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Introduction to Anomaly Detection: Concepts and Techniques

#artificialintelligence

Machine Learning has four common classes of applications: classification, predicting next value, anomaly detection, and discovering structure. Among them, Anomaly detection detects data points in data that does not fit well with the rest of the data. It has a wide range of applications such as fraud detection, surveillance, diagnosis, data cleanup, and predictive maintenance. Although it has been studied in detail in academia, applications of anomaly detection have been limited to niche domains like banks, financial institutions, auditing, and medical diagnosis etc. However, with the advent of IoT, anomaly detection would likely to play a key role in IoT use cases such as monitoring and predictive maintenance. This post explores what is anomaly detection, different anomaly detection techniques, discusses the key idea behind those techniques, and wraps up with a discussion on how to make use of those results. Is it not just Classification?


Graph Analysis for Detecting Fraud,Waste, and Abuse in Healthcare Data

AAAI Conferences

Detection of fraud, waste, and abuse (FWA) is an important yet difficult problem. In this paper, we describe a system to detect suspicious activities in large healthcare claims datasets. Each healthcare dataset is viewed as a heterogeneous network of patients, doctors, pharmacies, and other entities. These networks can be large, with millions of patients, hundreds of thousands of doctors, and tens of thousands of pharmacies, for example. Graph analysis techniques are developed to find suspicious individuals, suspicious relationships between individuals, unusual changes over time, unusual geospatial dispersion, and anomalous networks within the overall graph structure. The system has been deployed on multiple sites and data sets, both government and commercial, to facilitate the work of FWA investigation analysts.


Management AI: Anomaly Detection And Machine Learning

#artificialintelligence

When a person drives, there are many things that are quickly noticed and then ignored. What gains attention are those things that might be a danger. A pedestrian who might walk out into the road, a light turning yellow, an adjacent car drifting into the same lane, all of those need special attention. The same thing is true in the world of business computing. For instance, a sudden increase in sales is great, but the company needs to track that anomalous increase back to its cause in order to identify and replicate the reason.


Graph Analysis for Detecting Fraud, Waste, and Abuse in Health-Care Data

AI Magazine

Detection of fraud, waste, and abuse (FWA) is an important yet challenging problem. In this article, we describe a system to detect suspicious activities in large healthcare data sets. Each healthcare data set is viewed as a heterogeneous network consisting of millions of patients, hundreds of thousands of doctors, tens of thousands of pharmacies, and other entities. Graph-analysis techniques are developed to find suspicious individuals, suspicious relationships between individuals, unusual changes over time, unusual geospatial dispersion, and anomalous network structure. The visualization interface, known as the network explorer, provides a good overview of data and enables users to filter, select, and zoom into network details on demand.


Graph Analysis for Detecting Fraud, Waste, and Abuse in Healthcare Data

AI Magazine

Detection of fraud, waste, and abuse (FWA) is an important yet challenging problem. In this article, we describe a system to detect suspicious activities in large healthcare datasets. Each healthcare dataset is viewed as a heterogeneous network consisting of millions of patients, hundreds of thousands of doctors, tens of thousands of pharmacies, and other entities. Graph analysis techniques are developed to find suspicious individuals, suspicious relationships between individuals, unusual changes over time, unusual geospatial dispersion, and anomalous network structure. The visualization interface, known as the Network Explorer, provides a good overview of data and enables users to filter, select, and zoom into network details on demand. The system has been deployed on multiple sites and datasets, both government and commercial, and identified many overpayments with a potential value of several million dollars per month.