Big Data in Insurance: How Insurers can leverage Big Data Insights

Just like all the other industries affected by big data, insurance hasn’t been untouched either. The insurance industry is increasingly looking towards insights derived from analysis of big data. Initially, only 9% of the insurers were interested in big data projects, but that number rose up to 25% in 2014.

In today’s scenario, almost 40% of insurers consider big data a game changer. To add to this, a 2016 Accenture survey with 561 Chief Security Officers found that 94% of insurance CSOs believed that big data and other tech advancements would impact their business over the course of the next 5 years. And yet only 18% of CSOs, of the ones that were interviewed thought that their companies were prepared to leverage this new up-coming opportunity.

Benefits of using big data in Insurance

The use of big data in insurance offers a greater understanding of the risk factors involved and based on that can provide countermeasures to reduce risk and enhance insurability. A few ways to do that is using big data in the following aspects-

Policy choices
In many cases, data-based insights make it possible to alight better premium and reduce overall costs of the insurance. This has great economic and social benefits. New approaches to encourage behavior that leads to more caution in the future can be brought to life with big data. The new tech allows the role of insurance to grow from pure risk protection towards risk prediction.

Privacy and data protection concerns
It refers to the importance of balancing benefits of tailored products and individual risk assessment with the right to privacy, and at the same time ensuring fairness and non-discrimination.
In case of individualization of insurance policy, using big data for more sophisticated risk pricing can lead to personalized pricing for policyholders. This could mean that people with high risk could never afford insurance. On the other hand, better knowledge and assessments of risks can help make policies that cover different types of scenarios.

•  Actuarial/underwriting
There are the top two areas of big data applications. With 28% of P&C companies and 15% of L&A companies successfully using big data by 2014. Actuaries can now use multi-variety analysis and predictive modeling to achieve better results, which are most consistent and faster, making the regulators and consumers happy.

Claims
Claims are the biggest expenditure. 80% of the premiums go away in pay-out and other expenses. Now structures, as well as unstructured data, can be used to cut down on decision-making time and errors, ultimately resulting in a low costing service which has high customer satisfaction.

• Customer acquisition and retention.
2/3 of the insurers either use or are planning to use big data to secure most customers. Similarly, some firms are using advanced wrangling and analytics technique to extend new services to customers.

Fraud detection
Nearly 10% of all the claims made are fraud. The old methods of detection leave insurers open and vulnerable to the new types of fraud. From text mining to social network geospatial analytics, new sophisticated data analysis methods can increase detection, boost margins and reduce costs.

6 ways in which Insurers can use Big Data to their advantage

Risk assessment
One of the most important things for any insurer is to determine policy premiers. Mostly used by health, home, and automobile companies, they use in-vehicle communication devices and wearable like Fitbit, Apple watches etc. to calculate and determine risk.
By predictive modeling, the insurers can determine the kind of risks a person might be involved in. Activity trackers can now monitor the kind of lifestyle one leads and give relevant data to insurers.

Fraud detection
Insurers can reduce the number of fraud cases through data analysis and predictive modeling. They match the variables in every claim against the ones in fraud claims and see if any required a further investigation.
These variables could range from the person making the claim, the network of people associated with it and agencies involved in the claim.

Consumer insights
Acquiring a comprehensive understanding of consumer behavior, habits and needs is a strategic move that can help in identifying future behavior. The info gained from call center data, customer e-mails, social media, user forums etc. help the insurers making a unique profile for each consumer.
These insights not only provide info regarding the behavior of a consumer, but it also helps develop a trusted relationship with the consumer. As a result of this strategic learning, insurers achieve positive outcomes, such as solving consumer problems in real time.

Marketing
After gaining a full understanding of consumer behavior, insurance companies became more efficient in offering targeted products and services. This is done by offering personalized services, like low priced premium, family packages etc.

Automation
Insurers used to automate simple processes such as compliance checks, data entry, repetitive tasks that require less-initiative skills. With the rise of big data, a whole lot of new possibilities have been opened up. Now loan underwriting, reconciliation, property assessment, claims verification etc. can be done faster and more efficiently. As the industry moves more towards automation, insurers can save a lot of time and money with the help of machine learning.

• Smarter labor and finance
With the help of real-time analysis, daily adjustment can be made to premium rates, strategies, underwriting limits etc. Data mining techniques can also be used to cluster and score claims in order to prioritize and assign claims. These save insurers a huge amount of time and labor, preventing them from settling for high settlement amounts.


With the speed at which technology is evolving, it is paramount that we keep pace with it. If you are an insurer and are looking to use big data to your advantage, contact us at Datahut, your big data experts.

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