I call BS: Fraud Detection in Crowdfunding Campaigns

Donations to charity-based crowdfunding environments have been on the rise in the last few years. Unsurprisingly, deception and fraud in such platforms have also increased, but have not been thoroughly studied to understand what characteristics can expose such behavior and allow its automatic detection and blocking. Indeed, crowdfunding platforms are the only ones typically performing oversight for the campaigns launched in each service. However, they are not properly incentivized to combat fraud among users and the campaigns they launch: on the one hand, a platform's revenue is directly proportional to the number of transactions performed (since the platform charges a fixed amount per donation); on the other hand, if a platform is transparent with respect to how much fraud it has, it may discourage potential donors from participating. In this paper, we take the first step in studying fraud in crowdfunding campaigns. We analyze data collected from different crowdfunding platforms, and annotate 700 campaigns as fraud or not. We compute various textual and image-based features and study their distributions and how they associate with campaign fraud. Using these attributes, we build machine learning classifiers, and show that it is possible to automatically classify such fraudulent behavior with up to 90.14% accuracy and 96.01% AUC, only using features available from the campaign's description at the moment of publication (i.e., with no user or money activity), making our method applicable for real-time operation on a user browser

Misregulation Of Person To Person Lending

Amid a financial crisis and credit crunch, retail investors are lending a billion dollars over the Internet, on an unsecured basis, to total strangers. Technological and financial innovation allows person-to-person (“P2P”) lending to connect lenders and borrowers in inspiring ways never before imagined. However, all is not well with P2P lending. The SEC threatens the entire industry by asserting jurisdiction with a fundamental misunderstanding of P2P lending. This Article illustrates how the SEC has transformed this industry, making P2P lending less safe and more costly, threatening its very existence. The SEC’s misregulation of P2P lending provides an opportunity to theorize about regulation in a rapidly disintermediating world. The Article then proposes a preferable regulatory scheme designed to preserve and discipline P2P lending\u27s innovative mix of social finance, micro lending, and disintermediation. This proposal consists of regulation by the new Consumer Financial Protection Bureau

Disrupting Finance

This open access Pivot demonstrates how a variety of technologies act as innovation catalysts within the banking and financial services sector. Traditional banks and financial services are under increasing competition from global IT companies such as Google, Apple, Amazon and PayPal whilst facing pressure from investors to reduce costs, increase agility and improve customer retention. Technologies such as blockchain, cloud computing, mobile technologies, big data analytics and social media therefore have perhaps more potential in this industry and area of business than any other. This book defines a fintech ecosystem for the 21st century, providing a state-of-the art review of current literature, suggesting avenues for new research and offering perspectives from business, technology and industry

Disrupting Finance : FinTech and Strategy in the 21st Century

This open access Pivot demonstrates how a variety of technologies act as innovation catalysts within the banking and financial services sector. Traditional banks and financial services are under increasing competition from global IT companies such as Google, Apple, Amazon and PayPal whilst facing pressure from investors to reduce costs, increase agility and improve customer retention. Technologies such as blockchain, cloud computing, mobile technologies, big data analytics and social media therefore have perhaps more potential in this industry and area of business than any other. This book defines a fintech ecosystem for the 21st century, providing a state-of-the art review of current literature, suggesting avenues for new research and offering perspectives from business, technology and industry

Application of Big Data Technology, Text Classification, and Azure Machine Learning for Financial Risk Management Using Data Science Methodology

Data science plays a crucial role in enabling organizations to optimize data-driven opportunities within financial risk management. It involves identifying, assessing, and mitigating risks, ultimately safeguarding investments, reducing uncertainty, ensuring regulatory compliance, enhancing decision-making, and fostering long-term sustainability. This thesis explores three facets of Data Science projects: enhancing customer understanding, fraud prevention, and predictive analysis, with the goal of improving existing tools and enabling more informed decision-making. The first project examined leveraged big data technologies, such as Hadoop and Spark, to enhance financial risk management by accurately predicting loan defaulters and their repayment likelihood. In the second project, we investigated risk assessment and fraud prevention within the financial sector, where Natural Language Processing and machine learning techniques were applied to classify emails into categories like spam, ham, and phishing. After training various models, their performance was rigorously evaluated. In the third project, we explored the utilization of Azure machine learning to identify loan defaulters, emphasizing the comparison of different machine learning algorithms for predictive analysis. The results aimed to determine the best-performing model by evaluating various performance metrics for the dataset. This study is important because it offers a strategy for enhancing risk management, preventing fraud, and encouraging innovation in the financial industry, ultimately resulting in better financial outcomes and enhanced customer protection

Digital Asset Regulation: Peering into the Past, Peering into the Future

Blockchain is often compared to the internet as a disruptive technology that will realign economic structures across the world. This analogy extends to law and regulation. Similar to internet-based services, digital assets raise a host of challenges for policymakers. They also pose general questions regarding the desirability and practicality of regulating decentralized systems. Such debates play out against a backdrop of concerns that regulatory action will chill innovation or push market activity to more tolerant jurisdictions. The story of internet policy in the late 1990s and early 2000s therefore provides important lessons for policymakers today when confronting digital assets. Two incidents are of particular significance: the Clinton administration’s 1997 Framework for Global Electronic Commerce and the judicial effort to address peer-to-peer (P2P) file sharing. The early internet regulatory debates demonstrated that action by all three branches of government was important to resolve uncertainties and distinguish legitimate from illegitimate market activity. The history illustrates that policymakers have many tools at their disposal beyond direct prohibitions or exclusions from requirements. Claims that regulation is inherently impossible or damaging to market development are generally overblown. Focusing on policy objectives, rather than starting from traditional categories that were historically developed based on those objectives, will help policymakers develop appropriate rules for novel digital asset markets such as decentralized finance (DeFi)

Essays on trust and online peer-to-peer markets

The internet has led to the rapid emergence of new organizational forms such as the sharing economy, crowdfunding and crowdlending and those based on the blockchain. Using a variety of methods, this dissertation empirically explores trust and legitimacy in these new markets as they relate to investor decision making