How Data Science is Revolutionizing Insurance

Traditionally, insurance consulting strategies have been time consuming, and highly capital and people intensive. Lately, high-risk factors and steep competition is driving the industry to extensively rely on predictive analogies. The predictive analytics model helps insurance companies structure business strategies based on data pertaining to risks. But sifting through massive blocks of data to find relevant information is not easy. Thanks to data science, this chaos is fast transforming into a huge streamlined opportunity for insurance companies.

Data science combines the information collected from various sources and processes including information logs, wearable devices and drones with available consumer history and records to process information, analyze risks and predict possible outcomes. This enables insurance companies to offer solutions that increase customer attraction as well as retention rates. Here’s a deeper view of how data science is revolutionizing insurance.

Understanding risks

Insurance has two types of risks: pure and speculative. Risk assessment is an important part of insurance policies and companies deploy several strategies to prevent financial losses. Understanding risk relies upon risk quantification and information – the two issues that data science specializes in addressing. Data science has given rise to a unique ‘matrix model’ which generates risk information in real time. The algorithm detects and combines data from various sources on individual customers revealing their characters, behavioral patterns and preferences which in turn help tracking the potential risk that a particular set of customers carry. For example, an insurance organization used data science to understand the collective behavior of a fleet management company’s drivers and assessed the cumulative data to craft the group medical policy for the company.  Such capabilities make data science the ideal tool for assessing calculated risks.

Customizing marketing

Customers seek personalization in their insurance policies that align well with their lives and contexts. Data science fulfills this complex demand by extracting and analyzing information like demographic data, behavior, lifestyle choices, healthcare data, and related info. Equipped with this highly focused information, insurance companies can customize their existing policies to offer customers tailor-made policies that they are more likely to invest in.

Detecting fraud

Insurance frauds cause billions of dollars of financial loss every year. While fraud analysis has always been an integral part of insurance policies, the process has been further refined only recently by the influx of data science. By creating algorithms and feeding them with relevant data, insurance companies are able to detect unusual changes in user behavior which makes it possible to accurately determine fraudulent activities and suspicious links. A certain insurance company leveraged the power of data analytics to crack down on frauds in healthcare by analyzing the medical bills against plausibility, outcomes and behavior.

Predicting claims 

Predicting claims can be beneficial in saving time and especially money. In this context, data science is highly proficient in connecting claims with a host of other data around observations of customer’s general activities. This creates an individual customer’s profile offering a futuristic glimpse of the type of claims that can be made by the customer. Based on this information, insurance companies can price their policies accordingly and also, improve their costing models. These predictive capabilities go a long way in creating a competitive edge for insurance companies. Major organizations now use cloud storage devices as data repositories and link them with data analytics to understand consumer behavior.

Making recommendations 

Many insurance companies use recommendation engines which study customer responses to questionnaires or search history and make customized suggestions on the types of policies that will suit the requirements of each individual customer. They influence the customer’s buying decisions and preferences and also help insurance companies offer the exact policy that the lead might be interested in.

Businesses are fast adopting the marvels of technology and optimizing their functions. Data and application of the science behind it can help insurance companies understand their customers better, create tailor-made solutions for maximum customer engagement, and make informed business decisions. A step ahead for insurance companies is to broaden the outlook and focus not just on gathering data but also decipher ways to monetize the same through “data as a business” models.

References:

https://www.youtube.com/watch?v=m1M2n7kvzyM

https://www.mckinsey.com/industries/financial-services/our-insights/harnessing-the-potential-of-data-in-insurance

https://www.information-age.com/data-analytics-insurance-industry-123481252/

https://medium.com/activewizards-machine-learning-company/top-10-data-science-use-cases-in-insurance-8cade8a13ee1