Default Prediction with Machine Learning

In many cases, financial institutions collect additional information about the performance of a company or loan, such as delays in repayment or changes in ratings. Intuitively, such auxiliary data is closely related to default events, since loans with large delays in repayment are likely “closer” to defaults than loans without any repayments. Yet, traditional classification methods typically neglect this additional information and only consider binary default events.

Importantly, the Tobit loss is asymmetric for defaulted observations, such that the loss decreases for predictions even past the default threshold value. Intuitively, this leads to the desired behavior of predicting a higher default potential for default cases, instead of penalizing predictions above the threshold. Yet, the Tobit model only considers linear dependencies on the features and cannot learn complex nonlinear relations in the data.

Interestingly, if the auxiliary variable is independent of the decision function, i.e., the auxiliary data contains no additional information, the Grabit model still performs as well as the best competing binary classifier in our simulation study. Further, we observe that the Grabit model outperforms other models also in cases of larger datasets if the decision function is sufficiently complex, for example, having strong nonlinearities or interactions among predictors.

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