From Cash to Electronic Payments: Microeconomic, Macroeconomic, and Policy Aspects of Retail Payment Systems

This dissertation explores three aspects of the economics and policy issues surrounding retail payments (low-value frequent payments): the microeconomic aspect, by measuring costs associated with retail payment instruments; the macroeconomic aspect, by quantifying the impact of the use of electronic rather than paper-based payment instruments on consumption and GDP; and the policy aspect, by identifying barriers that keep countries stuck with outdated payment systems, and recommending policy interventions to move forward with payments modernization. Payment system modernization has become a prominent part of the financial sector reform agenda in many advanced and developing countries. Greater use of electronic payments rather than cash and other paper-based instruments would have important economic and social benefits, including lower costs and thereby increased economic efficiency and higher incomes, while broadening access to the financial system, notably for people with moderate and low incomes. The dissertation starts with a general introduction on retail payments. Chapter 1 develops a theoretical model for measuring payments costs, and applies the model to Guyana—an emerging market in the midst of the transition from paper to electronic payments. Using primary survey data from Guyanese consumers, the results of the analysis indicate that annual costs related to the use of cash by consumers reach 2.5 percent of the country’s GDP. Switching to electronic payment instruments would provide savings amounting to 1 percent of GDP per year. Chapter 2 broadens the analysis to calculate the macroeconomic impacts of a move to electronic payments. Using a unique panel dataset of 76 countries across the 17-year span from 1998 to 2014 and a pooled OLS country fixed effects model, Chapter 2 finds that on average, use of debit and credit cards contribute USD 16.2 billion to annual global consumption, and USD 160 billion to overall annual global GDP. Chapter 3 provides an in-depth assessment of the Albanian payment cards and remittances market and recommends a set of incentives and regulations (both carrots and sticks) that would allow the country to modernize its payment system. Finally, the conclusion summarizes the lessons of the dissertation’s research and brings forward issues to be explored by future research in the retail payments area.

Active Data Collection Techniques to Understand Online Scammers and Cybercriminals

Nigerian scam, also known as advance fee fraud or 419 scam, is a prevalent form of online fraudulent activity that causes financial loss to individuals and businesses. Nigerian scam has evolved from simple non-targeted email messages to more sophisticated scams targeted at users of classifieds, dating and other websites. Even though such scams are observed and reported by users frequently, the community’s understanding of Nigerian scams is limited since the scammers operate “underground”. To better understand the underground Nigerian scam ecosystem and seek effective methods to deter Nigerian scam and cybercrime in general, we conduct a series of active and passive measurement studies. Relying upon the analysis and insight gained from the measurement studies, we make four contributions: (1) we analyze the taxonomy of Nigerian scam and derive long-term trends in scams; (2) we provide an insight on Nigerian scam and cybercrime ecosystems and their underground operation; (3) we propose a payment intervention as a potential deterrent to cybercrime operation in general and evaluate its effectiveness; and (4) we offer active and passive measurement tools and techniques that enable in-depth analysis of cybercrime ecosystems and deterrence on them. We first created and analyze a repository of more than two hundred thousand user-reported scam emails, stretching from 2006 to 2014, from four major scam reporting websites. We select ten most commonly observed scam categories and tag 2,000 scam emails randomly selected from our repository. Based upon the manually tagged dataset, we train a machine learning classifier and cluster all scam emails in the repository. From the clustering result, we find a strong and sustained upward trend for targeted scams and downward trend for non-targeted scams. We then focus on two types of targeted scams: sales scams and rental scams targeted users on Craigslist. We built an automated scam data collection system and gathered large-scale sales scam emails. Using the system we posted honeypot ads on Craigslist and conversed automatically with the scammers. Through the email conversation, the system obtained additional confirmation of likely scam activities and collected additional information such as IP addresses and shipping addresses. Our analysis revealed that around 10 groups were responsible for nearly half of the over 13,000 total scam attempts we received. These groups used IP addresses and shipping addresses in both Nigeria and the U.S. We also crawled rental ads on Craigslist, identified rental scam ads amongst the large number of benign ads and conversed with the potential scammers. Through in-depth analysis of the rental scams, we found seven major scam campaigns employing various operations and monetization methods. We also found that unlike sales scammers, most rental scammers were in the U.S. The large-scale scam data and in-depth analysis provide useful insights on how to design effective deterrence techniques against cybercrime in general. We study underground DDoS-for-hire services, also known as booters, and measure the effectiveness of undermining a payment system of DDoS Services. Our analysis shows that the payment intervention can have the desired effect of limiting cybercriminals’ ability and increasing the risk of accepting payments.