existing datasets.

Customer Insights Data

Sales Automation

The days of the door to door insurance salesman are pretty much gone now.  Automation has disrupted the insurance industry like it has in others.  Automation is only one part of the puzzle, however.

Granted, automation has let insurers reduce the number of repetitive tasks they have to perform, such as data entry or compliance checks but on the other hand, it has resulted in whole new sets of newer technologies such as interactive websites that allow customers to purchase new policies. On the other hand, with the rise of NLP (natural language processing) and AI(artificial intelligence), we’re starting to see insurers such as Singapore Life offer chatbots over platforms such as Facebook Messenger which guides customers through a process that lets them obtain life insurance cover.

With more and more people getting comfortable with cloud technologies such as chatbots and interactive websites, the size of these automated datasets being generated is only going to grow exponentially.  By applying analytics to this information, insurers can deploy more efficient ways for new customers to buy insurance.

[bctt tweet=”The insurance industry uses #PredictiveAnalytics to unearth anomalies, events and behavior that lead to fraud” username=”GAPapps”]

Digital Marketing of Insurance Products

With increased automation and intelligent computer systems that can segment policyholders on specific behaviors and attributes, it creates the perfect cocktail to let insurers better target the market with their products and services. It’s all about the data and being able to analyze it!

Providers can now offer personalized products to potential customers, distribute e-mail newsletters or even activate social media ad-campaigns during a policy holder’s life events such as the arrival of a new baby.

For example, using AI and analytics, ad-tech solutions can be deployed to mine social media profiles to listen for signals and then serve the relevant advertising creative, thereby allowing providers to target the right person with the right message.

Additionally, with advancements in AI, software can now even detect the sentiment being expressed in a given stream of text (negative or positive). Some advancements can even detect the mood being expressed in digital images!  Having access to such insights can let digital marketers/insurance firms tailor their advertising creative accordingly and help boost customer retention, find new leads and so on.

Download our Analytics Use-Cases to learn more about the benefits that analytics brings to the insurance industry


In this blog post, we’ve explored how analytics can help fight fraud, improve insurance companies’ marketing efforts and even surface consumer insights whilst at the same time reduce the overall risk insurers get exposed to.  We’ve also seen how it informs pricing strategy which can help insurance companies maintain a competitive edge, and looked at how analytics can help guide an insurance companies marketing strategy.

We hope that by reading this that we’ve given you a better understanding of analytics and some of the real-world use cases it can be applied to.

Here at Growth Acceleration Partners, we have extensive expertise in many verticals.  Our nearshore business model can keep costs down whilst maintaining the same level of quality and professionalism you’d experience from a domestic team.

Our Centers of Engineering Excellence in Latin America focus on combining business acumen with development expertise to help your business.  We can provide your organization with resources in the following areas:

  • Software development for cloud and mobile applications
  • Data analytics and data science
  • Information systems
  • Machine learning and artificial intelligence
  • Predictive modeling
  • QA and QA Automation

If you’d like to find out more, then visit our website here.  Or if you’d prefer, why not arrange a call with us?

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