Correlating Medi-Claim Service by Deep Learning Neural Networks

Medical insurance claims are of organized crimes related to patients, physicians, diagnostic centers, and insurance providers, forming a chain reaction that must be monitored constantly. These kinds of frauds affect the financial growth of both insured people and health insurance companies. The Convolution Neural Network architecture is used to detect fraudulent claims through a correlation study of regression models, which helps to detect money laundering on different claims given by different providers. Supervised and unsupervised classifiers are used to detect fraud and non-fraud claims

Show Me Your Claims and I'll Tell You Your Offenses: Machine Learning-Based Decision Support for Fraud Detection on Medical Claim Data

Health insurance claim fraud is a serious issue for the healthcare industry as it drives up costs and inefficiency. Therefore, claim fraud must be effectively detected to provide economical and high-quality healthcare. In practice, however, fraud detection is mainly performed by domain experts resulting in significant cost and resource consumption. This paper presents a novel Convolutional Neural Network-based fraud detection approach that was developed, implemented, and evaluated on Medicare Part B records. The model aids manual fraud detection by classifying potential types of fraud, which can then be specifically analyzed. Our model is the first of its kind for Medicare data, yields an AUC of 0.7 for selected fraud types and provides an applicable method for medical claim fraud detection

A TAXONOMY OF MACHINE LEARNING-BASED FRAUD DETECTION SYSTEMS

As fundamental changes in information systems drive digitalization, the heavy reliance on computers today significantly increases the risk of fraud. Existing literature promotes machine learning as a potential solution approach for the problem of fraud detection as it is able able to detect patterns in large datasets efficiently. However, there is a lack of clarity and awareness on which components and functionalities of machine learning-based fraud detection systems exist and how these systems can be classified consistently. We draw on 54 identified relevant machine learning-based fraud detection systems to address this research gap and develop a taxonomic scheme. By deriving three archetypes of machine learning-based fraud detection systems, the taxonomy paves the way for research and practice to understand and advance fraud detection knowledge to combat fraud and abuse