Fraud Detection and Analysis for Insurance Claims Using Machine Learning

• Python
• Machine Learning

Fraud is on the rise across all industries, costing the insurance sector billions of dollars annually, according to estimates. Insurance fraud is an illegal conduct that is done on purpose in order to profit financially. This is currently the most serious issue that numerous insurance companies throughout the world are facing. The majority of the time, one or more gaps in the investigation of false claims has been identified as the primary factor. Insurance fraud is a dishonest conduct that is routinely committed in order to profit financially. These false claims cost the insurance industry a lot of money and cause billions in unnecessary expenses every year. Because of this, the urge to use computer solutions to stop fraud activities grew, offering clients not only a dependable and stable environment but also drastically lowering fraud claims. Since the losses are covered by raising policyholders? premium costs, these frauds have a negative impact on society. The traditional claim investigation procedure has also been blamed for producing unreliable conclusions because it is time-consuming and laborious. So, utilising Random Forest Classifier, we develop a machine learning-based framework for fraud detection in this research. We demonstrated the results of our research by automating the evaluation of insurance claims using a variety of data methodologies, where the detection of erroneous claims would be done automatically using Data Analytics and Machine Learning techniques. Additionally, the system may be able to create heuristics for fraud signs. As a result, this strategy has a favourable effect on the entire insurance business by boosting both firm reputation and customer happiness.