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Thanks to Big Data Analytics, the Insurance Industry Is Transforming – Here is How

Insurance primarily is a business of providing risk management. It is typically a contingent or a hedge against the risk of an uncertain loss. From the very nature, it can be deciphered that a lot of data needs to be captured by the reps from the clients or customers seeking a safeguard. These data points are then crunched by the underwriters to calculate a premium and the risk associated with a particular client. The problem with this methodology is that it is not scalable.

Traditionally, while Insurance companies have been a laggard in harnessing the potential of the burgeoning data and the improving computation power, the situation is now changing. The whole industry has been disrupted by the advancements in technology. Not only the cost of collecting and storing data has plummeted down south, but other advancements like big data analytics, artificial intelligence and the Internet of Things have enabled the cost of insurance to come down by better aligning premiums and risks. According to researchby SNS Telecom & IT, big data investments in the insurance industry are expected to account for more than $2.4 billion by the end of 2018.

Here are some ways the insurance Industry can leverage the power of data and analysis –

  1. Product Development– By leveraging the state-of-the-art data reporting and analytics technologies, insurers can investigate their data, slice & dice them and do in-depth analysis at a granular level. The resulting analysis provides them the right inputs regarding the most used product features and which features contribute to the maximum performance. All this data can generate actionable insights into what tomorrow’s product should look like. Moreover, these insights can also trigger customizations in the already existing products so that the products are better aligned with the customer needs.
  2. Customer Segmentation– Insurers can have a comprehensive and 360-degree view of their customers by marrying the internal data (like ERP, CRM) along with external data such as social media feeds available to them. These data points subsequently help in tailoring both communication and products to provide more value.
  3. Customer Service– Text analytics combined with web scrapping tools can find out about the pain points customers are talking about on the web. These tools can also make sense out of heaps of unstructured data in the form of customer emails to alleviate the customer pain points.
  4. Employee Productivity – While an insurance agent is interacting with a customer not only is he collecting data about the customer, but the customer also has a list of queries for which she will seek clarification from the agent. By getting the correct profile of the customer, the right set of information can be provided to the agent who can subsequently answer the queries posed by the customer while
  5. Fraud Detection – The advent of data lake has made it possible for the insurers to store and crunch copious amounts of data. This data is then leveraged to arrive at fraud indictors. The indicators, used in tandem with predictive modeling, can assist the insurers in identifying possible fraudulent activities and claims by agents, employees, drug stores, hospitals, etc.
  6. Product Underwriting – Inputs from the external and the internal sources can drive to create a model which can improve the accuracy of the product underwriting. Recently American automakers have started monetizing the IoT data streaming in from connected cars by selling them to Insurers who can then use the data as inputs for the product underwriting.
  7. Customer Experience – This is more or less convergence of all the above points. The rightly tailored product with correct premium targeted to the right customer at the right time exponentially increases the customer satisfaction. Moreover, timely detection of fraud, enhancements in employee productivity, streamlining of underwriting process causes a lot of cost savings which can then be passed onto the customers.
  8. Boosting Sales – Having better visibility into potential targets and the potential influencers who can spread word of mouth go a long way in improving the bottom-line.
  9. Customer Retention – The underlying principle of this churn prediction is akin to the fraud detection aspect. From the historical data, the symptoms of a person dropping off can be identified and then used to determine the potential customers who might jump ship in the future. From there on extra attention can be paid to those customers in the form of customized products etc. to prevent the probable churn.

Data is disrupting the industry across the board, and Insurance is no exception. The market is evolving, and the new entrants are better prepared to address the expectations of the modern consumer. The old players will have to shift gears and move up the maturity ladder to become more efficient and define new value propositions.

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