Jul 2026· Magna Scientia Advanced Research and Reviews· 0 citations
TL;DR
Results demonstrate the potential of machine learning, combined with NLP and predictive analytics, to add value in terms of detection accuracy, false-positive rate reduction, and near real-time fraud prevention.
Abstract
Healthcare fraud is a major problem in the United States, which annually totals tens of billions of dollars and impacts financial sustainability, as well as patient confidence. Traditional forms of detection, such as manual risk audits and static category rule-based solutions, are becoming less effective in the face of fraudster sophistication and ever-evolving fraudulent tactics. Based on a systematic review of peer-reviewed articles, policy reports, and industry white papers, this article examines how artificial intelligence (AI) can be used to reduce financial fraud in U.S. healthcare systems. Results demonstrate the potential of machine learning, combined with NLP and predictive analytics, to add value in terms of detection accuracy, false-positive rate reduction, and near real-time fraud prevention. Moreover, integration with AI human-in-the-loop is demonstrated to increase efficiency with retention of supervision. However, some obstacles persist around data quality, bias of algorithms, interpretability, and regulatory aspects. In all, AI has strong potential as a game-changer in the fight against healthcare fraud if it is rolled out subject to robust governance and ethical considerations.
A conceptual model demonstrating how AI-enabled analytics techniques -- encompassing supervised machine learning, unsupervised anomaly detection, deep learning, and graph-based network analytics -- directly and indirectly enhance fraud detection accuracy, response speed, and organisational risk posture is developed.
Prof. Roopa U Prof. Roopa U, Shrushti S Nelogi Shrushti S Nelogi· International Scientific Jou...· 0 citations
The review highlights the transformative potential of AI while emphasizing the need for ethical, transparent, and secure implementation strategies to maximize effectiveness in combating increasingly sophisticated financial fraud schemes.
G. Onyarin· International journal of res...· 0 citations
The Centers for Medicare & Medicaid Services (CMS) is responsible for the provision of healthcare coverage to more than 150 million beneficiaries; however, the enterprise systems of CMS suffer from various issues, namely data interoperability, inefficiency, and fraud, waste, and abuse. Rule-based mechanisms have proven to be inadequate for fraud prevention in the changing environment, whereas the fragmentation of datasets of CMS limits the effectiveness of any analysis. Therefore, this paper aims to propose an integrated AI-based framework for healthcare analytics, which includes interoperability, predictive analytics, anomaly detection, and explainable artificial intelligence for large-scale fraud detection. In particular, the suggested framework includes a novel FHIR-like interoperability module that would allow to align heterogeneous datasets within CMS into a single patient-provider-focused data lake. A hybrid approach to the fraud detection algorithm implementation is introduced based on supervised machine learning methods (Logistic Regression, Random Forest, XGBoost, LightGBM, CatBoost) with the use of imbalance aware training. Moreover, the framework incorporates unsupervised anomaly detection algorithms (Isolation Forest, Local Outlier Factor) and graph-based network analysis of providers to detect relational fraud. Experimental results on a dataset with over 1.1 million samples show that XGBoost outperforms other classifiers, producing the highest accuracy at 99.64%, the highest ROC-AUC at 0.9998, and the highest F1-score at 0.96. Unsupervised models continue to detect anomalous providers at a rate of 2%, whereas graph-based analysis detects suspicious communities among providers at 58% of the total set. Claim frequency and Medicare payment amounts appear as features used most frequently by feature attribution in detecting fraudulent activities.
Partha Pratim Saha, Md Ranjan Paul, Rezaul Karim Khan et al.· Journal of Intelligent Decis...· 5 citations
It is argued that artificial intelligence is best understood as an instrument of triage rather than adjudication, and it draws out the governance, forensic, and pedagogical consequences of that position for both mature and emerging markets, including African jurisdictions such as Ghana.
Dr. Gaduga Godwin, Esq· International Journal of inn...· 0 citations
The rapid growth of digital banking, e-commerce, electronic payments, and financial technology has increased both the volume and complexity of financial transactions, leading to greater fraud risks. Traditional rule-based fraud detection systems struggle to identify evolving and sophisticated fraud patterns. Artificial Intelligence (AI) offers an effective solution through machine learning, pattern recognition, and predictive analytics. This study explores AI-based fraud prevention systems in financial services, focusing on data acquisition, preprocessing, feature engineering, classification, anomaly detection, and real-time monitoring. Various machine learning models, including Random Forest, Support Vector Machines, Artificial Neural Networks, and Deep Learning, are evaluated for fraud detection. The findings indicate that AI-driven systems significantly improve fraud detection accuracy, reduce false positives, minimize operational losses, and enhance customer trust. The study concludes that AI is a critical component of modern financial security infrastructure and will play an increasingly important role in combating financial fraud.
E. Harris· International Journal of Com...· 0 citations
Insurance fraud is a big problem that the insurance industry is trying to solve. Economic loss, operational costs, and a decline in consumer trust are all consequences of insurance fraud. Traditional techniques of fraud detection, such as rule-based systems and human processes, frequently fail to detect more sophisticated fraud schemes. By analyzing complicated data in real-time, artificial intelligence (AI) has been shown to be an effective tool for automating the identification of fraud. Machine learning, explainable AI, federated learning, deep learning, reinforcement learning, natural language processing, and the most current approaches to AI methods in insurance fraud detection are included in this review. The paper also contains a discussion on traditional fraud detection methods, most popular insurance frauds, data preprocessing methods and some common metrics. Additionally, it looks at the latest research studies and points out the advantages and disadvantages of the available AI-driven approaches. Data quality, class imbalance, privacy preservation, model interpretability, scalability and regulatory compliance are among the key challenges that are critically analyzed.
Amit Jain· International Journal of Adv...· 0 citations
