Auto Insurance Fraud Prediction

Problem Statement: The goal of this project is to build a model that can detect auto or motor insurance fraud. The challenge behind fraud detection in machine learning is that frauds are far less common as compared to legit insurance claims. This type of problems is known as imbalanced class classification.

Frauds are unethical and are losses to the company. By building a model that can classify auto insurance fraud, I am able to cut losses for the insurance company. Less losses equates to more earning.

Relevance to businesses: Imbalance class problems are common in many industries. Many a times, we are interested in a minority class against another much bigger class or classes. For instance, classification of other types of frauds, classification of defective goods or customer churn.