Predictive Analytics For Life Insurance



The insurance sector is blessed with data in large volumes. Data being the oil in today’s time, most of the organizations do not have the means to utilize it to their advantage. It could either be a lack of technological awareness or being too comfortable with legacy systems. Either way, it is the insurer who suffers the most in terms of finances and time.

The recent years have witnessed a lot of companies investing in business intelligence and analytics to make the data work for them. One of the most important features available for the insurer is the analytics tool for predictions.



Predictive analytics basically consists of analyzing a large set of data to make interpretations or to identify meaningful relationships and using the same to predict future events. Having said that, Predictive Analytics does not tell you what WILL happen in the future. However, using what-if situations and assessing risks, it can forecast what MIGHT happen in the future with an acceptable level of reliability.

As an application to businesses, predictive models are used to analyze the current data and historical events to understand customer behaviour and to identify the potential risks and opportunities for the companies. With the use of different techniques such as data mining, statistical modelling and in some cases applying Machine Learning for an AI edge, Predictive Analytics can help create forecasts.


As an enabler of Big Data, predictive analytics uses the combination of customer insights and historical data in addition to the vast real-time customer data collected by the company to forecast future events. For e.g. a store that implements a loyalty system can store data about their customers. These include basic information as well as purchase history. Using this data, the predictive model can give an idea as to which promotions can entice the customer to buy another product from their company. Similar can be achieved by recommending products to the user on websites.



The life insurance industry is a well know adopter of statistics and data analytics. However, they are not yet using analytics to its full potential to improve the insurance buying process as other business segments are doing. For instance, the property and casualty insurance make use of generalized linear and credibility models along with credit scoring models for driving intelligent business decisions.

Life insurance is like a protection cover for individuals to save them from the financial effects of untimely mortality. Life insurance agents can estimate life expectancy with the use of mortality tables. Also, underwriting techniques help in assessing risks. Although this is a widely used technique across the industry, it comes at a cost of time and money. Typically, for every application, the life insurer must invest almost a month’s time and hundreds of dollars for underwriting which then causes the premiums to be high.




By using individual scores for multiple risk factors and the collaboration between them, clustering techniques can also be used for the same. For e.g., if we look at a high-risk factor like ‘smoker’ or someone with a ‘heart condition’, the predictive model can give accurate results by combining the two and adding the individual’s age. So, the individual risk multiplies if they are present in the same individual which elevates to a risk of a different magnitude.


The processing of claims should be upfront and without any variability. The client presents the Policy to the insurer, a proof of the unfortunate event and collect money from the insurance company. However, there is still room for fraud. Predictive analytics can help uncover fraudulent claims by analyzing the circumstances that were involved in the said event.

For e.g. In case of a car accident, the condition of the tires, speed of the car and visibility during the action can throw light on the claims filed. This can help identify whether the accident was due to driver negligence or misfortune.

ABOUT EXPONENTIA DATALABS provides intelligent products/platforms capable of automated cognitive decision making to improve productivity, quality, and economics of the underlying business processes. Our artificial intelligence, NLP and machine learning-based platforms integrate into existing systems to provide a seamless experience to business users. The highly customizable and modular platforms have a short deployment cycle, provide tangible process KPI improvements right from the first month of deployment and have demonstrable high ROI across the existing clients.

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