Updated: Mar 19
The insurance industry is a rather tricky sector but with business analytics making a move into the insurance industry, businesses are capitalizing on the technology to track and increase sales profitability, create robust databases for performance management, and predict sales for enhanced visibility of revenue. Working through the critical procedures of pricing, promotions, cash repressions, and constant risks, the insurance industry mitigates these problems to ensure compliance.
Generally, the insurance firms have been dependent solely upon the statistics and the data received regularly but recently, with the help of business analytics, there has been a standardized change to achieve the business goals. Business analytics provides actionable insights that can be used for plenty of business use cases. Insurers are constantly using business analytics to not only protect their companies from risks but also use consumer information to find potential growth opportunities.
The role of Business Analytics in the Insurance Industry can be seen through these 5 major use cases:
Real-time risk detection and prevention-
It is a well-known fact that the business in the insurance industry comes with a high-risk factor. Here, the business analytics performs a real-time risk analysis that empowers organizations and allows them to be swift in a volatile risk environment. For example, the ability to reliably determine the risk presented by a specific driver in the case of car insurance can allow insurers to determine a profitable-revenue plan. Insurance companies can now procure the slightest details, such as the car's braking activity and acceleration. Using business analytical modeling, by comparing the behavioral data of a driver with their vast database on the actions of other drivers, insurers can reliably determine the risk of the driver being involved in an accident.
Influencing Customer Behaviour
Insurance firms have also used business analytics to analyze telematics data and influence customer behavior. Health insurance providers, for example, will gather and analyze data generated from IoT devices and wearable technology such as fitness trackers to track variables that determine a person's health and quantify risk. Insurance firms may provide a thorough evaluation of the health of their clients by tracking actions and behaviors and advising customers to take better care of their health, thus minimizing the risk involved. Furthermore, insurance providers may provide services and discounts and encourage clients to use health tracking systems.
Personalized Marketing Strategies to target specific customers
The personalization of policies, plans, costs, reviews, and marketing advertisement not only attracts the consumers but, in turn, increases a company's insurance rates as well. Personalization is not a foreign concept in the insurance industry. Customers are always seeking the best-suited offers to fulfill their demands in terms of personalized offers, policies, loyalty programs, and recommendations. Business analytics extracts numerous details (lifestyle details, beliefs, interests, preferences, and more) from the widely extended database and assists insurance companies to offer their customers with the most highly personalized and appropriate experiences.
Detection Of Fraudulent Claims
Because of false claims, insurance firms suffer massive losses every year. Using business analytics that employ predictive models for effective fraud detection, improvements in data science technology have made it possible to identify frauds, dubious claims, and behavioral trends. This algorithm uses historical information on fraudulent activity to arrive at particular factors that predict the claims can be deceptive. The system halts the claim process and advises an investigation into the case if a claim is identified made by a person with a history of false claims.
It is of prime importance for an insurance industry to be able to forecast the events in the future. Being able to make precise predictions about claims can help minimize risks, achieve a competitive edge and reduce economic losses. Some of the most complicated processes involved in developing financial models having a large number of variables that influence the outcomes are driven by business analytics. To identify relationships between huge amounts of variables and to detect significant features essential to develop a customer portfolio, algorithms are proposed. Predicting the prospective claims can allow the insurance industry to improve their pricing models as well as develop optimum premiums.