Big Data transforming the insurance industry

In spite of its conservative approach to things, the insurance industry has hugely benefited from data science – Big Data, data analytics, and the likes. Insurance involves grouping people into different risk categories based on the policyholders’ personal data and accident statistics. 

Now that the big data world includes new sources of information, these are capable of generating real-time insights about a person’s lifestyle which can further be utilized to gain competitive advantage by the insurance companies. 

However, in spite of crossing significant milestones, market experts believe that the insurance sector is still not utilizing Big Data to its fullest capacity. The capabilities of Big Data is much more than its current levels of utilization.

What is the potential of big data in the insurance sector?

A 2018 study related to Big Data Analytics by Wikibon says that Big Data revenues are expected to rise to $103 billion by the year 2027. Another study by Accenture reports that about 79% of company executives feel that failing to leverage Big Data might even lead to the extinction of enterprises. (Source)

So, how can Big Data be better utilized for improving the insurance sector?

Leading data experts say that Big data techniques can be effectively leveraged in various aspects of the insurance sector, such as –

• Pricing and customer retention

Developing price models is one of the main challenges that insurance companies face, and that impacts their customer relationship too. Using advanced data science algorithms, companies can now detect early signs of customers’ dissatisfaction so that they can quickly act upon them. With customized data insights, companies can alter their pricing models if necessary, or offer better deals to maintain customer loyalty. 

• Product development and sales

Insurance companies can analyze massive amounts of customer data via social networks and feedback, to understand the preferences of their potential customers. Based on that understanding, companies can formulate better marketing strategies to target such customers. Or they might also consider launching new and improved products that better suit customers’ needs, like on-demand insurance schemes, and cybercrime insurance.

• Risk assessment

One of the prime challenges of insurance companies all over the world is to verify the customer’s data. Putting in human efforts to validate this data is next to impossible, which thereby leaves room for risks that can even jeopardize the company’s future. When Big Data predictive modeling is used, companies can just process data to forecast possible risks in the future. And accordingly, risk mitigation policies can be charted to prevent and minimize such chances.

• Fraud detection and prevention

According to reports released by the Coalition Against Insurance Fraud in the USA, every American insurance company loses more than $80 million from fraudulent activities. (Source) 

Now, if these insurance companies start using predictive modelling they can compare a potential customer’s profile with that of fraudulent profiles from the past which streamlines fraud detection activities. Big Data has helped improved fraud detection rates by as much as 60% today. 

Final words

The increased adoption of data science has gifted varied benefits to both customers and company stakeholders. So the future looks bright ahead; as per SNS Telecom & IT, insurance companies are expected to invest about $3.6billion in big data by 2021.