Outlier Mining Methods Based on Graph Structure Analysis

Outlier detection in high-dimensional datasets is a fundamental and challenging problem across disciplines that has also practical implications, as removing outliers from the training set improves the performance of machine learning algorithms. While many outlier mining algorithms have been proposed in the literature, they tend to be valid or efficient for specific types of datasets (time series, images, videos, etc.). Here we propose two methods that can be applied to generic datasets, as long as there is a meaningful measure of distance between pairs of elements of the dataset. Both methods start by defining a graph, where the nodes are the elements of the dataset, and the links have associated weights that are the distances between the nodes. Then, the first method assigns an outlier score based on the percolation (i.e., the fragmentation) of the graph. The second method uses the popular IsoMap non-linear dimensionality reduction algorithm, and assigns an outlier score by comparing the geodesic distances with the distances in the reduced space. We test these algorithms on real and synthetic datasets and show that they either outperform, or perform on par with other popular outlier detection methods. A main advantage of the percolation method is that is parameter free and therefore, it does not require any training; on the other hand, the IsoMap method has two integer number parameters, and when they are appropriately selected, the method performs similar to or better than all the other methods tested.Peer ReviewedPostprint (published version

Economic Concepts of Organic Certification

Certification is a key element in marketing organic food products. Based on economic theory, this report wants to illustrate the economic reasoning for certification. The intention is to provide a description of economic concepts, which is understandable for a wider audience. We are focusing on the basic economic literature. Chapter 1 “Organic certification system” describes the current control system in the European Union. Why this is necessary, will then be explained based on a synopsis of economic literature. Of specific significance for organic certification and the CERTCOST project are the concepts of institutional economics and economics of crime. The relevant points of economic theory will be presented and discussed in chapter 2 “Theoretical framework”. Finally, the costs and benefits of organic certification will be illustrated in chapter 3 “Costs and benefits of organic certification”

The Effectiveness of Insurance Fraud Statutues: Evidence from Automobile Insurance

Insurance fraud, which adds an estimated $85 billion per year to the total insurance bill in the U.S., is an extremely serious problem for consumers, regulators, and insurance companies. This paper analyzes the effects of state legislation and market conditions on automobile insurance fraud from 1988 to 1999, a period representing a substantial increase in the enactment of antifraud legislation. Our empirical results show that the laws have mixed effects; two laws have no statistically significant effect on fraud. The strongest evidence of fraud mitigation effects are associated with mandatory Special Investigation Units, classification of insurance fraud as a felony, and mandatory reporting of professionals to licensing authorities. However, laws requiring insurers to report potentially fraudulent claims to law enforcement authorities increase fraud, which may reflect some substitution from more efficacious private efforts to less productive state activity. Many underlying characteristics of the market also affect fraud.Insurance Fraud, Automobile Insurance, Moral Hazard

Chip and Skim: cloning EMV cards with the pre-play attack

EMV, also known as "Chip and PIN", is the leading system for card payments worldwide. It is used throughout Europe and much of Asia, and is starting to be introduced in North America too. Payment cards contain a chip so they can execute an authentication protocol. This protocol requires point-of-sale (POS) terminals or ATMs to generate a nonce, called the unpredictable number, for each transaction to ensure it is fresh. We have discovered that some EMV implementers have merely used counters, timestamps or home-grown algorithms to supply this number. This exposes them to a "pre-play" attack which is indistinguishable from card cloning from the standpoint of the logs available to the card-issuing bank, and can be carried out even if it is impossible to clone a card physically (in the sense of extracting the key material and loading it into another card). Card cloning is the very type of fraud that EMV was supposed to prevent. We describe how we detected the vulnerability, a survey methodology we developed to chart the scope of the weakness, evidence from ATM and terminal experiments in the field, and our implementation of proof-of-concept attacks. We found flaws in widely-used ATMs from the largest manufacturers. We can now explain at least some of the increasing number of frauds in which victims are refused refunds by banks which claim that EMV cards cannot be cloned and that a customer involved in a dispute must therefore be mistaken or complicit. Pre-play attacks may also be carried out by malware in an ATM or POS terminal, or by a man-in-the-middle between the terminal and the acquirer. We explore the design and implementation mistakes that enabled the flaw to evade detection until now: shortcomings of the EMV specification, of the EMV kernel certification process, of implementation testing, formal analysis, or monitoring customer complaints. Finally we discuss countermeasures

A Study on the Efficient Estimation of the Payment Intention in the Mail Order Industry

AbstractThis paper presents investigating the customer payment intention prediction in the mail order industry. As the B2C market expands their market volume, the fraud transactions increase in number. The primary indicator for the detection are the shipping address, the recipient name, and the payment method. These information usually make use of the prediction in the Japanese mail order industry. Conventional detecting method for the fraud depends on the human working experiences so far. As the number of transaction becomes large, fraud detection becomes difficult. The mail order industry needs something new method for the detection. The result of the Google Flu Trends shows, accurate prediction needs the heuristics knowledge. For these backgrounds, we observe the transaction data with the customer attribute information gathered from a mail order company in Japan and characterized the customer with machine learning method. From the results of the intensive research, potential fraudulent transactions are identified. Intensive research revealed that the classification of the deliberate customer and the careless customer with machine learning. This result will make use of the customer screening at the time of order received

An Investigation of Fraud in Nonprofit Organizations: Occurrences and Deterrents

Losses due to fraudulent activities are particularly troublesome in the nonprofit sector because they directly reduce resources available to address tax-exempt purposes. The ensuing bad publicity may also reduce contributions and grants in subsequent periods. This paper uses data provided by Certified Fraud Examiners to report on the types of fraud they identified in nonprofit organizations and the characteristics of both the victims and the perpetrators of the fraudulent activities. Based on the analysis of the data, the authors suggest ways that fraud losses can be prevented or mitigated. In particular, governing boards are urged to consider important controls in addition to the annual financial statement audit.This publication is Hauser Center Working Paper No. 35. The Hauser Center Working Paper Series was launched during the summer of 2000. The Series enables the Hauser Center to share with a broad audience important works-in-progress written by Hauser Center scholars and researchers