Workshop proceedings, special national workshop : prevention and detection of fraud, waste and abuse of public funds : Washington, D.C., November 1979

Proceedings of a workshop sponsored by the Law Enforcement Assistance Administration, Nov. 14-16, 1979

Point-and-shoot: rapid quantitative detection methods for on-site food fraud analysis – moving out of the laboratory and into the food supply chain

Major food adulteration and contamination events occur with alarming regularity and are known to be episodic, with the question being not if but when another large-scale food safety/integrity incident will occur. Indeed, the challenges of maintaining food security are now internationally recognised. The ever increasing scale and complexity of food supply networks can lead to them becoming significantly more vulnerable to fraud and contamination, and potentially dysfunctional. This can make the task of deciding which analytical methods are more suitable to collect and analyse (bio)chemical data within complex food supply chains, at targeted points of vulnerability, that much more challenging. It is evident that those working within and associated with the food industry are seeking rapid, user-friendly methods to detect food fraud and contamination, and rapid/high-throughput screening methods for the analysis of food in general. In addition to being robust and reproducible, these methods should be portable and ideally handheld and/or remote sensor devices, that can be taken to or be positioned on/at-line at points of vulnerability along complex food supply networks and require a minimum amount of background training to acquire information rich data rapidly (ergo point-and-shoot). Here we briefly discuss a range of spectrometry and spectroscopy based approaches, many of which are commercially available, as well as other methods currently under development. We discuss a future perspective of how this range of detection methods in the growing sensor portfolio, along with developments in computational and information sciences such as predictive computing and the Internet of Things, will together form systems- and technology-based approaches that significantly reduce the areas of vulnerability to food crime within food supply chains. As food fraud is a problem of systems and therefore requires systems level solutions and thinking.

Adaptive communal detection in search of adversarial identity crime

This paper is on adaptive real-time searching of credit application data streams for identity crime with many search parameters. Specifically, we concentrated on handling our domain-specific adversarial activity problem with the adaptive Communal Analysis Suspicion Scoring (CASS) algorithm. CASS's main novel theoretical contribution is in the formulation of State-of- Alert (SoA) which sets the condition of reduced, same, or heightened watchfulness; and Parameter-of-Change (PoC) which improves detection ability with pre-defined parameter values for each SoA. With pre-configured SoA policy and PoC strategy, CASS determines when, what, and how much to adapt its search parameters to ongoing adversarial activity. The above approach is validated with three sets of experiments, where each experiment is conducted on several million real credit applications and measured with three appropriate performance metrics. Significant improvements are achieved over previous work, with the discovery of some practical insights of adaptivity into our domain.

Image source detection : a case study on Facebook images taken by iPhones

Forged and tempered digital images become increasingly common on Facebook to aid computer frauds. The situation is worsened as many users can use a phone to take a photo and upload it to Facebook within two clicks, which highlights the need of image forensics for the cyber fraud cases. In this paper, we show the existence of the Facebook image filter which automatically changes the Facebook photos and consequently challenges the validity of forensic results. We aim to enable forensic investigators to relate a seized camera and a Facebook image. Specifically, we utilize intrinsic sensor pattern noise produced by a camera's lens to derive forensically useful information as Photo Response Non-Uniformity (PRNU) patterns. We propose to compare the PRNU patterns of a Facebook image and the flat field images produced by the candidate cameras. And we conclude this method to be effective by successfully identifying the correct iPhone from a list of four for a given Face book image.

Fraud, market reaction, and the role of institutional investors in Chinese listed firms

The extent and type of financial fraud committed by listed firms in China, stock market reaction to the detection and announcement of fraud, and the association between institutional ownership and financial fraud are the subjects of this article. Using fraud data from the period between 2001 and 2011, the authors find wide occurrences of fraud and a strong negative market reaction on the announcement date, particularly in cases of serious fraud. Fraud is more likely to occur at firms that have a smaller proportion of independent directors and at poorly performing firms. Firms with higher mutual fund ownership subsequently have fewer incidences of fraud. Our results reports by the authors indicate that ownership by independent institutions, such as mutual funds, serves as an effective monitoring mechanism, deterring fraud and enhancing corporate governance in Chinese capital markets.

Detecting Fraud: The Role of the Anonymous Reporting Channel

The purpose of this paper is to examine whether anonymous reporting channels (ARCs) are effective in detecting fraud against companies. Fraud, which comprises predominantly asset misappropriation, represents a key operational risk and a major cost to organisations (ACFE, http://www.acfe.com/uploadedFiles/ACFE_Website/Content/rttn/2012-report-to-nations.pdf, 2012; KPMG, http://www.kpmg.com/AU/en/IssuesAndInsights/ArticlesPublications/Fraud-Survey/Documents/fraud-bribery-corruption-survey-2012v2.pdf, 2012). The fraud triangle (incentives, opportunities and attitudes) provides a framework for developing our understanding of how ARCs can increase detection of fraud. Using publicly listed company survey data collected by KPMG in Australia—where ARCs are not mandated—we find a positive association between ARCs and reported fraud. These results indicate that ARCs are effective in detecting fraud. Additional analysis reveals that small firms derive the greatest benefit from adopting ARCs. We also find that independent boards do not directly influence the detection of fraud, but companies with independent boards detect more fraud because they implement ARCs.

Detection and Identification of Abnormalities in Customer Consumptions in Power Distribution Systems

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)