Importance of Machine Learning Models in Healthcare Fraud Detection

Abstract

With the advent of technology and the improvements in AI, many healthcare institutions are struggling with the threat of fraud. As such, Healthcare fraud poses a significant threat to the healthcare industry, as it has led to numerous financial losses. In addition, there have been cases of compromised patient care due to the fraudsters being so advanced in their systems. The purpose of this research is to investigate the pivotal role of machine learning models and how they can be used to address the challenge of fraud. Many professionals have stated that machine learning models can enhance the accuracy and fairness of healthcare fraud detection. The ideas stem from the ability to leverage a diverse dataset of healthcare transactions, including claims and billing records. Other ideas include patient demographics, where a range of machine learning algorithms, like (Random et al.) and deep learning models (CNN, RNN), are significant in evaluating the performance of the technology. The results from this research show that machine learning models are better when compared to traditional approaches. These models can achieve high precision and recall scores. The models exhibit robustness, and they are able to show an ability to adapt to variations in fraud patterns. Therefore, machine learning models offer a promising avenue for healthcare organizations to combat fraud.