We develop a model to predict consumer default based on deep learning. We show that the model consistently outperforms standard credit scoring models, even though it uses the same data. Our model provides favorable credit risk assessment to young borrowers relative to standard credit scoring...

We conduct a lottery experiment to assess the predictive importance of simple choice process metrics (SCPMs) in forecasting risky 50/50 gambling decisions using different types of machine learning algorithms as well as traditional choice modeling approaches. The SCPMs are recorded during a fixed...

Modeling advances create credit scores that predict default better overall, but raise concerns about their effect on protected groups. Focusing on low- and moderate-income (LMI) areas, we use an approach from the Fairness in Machine Learning literature — fairness constraints via group-specific...

We develop a model to predict consumer default based on deep learning. We show that the model consistently outperforms standard credit scoring models, even though it uses the same data. Our model is interpretable and is able to provide a score to a larger class of borrowers relative to standard...

We develop a model to predict consumer default based on deep learning. We show that the model consistently outperforms standard credit scoring models, even though it uses the same data. Our model is interpretable and is able to provide a score to a larger class of borrowers relative to standard...

Disruptive innovations caused by FinTech (i.e., technology-assisted customized fnancial services) have brought digital peer-to-peer (P2P) payments to the fore. In this chal‑ lenging environment and based on theories about customer behavior in response to technological innovations, this paper...

Credit risk evaluation has a relevant role to financial institutions, since lending may result in real and immediate losses. In particular, default prediction is one of the most challenging activities for managing credit risk. This study analyzes the adequacy of borrower's classification models...

We examine the profitability of personalized pricing policies that are derived using different specifications of demand in a typical retail setting with consumer-level panel data. We generate pricing policies from a variety of models, including Bayesian hierarchical choice models, regularized...

Using an incentivized randomized experiment, we estimate the causal effect of a Chapter 11 bankruptcy filing on consumer demand for the bankrupt firm's products. Knowledge of Hertz's Chapter 11 bankruptcy reduces consumers' willingness-to-pay for Hertz by 35%. We show evidence that consumers...