Why Big Data Is The Next Game Changer In Insurance Industry

Big Data Is The Next Game Changer

Big Data, automation techniques, smart technology, and advanced analytics are changing the dynamics of every industry. Whether it is retail, commercial properties, electronics or even insurance industry, there are thousands of use-cases and implementations of Big Data found everywhere. In the digital era, when technology adaptation is not a trend but a necessity to stay on the competitive edge, Big Data tools are being used by many industries for a faster data processing, better analytics, forecasting, and resource optimization.

The most common applications of Big Data are:

  • Better Customer Experience – Virtual assistants, Robo-advisors and self-learning, human-driven chatbots are being introduced in many industries for better user interaction and overall experience.
  • Process Automation – combined with Artificial Intelligence, it can be used to automate processes to replace manual labor and improve efficiency and transparency of the workflow.
  • New Opportunities – bulk data processing can lead to more attractive business models like peer-to-peer concepts and digital insurers.

Big Data In Insurance Industry

Insurers collect huge amounts of data on a regular basis. This data can be structured, like, Excel sheets or data-driven from another data source, and unstructured, for example, data gathered from a PDF, newspaper, utility bill or any such document. Big Data facilitates collection, organization, and analysis of such data which was not possible otherwise. Let’s look at some big benefits insurance industry can reap from Big Data:

  • Risk Assessment

Take an example of property insurance, imagine analyzing before-hand, how much that Arabian Ranches villa for sale that you’re interested in, is going to appreciate or depreciate in near future and assessing other risks associated with it. Risk assessment is one of the insurance industry’s major challenges. Using Big Data, insurers can effectively assess the risk associated with a potential customer by analyzing their data and using predictive data modeling techniques over it.

  • Better Fraud Detection

According to studies, 1 in every 10 insurance claims is filed fraudulently. Fraud detection has become a big challenge for many insurance companies. The capability of Big Data to analyze and process enormous datasets from many data sources, including telemetric and social media, can be the best weapon against fraudulent claims.

  • Analyzing More Complexities

Getting insurance to file false claims and acquire money from the insurance company is a common practice these days. People use complex data to make it seem seamless, to an extent that differentiating them from genuine ones seems impossible. Using Data Mining techniques, insurance companies can effectively analyze complex cases that require a lot of checking, hence catering to a bigger challenge faced by the insurance industry.

  • Faster Settlement

Many consumers complain about how claim settlement is a time-consuming process. The main reason for it is because it takes too much analysis, and this is where Big Data analytics comes to rescue. Its capacity of processing and analyzing huge datasets can make analyzing various aspects of a claim faster, fastening the overall process of claim settlements.

  • Customer Loyalty

With the insurance industry being huge in size, customer loyalty and retention is important for any insurer to get an edge over its competitors. By using Big Data, companies can analyze customer’s satisfaction or dissatisfaction using data gathered from their customer’s activities. They can also offer promotions, early-bird discounts and other attractive packages for better customer retention.

  • Reduced Cost

Insurance companies have a reputation to be dependent on manual processes. Business processes like underwriting, analysis, and risk assessment are still dependent on human interaction and hence more prone to errors. Big Data can automate various business process flows, thereby reducing manual labor and cost associated with those manual processes.

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