Predictive Analytics: More Than A Buzzword For The Life Insurance Industry

Jul 07, 2018 | 1 year ago | Read Time: 4 minutes | By Manan Vyas

While it may seem like big data and predictive analysis are just some buzzwords that are used in this age of digitisation to assure customers that the insurance industry is changing with time, the reality is that it is transforming everything we know about the industry, especially premiums.

Big Data Analysis in Insurance

What is predictive analysis?

Predictive analysis or modeling is defined by industry analysts as when large sets of data are used to interpret systemic patterns over a long period of time, and make relationships and inferences between variables in the said data set. Which means that statistical tools are used to help any industry transform customer data into business rules, which in turns helps make better business decisions, improve efficiency and improve customer satisfaction.

When talking about the insurance industry we see that while predictive analysis and modeling is used to take information from the past, and work out what is expected to happen in the future using models. It has become a key tool in any insurer’s toolkit, and while it has already created a huge splash in the auto and property insurance industry, it is now seeping into the life insurance sector, completely transforming the way companies charge premiums.

Effects of predictive analysis in life insurance

Predictive models in the life insurance industry have gained increased prominence, with companies looking for guidance in all areas, including claims managements, premium auditing, target marketing, fraud detection and many more. A study conducted by global consulting firm McKinsey and Company entitled “Digital disruption in insurance: Cutting through the noise” talks about the “tripe prize” of such models, which includes satisfied customers, lower costs and higher growth.

One of the biggest impacts of predictive modeling is the change it has transpired in the underwriting process for insurance companies. While in the past underwriting was deemed as being lengthy, complex and invasive, such traditional methodologies have now been replaced with innovative technologies that mine the data from various primary and secondary sources. An industry study conducted about the impact of digitisation on life insurance titled, “Life insurance in the digital age: fundamental transformation ahead” highlights that such data techniques help in reducing the “length and invasiveness of risk assessment, improve risk selection and refine policy pricing.”

Predictive Analysis in Insurance Industry

Figure: Current global use of underwriting engines

In the past traditional methods of underwriting have cost insurers valuable time and money, however, with the advent of new models, this is all changing. Predictive models map out all factors including, age, family medical history, geographic location, type of work, just to name a few, to calculate the estimated cost of insuring a customer.

Another major change in the life insurance industry is the growing importance and use of wearable technology such as FitBit activity trackers and digital watches such as the Apple Watch which help in tracking a person’s day to day activities, which can help insurers evaluate and understand their lifestyle, which is critical to health assessment, and subsequently premium assessment. As per research conducted by consulting company Accenture, over a third of insurance companies now offer products and services modeled around the use of such devices. They not only help in providing more digital data to insurers, but also enable them to gain more insights into customer behavior.

Additionally, while predictive modelling is mostly being used in the industry currently for helping model health risks for insurers, market experts predict that in a few years it will help insurers predict results in other areas of uncertainty. This includes using models to evaluate which products customers are most likely to buy, thereby, helping transform customer value proposition, and make products more competitive and specifically tailored to customers. Other uses of predictive analysis include helping expand customer relationships and improve internal performances.

Insurance Companies Big Data Analysis

Figure: Challenges faced by companies using data analytics

Undeniably, there are still several challenges faced by the life insurance sector which prohibit insurers from realising the true benefits of predictive modeling, including restrictions of infrastructure, financial limitations, lack of expertise, cost of training employees, and the overall constraint on the availability of free exchange of digital data. However, companies are committed in helping both customers and the industry realise the true potential of big data and predictive modeling, and taking aggressive steps towards helping the same.

Predictive modeling is helping life insurance companies to segment and underwrite their risks through a more reliable, cost effective and accurate process. Such a transformation in efficiency and risk selection not only helps in capturing greater profits, but also pushes them to charge lower premiums from customers. Additionally, it is important to remember that it is not all just about numbers. Predictive modeling aims to increase overall access to insurance by making the industry more approachable, cost effective and friendly. Experts also predict that in some ways predictive modeling has the capability of pushing customers to adopt healthier lifestyles, and if it is able to do so, that would be the “ultimate victory.”

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