Cost-Sensitive Selective Classification and its Applications to Online Fraud Management

abstract: Fraud is defined as the utilization of deception for illegal gain by hiding the true nature of the activity. While organizations lose around $3.7 trillion in revenue due to financial crimes and fraud worldwide, they can affect all levels of society significantly. In this dissertation, I focus on credit card fraud in online transactions. Every online transaction comes with a fraud risk and it is the merchant's liability to detect and stop fraudulent transactions. Merchants utilize various mechanisms to prevent and manage fraud such as automated fraud detection systems and manual transaction reviews by expert fraud analysts. Many proposed solutions mostly focus on fraud detection accuracy and ignore financial considerations. Also, the highly effective manual review process is overlooked. First, I propose Profit Optimizing Neural Risk Manager (PONRM), a selective classifier that (a) constitutes optimal collaboration between machine learning models and human expertise under industrial constraints, (b) is cost and profit sensitive. I suggest directions on how to characterize fraudulent behavior and assess the risk of a transaction. I show that my framework outperforms cost-sensitive and cost-insensitive baselines on three real-world merchant datasets. While PONRM is able to work with many supervised learners and obtain convincing results, utilizing probability outputs directly from the trained model itself can pose problems, especially in deep learning as softmax output is not a true uncertainty measure. This phenomenon, and the wide and rapid adoption of deep learning by practitioners brought unintended consequences in many situations such as in the infamous case of Google Photos' racist image recognition algorithm; thus, necessitated the utilization of the quantified uncertainty for each prediction. There have been recent efforts towards quantifying uncertainty in conventional deep learning methods (e.g., dropout as Bayesian approximation); however, their optimal use in decision making is often overlooked and understudied. Thus, I present a mixed-integer programming framework for selective classification called MIPSC, that investigates and combines model uncertainty and predictive mean to identify optimal classification and rejection regions. I also extend this framework to cost-sensitive settings (MIPCSC) and focus on the critical real-world problem, online fraud management and show that my approach outperforms industry standard methods significantly for online fraud management in real-world settings.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Credit Card Fraud: A New Perspective On Tackling An Intransigent Problem

This article offers a new perspective on battling credit card fraud. It departs from a focus on post factum liability, which characterizes most legal scholarship and federal legislation on credit card fraud and applies corrective mechanisms only after the damage is done. Instead, this article focuses on preempting credit card fraud by tackling the root causes of the problem: the built-in incentives that keep the credit card industry from fighting fraud on a system-wide basis. This article examines how credit card companies and banks have created a self-interested infrastructure that insulates them from the liabilities and costs of credit card fraud. Contrary to widespread belief, retailers, not card companies or banks, absorb much of the loss caused by thieves who shop with stolen credit cards. Also, credit card companies and banks earn fees from every credit card transaction, including those that are fraudulent. In addressing these problems, this article advocates broad reforms, including legislation that would mandate data security standards for the industry, empower multiple stakeholders to create the new standards, and offer companies incentives to comply by capping bank fees for those that are compliant, while deregulating fees for those that are not compliant

Ensemble of Example-Dependent Cost-Sensitive Decision Trees

Several real-world classification problems are example-dependent cost-sensitive in nature, where the costs due to misclassification vary between examples and not only within classes. However, standard classification methods do not take these costs into account, and assume a constant cost of misclassification errors. In previous works, some methods that take into account the financial costs into the training of different algorithms have been proposed, with the example-dependent cost-sensitive decision tree algorithm being the one that gives the highest savings. In this paper we propose a new framework of ensembles of example-dependent cost-sensitive decision-trees. The framework consists in creating different example-dependent cost-sensitive decision trees on random subsamples of the training set, and then combining them using three different combination approaches. Moreover, we propose two new cost-sensitive combination approaches; cost-sensitive weighted voting and cost-sensitive stacking, the latter being based on the cost-sensitive logistic regression method. Finally, using five different databases, from four real-world applications: credit card fraud detection, churn modeling, credit scoring and direct marketing, we evaluate the proposed method against state-of-the-art example-dependent cost-sensitive techniques, namely, cost-proportionate sampling, Bayes minimum risk and cost-sensitive decision trees. The results show that the proposed algorithms have better results for all databases, in the sense of higher savings.Comment: 13 pages, 6 figures, Submitted for possible publicatio

Intelligent Financial Fraud Detection Practices: An Investigation

Financial fraud is an issue with far reaching consequences in the finance industry, government, corporate sectors, and for ordinary consumers. Increasing dependence on new technologies such as cloud and mobile computing in recent years has compounded the problem. Traditional methods of detection involve extensive use of auditing, where a trained individual manually observes reports or transactions in an attempt to discover fraudulent behaviour. This method is not only time consuming, expensive and inaccurate, but in the age of big data it is also impractical. Not surprisingly, financial institutions have turned to automated processes using statistical and computational methods. This paper presents a comprehensive investigation on financial fraud detection practices using such data mining methods, with a particular focus on computational intelligence-based techniques. Classification of the practices based on key aspects such as detection algorithm used, fraud type investigated, and success rate have been covered. Issues and challenges associated with the current practices and potential future direction of research have also been identified.Comment: Proceedings of the 10th International Conference on Security and Privacy in Communication Networks (SecureComm 2014

Semi-Annual Report to Congress for the Period of April 1, 2006 to September 30, 2006

[Excerpt] I am pleased to submit this Semiannual Report to the Congress, which highlights the significant activities and accomplishments of the Office of Inspector General (OIG) for the six-month period ending September 30, 2006. During this reporting period, our investigative work led to 295 indictments, 260 convictions, and over $76 million in monetary accomplishments. In addition, we issued 66 audit reports and questioned$90.2 million in costs. During this reporting period, the OIG continued to provide audit and investigative oversight of the Department of Labor’s (DOL’s) response to Hurricanes Katrina and Rita. We issued six management letters related to this effort. One of the letters identified individuals who had received disaster unemployment assistance (DUA) from one state, while also receiving DUA or state unemployment compensation from another state. In addition, an OIG investigation led to the indictment of a disaster-reconstruction company owner who had allegedly neglected to pay approximately $1.4 million in employee taxes owed to the Federal and state governments. OIG audits included significant recommendations to address vulnerabilities identified in DOL programs and operations. For example, we issued a performance audit that determined that DOL’s coal mine hazardous condition complaint process needed improvement. We also conducted several audits assessing the adequacy of the Department’s information security program and identified challenges in the areas of access controls and protection over personally identifiable information. Our investigations continue to combat labor racketeering in the workplace and fraud involving DOL programs. One particular investigation resulted in several former high-ranking officials of Laborers’ International Union of North America Local 91 in the State of New York either pleading guilty or being sentenced for conspiring to commit violations of the Hobbs Act. Another significant case involved Ralphs Grocery Company. Ralphs pled guilty to several felony counts related to charges that it illegally rehired locked-out workers during the supermarket labor dispute in Southern California more than two years ago. In June 2006, the company agreed to pay$70 million in fines and restitution. Finally, recognizing the need to collaboratively combat document and benefit fraud, the OIG joined with the Departments of Homeland Security, Justice, State, and other agencies to form task forces in 10 major cities. Led by the U.S. Immigration and Customs Enforcement, the task forces have been highly effective in targeting criminal organizations and ineligible beneficiaries engaged in this type of fraud. In one case, an investigation found that the owner of a labor leasing company used counterfeit labor certification forms to apply for at least 250 green cards. The owner of the company pled guilty to charges and faces 37 to 46 months’ incarceration. The OIG remains committed to promoting the economy, integrity, effectiveness, and efficiency of DOL programs and detecting waste, fraud, and abuse against those programs. I would like to express my sincere appreciation to a professional and dedicated OIG staff for their significant achievements during this reporting period

Is it Time to Address Selective Disclosure for Nonprofit Organizations?

Over the past two decades, there have been several highly publicized nonprofit scandals that have eroded the publics confidence in the sector (Aviv 2004). Significant changes in nonprofit regulation have been implemented to address these concerns that have expanded the financial information available to the public. Interestingly, the calls for more nonprofit accountability have not focused on an important concern, that of selective disclosure. This is a practice under which an organization provides material information to some constituents while withholding it from others. This paper argues that practice is frequently observed in the nonprofit sector. As the New Era Philanthropy scandal highlighted, this practice can pose substantial risks to the nonprofit sector by facilitating fraud and harming the publics trust. The paper describes the existing nonprofit reporting requirements and potential shortcomings. It examines two alternative disclosure environments, the Freedom of Information Act (FOIA) in the federal government and corporate securities regulation, particularly Regulation Fair Disclosure, and their limitations. It will then discuss what measures could be taken to address selective disclosure in the nonprofit sector.This publication is Hauser Center Working Paper No. 33.7. Hauser Working Paper Series Nos. 33.1-33.9 were prepared as background papers for the Nonprofit Governance and Accountability Symposium October 3-4, 2006

Semi-Annual Report to Congress for the Period of April 1, 2011 to September 30, 2011

[Excerpt] I am pleased to submit this Semiannual Report to Congress, which highlights the most significant activities and accomplishments of the U.S. Department of Labor (DOL), Office of Inspector General (OIG) for the six-month period ending September 30, 2011. During this reporting period, our investigative work led to 226 indictments, 172 convictions, and $50.9 million in monetary accomplishments. In addition, we issued 40 audit and other reports which, among other things, recommended that$677.1 million in funds be put to better use. OIG audits and investigations continue to assess the effectiveness, efficiency, economy, and integrity of DOL’s programs and operations. We also continue to investigate the influence of labor racketeering and/or organized crime with respect to internal union affairs, employee benefit plans, and labor-management relations. In the employment and training area, an OIG audit of Recovery Act funds spent on green jobs found that with 61 percent of the training grant periods having elapsed, grantees have achieved just 10 percent of their job placement goals. We recommended that the Employment and Training Administration (ETA) evaluate the program and obtain estimates of the need for the remaining $327 million of unspent grant funds. Another OIG audit found that ETA needs to better ensure that the Job Corps’ outreach and admissions service providers enroll only eligible students. If ETA’s recent and planned changes to the Job Corps’ student enrollment process are effectively implemented, then we estimate that nearly$165 million in funds could be put to better use by ensuring only eligible students are enrolled. Another audit estimated that up to $124 million in Workforce Investment Act funding was spent on training participants who did not obtain training-related employment, or information was insufficient to make the determination that training-related employment was obtained. An OIG investigation found a pattern of misconduct involving the Veterans’ Employment and Training Services (VETS) Assistant Secretary and two other senior VETS officials, which reflected a consistent disregard of Federal procurement rules and regulations. The Assistant Secretary and his Chief of Staff resigned following the issuance of our report. Our investigations continued to identify vulnerabilities and fraud in DOL programs. For example, an investigation resulted in two business owners being sentenced to more than three years in prison and ordered to forfeit$2.8 million as a result of their roles in an H-1B visa fraud conspiracy. Another investigation resulted in the owner of a medical practice group being sentenced to serve more than a year in prison and ordered to pay more than $2.5 million in restitution for fraudulent billings that were submitted to DOL’s Office of Workers’ Compensation Programs, Medicaid, Medicare, and private insurance companies. OIG investigations also continue to combat labor racketeering in the workplace. For example, one major investigation resulted in the sentencing of the former secretary-treasurer of the District Council of Carpenters to 11 years in prison and restitution of$5.7 million for receiving prohibited payments from contractors to allow the underpayment of contributions to the union-sponsored benefit plans, resulting in financial harm to union members. Another OIG investigation led to a former Plumbers Union worker being sentenced to three and one-half years in prison, among other things, after pleading guilty to charges of theft from an employee benefit plan and embezzlement of approximately $412,000 in union dues. The OIG remains committed to promoting the integrity, effectiveness, and efficiency of DOL. I would like to express my gratitude to the professional and dedicated OIG staff for their significant achievements during this reporting period. I look forward to continuing to work with the Department to ensure the integrity of programs and that the rights and benefits of worker and retirees are protected

Online reputation management: estimating the impact of management responses on consumer reviews

We investigate the relationship between a firm’s use of management responses and its online reputation. We focus on the hotel industry and present several findings. First, hotels are likely to start responding following a negative shock to their ratings. Second, hotels respond to positive, negative, and neutral reviews at roughly the same rate. Third, by exploiting variation in the rate with which hotels respond on different review platforms and variation in the likelihood with which consumers are exposed to management responses, we find a 0.12-star increase in ratings and a 12% increase in review volume for responding hotels. Interestingly, when hotels start responding, they receive fewer but longer negative reviews. To explain this finding, we argue that unsatisfied consumers become less likely to leave short indefensible reviews when hotels are likely to scrutinize them. Our results highlight an interesting trade-off for managers considering responding: fewer negative ratings at the cost of longer and more detailed negative feedback.Accepted manuscrip