Serena Khan, a fictional analyst at an investment management firm, was tasked with evaluating a potential investment in Robinhood Markets, Inc. in March 2023. Known for its sleek interface and zero-commission trades, Robinhood aimed to "democratize finance" by making investment products widely accessible. The company saw a surge in users during the COVID-19 pandemic but came under fire for its role in the meme-stock craze and its revenue model based on payment for order flow. Robinhood had struggled since its 2021 IPO as the number of active users declined and shares fell over 80% from their highs. This case encourages students to explore key themes relevant to a broad range of fintech companies and provides an opportunity to discuss the role of wholesale market makers in trade execution, the economics of PFOF, and the gamification of investing.

Alliant Credit Union (Alliant) had to decide whether to partner with Upstart Holdings, Inc. (Upstart), a financial technology (fintech) company that offered a platform to connect borrowers and lenders. Upstart's underwriting models used artificial intelligence (AI)/machine learning (ML) algorithms to analyze both standard financial variables and "alternative data" on borrowers (e.g., their education history). Studies found that Upstart's approach to underwriting resulted in fewer defaults and more approvals relative to conventional models based on credit scores. However, the use of alternative data in the underwriting process raised fair-lending concerns. In 2020, a nonprofit claimed that Upstart's use of educational variables led to discriminatory outcomes. While Upstart disputed this claim, it agreed to reform aspects of its models to ensure fairness. This case requires students to evaluate tradeoffs associated with the use of new data and technology in the underwriting process. On the one hand, Upstart's underwriting models expanded access to credit, particularly benefiting borrowers with limited credit histories. On the other hand, the use of alternative data raised fairness concerns because the variables were often correlated with factors such as race, ethnicity, and age. The case also provides an opportunity to discuss fair lending laws in the United States, the economics of underwriting, and funding models used by fintech firms. At Darden, this case is used as part of a second-year MBA elective on fintech. It is part of a unit that studies applications of fintech to borrowing and lending. The case could also be used in classes on banking and financial institutions or corporate social responsibility.

This case examines a fictional hedge fund manager's decision about whether to maintain his fund's position in bitcoin. It provides an opportunity for students to develop an investment thesis for or against bitcoin, evaluate alternative ways to gain exposure to bitcoin, and consider bitcoin's economic function (e.g., as a currency or commodity). The case can be used in an MBA elective course on fintech, capital markets, or investments.