Identifying fraud in healthcare programs is crucial, as an estimated 3%-10% of the total healthcare expenditures are lost to fraudulent activities. This study presents a systematic literature review of machine learning techniques applied to fraud detection in health insurance claims. We aim to analyze the data and methodologies documented in the literature over the past two decades, providing insights into research challenges and opportunities. We identified research studies on health insurance fraud detection using machine learning approaches from databases such as Google Scholar, Springer-Link journals, Elsevier, PubMed, Excerpta Medica Database (EMBASE), Scopus, the Association for Computing Machinery (ACM) Digital Library, and the Institute of Electrical and Electronics Engineers (IEEE) Xplore Digital Library. We included only articles that presented experimental results of machine learning-based approaches applied to healthcare claims. From the reviewed articles, 137 were selected for the final qualitative and quantitative analyses. In recent years, there has been a surge in publications centered on the use of machine learning to detect health insurance fraud. Among these studies, those focused on the detection of fraud committed by healthcare providers was the most prevalent, followed by fraud committed by patients. A wide variety of machine learning algorithms are highlighted in these studies, ranging from unsupervised (41 studies) and supervised methods (94 studies), to hybrid approaches (12 studies). While traditional machine learning approaches remain dominant in this research area, the adoption of advanced deep learning techniques is on the rise. Considering the type of healthcare claims data used, 30 studies utilized private data sources, while the rest used publicly available datasets. Data from 16 countries were utilized, with a majority coming from the United States (96 studies), followed by China (11 studies) and Australia (5 studies). Detecting fraud in healthcare claims using machine learning presents several challenges. These include inconsistent data, absence of data standardization and integration, privacy concerns, and a limited number of labeled fraudulent cases to train models on. Future work should focus on enhancing transparency in data preparation, promoting the sharing of fraud investigation outcomes by authorities, and developing benchmark datasets to enhance accessibility and comparability. Furthermore, innovative techniques in data sampling, feature encoding methods for training machine learning models, and exploring the latest advancements in deep learning can significantly advance research in health insurance fraud detection.

Flooding in many areas is becoming more prevalent due to factors such as urbanization and climate change, requiring modernization of stormwater infrastructure. Retrofitting standard passive systems with controllable valves/pumps is promising, but requires real-time control (RTC). One method of automating RTC is reinforcement learning (RL), a general technique for sequential optimization and control in uncertain environments. The notion is that an RL algorithm can use inputs of real-time flood data and rainfall forecasts to learn a policy for controlling the stormwater infrastructure to minimize measures of flooding. In real-world conditions, rainfall forecasts and other state information are subject to noise and uncertainty. To account for these characteristics of the problem data, we implemented Deep Deterministic Policy Gradient (DDPG), an RL algorithm that is distinguished by its capability to handle noise in the input data. DDPG implementations were trained and tested against a passive flood control policy. Three primary cases were studied: (i) perfect data, (ii) imperfect rainfall forecasts, and (iii) imperfect water level and forecast data. Rainfall episodes (100) that caused flooding in the passive system were selected from 10 years of observations in Norfolk, Virginia, USA; 85 randomly selected episodes were used for training and the remaining 15 unseen episodes served as test cases. Compared to the passive system, all RL implementations reduced flooding volume by 70.5% on average, and performed within a range of 5%. This suggests that DDPG is robust to noisy input data, which is essential knowledge to advance the real-world applicability of RL for stormwater RTC.

Augmenting a dataset with synthetic samples is a common processing step in machine learning with imbalanced classes to improve model performance. Another potential benefit of synthetic data is the ability to share information between cooperating parties while maintaining customer privacy. Often overlooked, however, is how the distribution of the data affects the potential gains from synthetic data augmentation. We present a case study in credit card fraud detection using Generative Adversarial Networks to generate synthetic samples, with explicit consideration given to customer distributions. We investigate two different cooperating party scenarios yielding four distinct customer distributions by credit quality. Our findings indicate that institutions skewed towards higher credit quality customers are more likely to benefit from augmentation with GANs. Relative gains from synthetic data transfer, in the absence of feature set heterogeneity, also appear to asymmetrically favour banks operating on the lower end of the credit spectrum, which we hypothesise is due to differences in spending behaviours.

Existing frameworks for transfer learning are incomplete from a systems theoretic perspective. They place emphasis on notions of domain and task, and neglect notions of structure and behavior. In doing so, they limit the extent to which formalism can be carried through into the elaboration of their frameworks. Herein, we use the Mesarovician systems theory to define transfer learning as a relation on sets, and subsequently, characterize the general nature of transfer learning as a mathematical construct. We interpret existing frameworks in terms of ours and go beyond existing frameworks to define notions of transferability, transfer roughness, and transfer distance. Importantly, despite its formalism, our framework avoids the detailed mathematics of the learning theory or machine learning solution methods without excluding their consideration. As such, we provide a formal, general systems framework for modeling transfer learning that offers a rigorous foundation for system design and analysis.