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?
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.
Liu, Juan (Medallia) | Bier, Eric (Palo Alto Research Center) | Wilson, Aaron (Palo Alto Research Center) | Guerra-Gomez, John Alexis (Yahoo Labs) | Honda, Tomonori (Inflection.com) | Sricharan, Kumar (Palo Alto Research Center) | Gilpin, Leilani (Massachusetts Institute for Technology) | Davies, Daniel (Palo Alto Research Center)
Healthcare-related programs include federal and series of technical challenges. From a data representation state government programs such as Medicaid, view, healthcare data sets are often large and Medicare Advantage (Part C), Medicare FFS, and diverse. It is common to see a state's Medicaid program Medicare Prescription Drug Benefit (Part D). Nonhealth-care or a private healthcare insurance program having programs include Earned Income Tax hundreds of millions of claims per year, involving Credit (EITC), Pell Grants, Public Housing/Rental millions of patients and hundreds of thousands of Assistance, Retirement, Survivors and Disability Insurance providers of various types, for example, physicians, (RSDI), School Lunch, Supplemental Nutrition pharmacies, clinics and hospitals, and laboratories. Assistance Program (SNAP), Supplemental Security Any fraud-detection system needs to be able to handle Income (SSI), Unemployment Insurance (UI), and the large data volume and data diversity. While healthcare data (insurance claims, health Data patterns from both sides are dynamic. The complexity records, clinical data, provider information, and others) of the problem calls for a rich set of techniques offers tantalizing opportunities, it also poses a to examine healthcare data. Healthcare financials are complex, involving a from a suspicious individual or activity (as singled multitude of providers (physicians, pharmacies, clinics out by the automated screening components) and and hospitals, and laboratories), payers (insurance interacts with the system to navigate through data plans), and patients. To design a good fraud-detection items and collect evidence to build an investigation system, one must have a deep understanding of the case. The two categories have quite different technical financial incentives of all parties. Starting from database indexing/caching for fast data retrieval and domain knowledge, auditors and investigators have user interface design for intuitive user-system interaction.
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.
A common need when you are analyzing real-world data-sets is determining which data point stand out as being different to all others data points. Such data points are known as anomalies. This article was originally published on Medium by Davis David. In this article, you will learn a couple of Machine Learning-Based Approaches for Anomaly Detection and then show how to apply one of these approaches to solve a specific use case for anomaly detection (Credit Fraud detection) in part two. A common need when you analyzing real-world data-sets is determining which data point stand out as being different to all others data points.