Project Abstract

Abstract
The insurance industry is constantly facing challenges related to fraudulent activities, particularly in the processing of insurance claims. Fraudulent claims not only lead to financial losses for insurance companies but also impact the overall credibility and trustworthiness of the industry. In an attempt to combat this issue, the development of advanced machine learning models for predicting insurance claim fraud has gained significant attention in recent years. This research project aims to develop a robust machine learning model specifically designed for predicting insurance claim fraud. The model will leverage historical data related to insurance claims, including various features such as claim amount, policyholder information, claim type, and previous claim history. By analyzing these features, the model will be trained to identify patterns and anomalies that are indicative of potential fraudulent activities. The research will begin with a comprehensive review of existing literature on machine learning techniques applied to fraud detection in the insurance industry. This review will provide insights into the different approaches, algorithms, and methodologies that have been previously used in similar contexts. Following the literature review, the research methodology will be outlined, detailing the steps involved in data collection, preprocessing, feature selection, model training, and evaluation. The methodology will also include a description of the dataset used for training and testing the machine learning model. The findings of the study will be presented and discussed in Chapter Four, focusing on the performance metrics of the developed machine learning model in predicting insurance claim fraud. The discussion will also explore the strengths and limitations of the model, as well as potential areas for improvement and future research directions. In conclusion, this research project aims to contribute to the ongoing efforts in the insurance industry to combat fraudulent activities through the development of a sophisticated machine learning model for predicting insurance claim fraud. By leveraging advanced data analytics techniques, the model has the potential to enhance fraud detection capabilities, thereby improving the overall efficiency and effectiveness of insurance claim processing systems.

Project Overview