What is the Impact of Big Data on Insurance?

This is undoubtedly the era of big data. In fact, the insurance industry operates on big data, and the success of its business model is based on analyzing this data to evaluate and make smart decisions.  Every action you perform with your computer, phone or any other device, generates data – it could be your phone tracking your location through GPS sensors, sharing a post on social media, and so on

What is big data?

It refers to the analysis and management of large volumes of data for use in recording, tracking, and predicting trends and patterns. Businesses in various sectors are inundated with vast swathes of data every day, both structured and unstructured. What matters is how the data is used and applied.

Big data is a relatively recent development in insurance and other industries, because prior to this, the size of data sets made it impossible to analyze using conventional techniques. But thanks to advancements in artificial intelligence and machine learning, big data can now be stored and accessed effectively. It has become all the more valuable for companies who wish to understand consumer trends and patterns.  For instance, telematics for car insurance policies are an excellent example of how smart devices can provide lucrative and accurate data that companies couldn’t access before.

Big data in the insurance industry

Insurance has always factored in data analysis such as accident statistics, policyholder’s personal information, along with third party sources to categorize people into several risk categories, prevent frauds, and optimize expenses. With the aid of big data, new sources of information can be accessed to create behavioral patterns for each particular customer and precisely determine their risk factor.

Pricing and Underwriting

Estimating premiums is a crucial aspect of auto insurance businesses, which are based on big data analytics and predictive models. Telematics data is used to monitor and analyze driving behavior. Based on the data, it can be established if the driver poses a great risk or adheres to safe habits. Thus insurance premiums can be decided accordingly. Some auto insurance companies offer good discounts on premiums if the telematics data proves that the drivers are following road safety rules without engaging in dangerous behavior.

When there is ample information to analyze, it becomes convenient for organizations to set policy premiums. In auto insurance, the insurance company can pore over information about most traveled areas, while determining the likelihood of the vehicle being damaged or discovering how prone that area is to road accidents. It lets them decide on the amount of premium to be charged to avoid losses. Here are the key aspects:

• Rewarding drivers – premium pricing will reflect risk behavior in a more accurate manner. Those who follow road safety rules and don’t engage in harsh braking, rapid acceleration, sharp turns, and other aggressive driving behavior, have to pay less. This practice can be used to promote better driving behavior and reduce risk at the same time. Drivers can be given incentives and rewards to promote safe driving habits.
• Economical premiums – when insurers understand the driver’s risk profile properly, they can provide closely tailored and more accurately priced premiums. An elaborate analysis of big data allows insures to identify risks and prescribe premiums accordingly. Those who maintain decent driving habits can be allotted economical premiums, while the ones being considered as “high risk” could have to pay much more.
• Predict driver behavior and reduce premiums – with the availability of new and informative data, insurers can now understand consumer risks better. The more insights they are able to glean from data about an individual, the more they understand the risks to which the insured is exposed, and potentially how to mitigate them. Thus they can take a proactive approach to reduce risk, instead of playing a reactive role to compensate for the cost, if an incident occurs. Insurers can develop services around this risk reduction, so drivers know how to bring down risk levels and along with insurance premiums too. 

Settling Claims

Settling policy claims is a tedious and extremely time-consuming process. It requires much effort on behalf of an adjuster to find out the loss amount and then recommend if a claim should be paid or not. In the age of big data analytics, the claim settlement process is being improved via automation and analysis of historical data to provide valuable information.

Machine learning algorithms make use of historical claims data as the basis of training by identifying dependent and independent variables. Big data analytics is used to score the claims in an order of priority so that claims with higher levels of complexity are assigned to the appropriate adjuster. This process saves time and prevents insurance companies from issuing the wrong settlement amounts.

Artificial intelligence and machine learning are vital in discovering the full potential of big data in insurance. Big data via telematics makes it possible to create an accurate driver profile. Thus, personalized product offers and risk-based pricing can be created by insurance companies.