Role of Machine Learning in the Insurance Industry
As the insurance industry continues to grow, it faces certain challenges related to customer retention, fraud detection, assessment of risk and many more. Advance Analytics, which includes Machine Learning (ML), may be the solution to such challenges.
Some of the Machine Learning use cases in the Insurance industry are explored below.
Assessment of Risk:
Risk management is very important for insurance industry. Insurance companies collect historical customer information, policy details etc. and do analysis and generate meaningful insights related to risk modelling.
Most insurance companies focus on customer retention and want to prevent attrition (or the churn) of customers. ML reveals key influencing factors that lead to customer churn. These factors are determined on the basis of multiple inputs like policy data and interaction data. Once the factors are ascertained timely measures could be taken to prevent customers churn.
Cross sell and Up sell:
Upsell and Cross Sell models recommend which product and services to sell to which set of existing customers. Recommendations are based on buying behavior of other customers that may possess similar characteristics. These cohesive groups are identified using algorithms and business rules.
For insurance companies, ML algorithms that can predict future claims are powerful tools. For example, ML technology has the ability to predict if a particular policy holder will initiate an Motor Insurance claim in the near future based on his/her driving history, age and type of vehicle. In the Health Insurance sector, this ability to predict claims behavior may drives various free wellness programs such as smoking cessation classes.
Based on more accurate claims forecasting, ML tools assist in price optimization of the insurance policy. This helps insurance companies to keep their premiums low, provide higher benefits and thereby gain more customers.
Going forward even the traditional Insurance companies will be compelled to use new age technology in order to survive and thrive in fiercely competitive markets.