The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey

Detection of non-technical losses (NTL) which include electricity theft, faulty meters or billing errors has attracted increasing attention from researchers in electrical engineering and computer science. NTLs cause significant harm to the economy, as in some countries they may range up to 40% of the total electricity distributed. The predominant research direction is employing artificial intelligence to predict whether a customer causes NTL. This paper first provides an overview of how NTLs are defined and their impact on economies, which include loss of revenue and profit of electricity providers and decrease of the stability and reliability of electrical power grids. It then surveys the state-of-the-art research efforts in a up-to-date and comprehensive review of algorithms, features and data sets used. It finally identifies the key scientific and engineering challenges in NTL detection and suggests how they could be addressed in the future

Electronic fraud detection in the U.S. Medicaid Healthcare Program: lessons learned from other industries

It is estimated that between $600 and$850 billion annually is lost to fraud, waste, and abuse in the US healthcare system,with $125 to$175 billion of this due to fraudulent activity (Kelley 2009). Medicaid, a state-run, federally-matchedgovernment program which accounts for roughly one-quarter of all healthcare expenses in the US, has been particularlysusceptible targets for fraud in recent years. With escalating overall healthcare costs, payers, especially government-runprograms, must seek savings throughout the system to maintain reasonable quality of care standards. As such, the need foreffective fraud detection and prevention is critical. Electronic fraud detection systems are widely used in the insurance,telecommunications, and financial sectors. What lessons can be learned from these efforts and applied to improve frauddetection in the Medicaid health care program? In this paper, we conduct a systematic literature study to analyze theapplicability of existing electronic fraud detection techniques in similar industries to the US Medicaid program

Wine, Fraud and Expertise

While fraud has existed in various forms throughout the history of wine, the establishment of the fine and rare wine market generated increased opportunities and incentives for producing counterfeit wine. In the contemporary fine and rare wine market, wine fraud is a serious concern. The past several decades witnessed significant events of fine wine forgery, including the infamous Jefferson bottles and the more recent large-scale counterfeit operation orchestrated by Rudy Kurniawan. These events prompted and renewed market interest in wine authentication and fraud detection. Expertise in wine is characterized by the relationship between subjective and objective judgments. The development of the wine fraud expert draws attention to the emergence of expertise as an industry response to wine fraud and the relationship between expert judgment and modern science

Risk management in organic coffee supply chains : testing the usefulness of critical risk models

This report documents the findings of the analysis of the supply chain of organic coffee from Uganda to the Netherlands using a Chain Risk Model (CRM). The CRM considers contamination of organic coffee with chemicals as a threat for the supply chain, and analyses the consequences of contamination in one stage of the supply chain for the subsequent stages. Next to this, CRM also analyses the effectiveness and efficiency of measures to avoid or detect contamination. In a subsequent phase of this research, the CRM can also be used to analyse the consequences of fraud (= mixing of organic coffee and non-organic coffee) and measures to tackle fraud

Developing systems to control food adulteration

The objective of this study is to explore the current strategies available to monitor and detect the economically and criminally motivated adulteration of food, identifying their strengths and weaknesses and recommend new approaches and policies to strengthen future capabilities to counter adulteration in a globalized food environment. Many techniques are used to detect the presence of adulterants. However, this approach relies on the adulterant, or means of substitution, being "known" and an analytical method being available. Further techniques verify provenance claims made about a food product e.g. breed, variety etc. as well as the original geographic location of food production. These consider wholeness, or not, of a food item and so do not need to necessarily identify the actual adulterant just whether the food is complete. The conceptual framework developed in this research focuses on the process of predicting, reacting and detecting economically and criminally motivated food adulteratio