Advocates of FinTech lending argue that it enables lenders to predict loan outcomes more accurately by employing complex analytical tools, such as machine learning (ML) methods. The authors of this paper apply ML methods, specifically random forests and stochastic gradient boosting, to loan-level data from the largest FinTech lender to assess whether these methods produce predictions of default on future loans that are substantially more accurate than the predictions of standard regression models. This study also examines which input variables are influential across different models and offers an intuitive presentation of their relationship with the loan outcome according to the ML models. In addition, it investigates whether having more data—additional observations and additional input variables—helps the ML methods more than the regular regression models. This paper also explores if the use of ML methods tends to produce more accurate or more favorable ratings of borrowers who have specific combinations of observed characteristics or are from locales with better or worse economic conditions.