Predicting customer churn requires considering multiple reasons for customer leave, including financial difficulties, dissatisfaction with service quality, and switching to competitors. Survival analysis methods allow us to estimate the probability of an event occurring over time, but have limited functionality in the case of competing risks. The paper proposes adapting classical survival analysis methods to competing risks by constructing a set of individual models using the One-vs-Rest scheme, with different approaches for incorporating censored events. The proposed method for aggregating forecasts using a multi-class classification meta-model will simplify the process of making expert decisions. According to the results of an experimental study using Freddie Mac mortgage lending data, the proposed methods surpassed existing solutions in quality and can be applied to mortgage lending problems.
Vasilev et al. (Fri,) studied this question.