Advanced Neural Networks for Detecting Medical Billing Anomalies and Predictive Fraud Prevention (The Science Post, 2026)
Healthcare fraud, particularly through aberrant medical billing, imposes substantial financial burdens on public healthsystems and compromises patient trust. Traditional rule-based audit systems fail to capture sophisticated, evolvinganomalous patterns. This study addresses the gap in dynamic, scalable fraud detection by proposing a deep learningframework combining autoencoders and long short-term memory (LSTM) networks. The primary purpose is todevelop and validate an advanced neural network model capable of identifying billing anomalies in real time andpredicting fraudulent claims before payment disbursement. Using a quantitative research design, we analyzed asynthetic dataset mimicking 500,000 Medicare claims, incorporating features such as procedure codes, patientdemographics, and provider behavior patterns. The methodology involved training a hybrid autoencoder-LSTMmodel for unsupervised anomaly detection and supervised classification. Key findings indicate that the proposedmodel achieves a 96.4% fraud detection recall and an area under the curve (AUC) of 0.98, significantlyoutperforming logistic regression and random forest baselines. The conclusion underscores that integrating predictivebusiness analytics with neural networks can reduce improper payments by an estimated 32%, offering a scalablesolution for US public health systems and private payers.