Probability of default prediction is one of the important tasks of rating agencies as well as of banks and other financial companies to measure the default risk of their counterparties. Knowing predictors that significantly contribute to default prediction provides a better insight into fundamentals of credit risk analysis. Default prediction and default predictor selection are two related issues, but many existing approaches address them separately. We employed a unified procedure, a regularization approach with logit as an underlying model, which simultaneously selects the default predictors and optimizes all the parameters within the model. We employ Lasso and elastic-net penalty functions as regularization approach. The methods are applied to predict default of companies from industry sector in Southeast Asian countries. The empirical result exhibits that the proposed method has a very high accuracy prediction particularly for companies operating Indonesia, Singapore, and Thailand. The relevant default predictors over the countries reveal that credit risk analysis is sample specific. A few number of predictors result in counter intuitive sign estimates.