In the insurance industry, machine learning has been used to solve numerous types of problems such as fraudulent claims, risks for policies, claims predictions, and customer satisfaction. SpeedWise® Machine Learning (SML) can help insurance companies utilize customer data to forecast valuable insights that will result in profitability, improved business operations, and enhanced customer experience.

Use Cases

Fraudelent Claims

Insurance firms may use machine learning to detect possible fraudulent claims quicker and more reliably. Trained models may look for characteristics in both typical and atypical claims, as well as examine the characteristics of an atypical claim that has been identified as a fraudulent indicator. Forecasted false statements can then be flagged for further inquiry.

Risk for Policies

Machine learning can analyze historical data to predict the risk associated with policies. For example, if the policy is for home insurance, the model may utilize variables such as location, weather, and previous payments made to predict the level of risk to write the policy.

Claims Prediction

Machine learning can forecast future claims. A common example of claims that occur are due to payment errors made by the company for a customer. In this example, the machine learning model can detect that the payment made is unusual from the previous patterns and analysis of payments made. Accurate prediction gives a probability to decrease financial loss for the company.

Customer Retention

Insurance companies can use data to maximize customer retention. Historical data such as coverages, price changes and location can be used to train a model that predicts how customers will behave in terms of acquisition and churn. The model can then be used to deduce which factors contribute most to customer retention.