Bankruptcy Prediction of Small and Medium Enterprises Using a Flexible Binary Generalized Extreme Value Model

We introduce a binary regression accounting-based model for bankruptcy prediction of small and medium enterprises (SMEs). The main advantage of the model lies in its predictive performance in identifying defaulted SMEs. Another advantage, which is especially relevant for banks, is that the relationship between the accounting characteristics of SMEs and response is not assumed a priori (e.g., linear, quadratic or cubic) and can be determined from the data. The proposed approach uses the quantile function of the generalized extreme value distribution as link function as well as smooth functions of accounting characteristics to flexibly model covariate effects. Therefore, the usual assumptions in scoring models of symmetric link function and linear or pre-specied covariate-response relationships are relaxed. Out-of-sample and out-of-time validation on Italian data shows that our proposal outperforms the commonly used (logistic) scoring model for different default horizons

Deep Generative Models for Reject Inference in Credit Scoring

Credit scoring models based on accepted applications may be biased and their consequences can have a statistical and economic impact. Reject inference is the process of attempting to infer the creditworthiness status of the rejected applications. In this research, we use deep generative models to develop two new semi-supervised Bayesian models for reject inference in credit scoring, in which we model the data generating process to be dependent on a Gaussian mixture. The goal is to improve the classification accuracy in credit scoring models by adding reject applications. Our proposed models infer the unknown creditworthiness of the rejected applications by exact enumeration of the two possible outcomes of the loan (default or non-default). The efficient stochastic gradient optimization technique used in deep generative models makes our models suitable for large data sets. Finally, the experiments in this research show that our proposed models perform better than classical and alternative machine learning models for reject inference in credit scoring

Réintégration des refusés en Credit Scoring

International audienceThe granting process of all credit institutions rejects applicants who seem risky regarding the repayment of their debt. A credit score is calculated and associated with a cutoff value beneath which an applicant is rejected. Developing a new score implies having a learning dataset in which the response variable good/bad borrower is known, so that rejects are de facto excluded from the learning process. We first introduce the context and some useful notations. Then we formalize if this particular sampling has consequences on the score's relevance. Finally, we elaborate on methods that use not-financed clients' characteristics and conclude that none of these methods are satisfactory in practice using data from Crédit Agricole Consumer Finance.Un système d'octroi de crédit peut refuser des demandes de prêt jugées trop risquées. Au sein de ce système, le score de crédit fournit une valeur mesurant un risque de défaut, valeur qui est comparéè a un seuil d'acceptabilité. Ce score est construit exclusivement sur des données de clients financés, contenant en particulier l'information " bon ou mauvais payeur " , alors qu'il est par la suite appliquéappliqué`appliquéà l'ensemble des deman-des. Un tel score est-il statistiquement pertinent ? Dans cette note, nous précisons et formalisons cette question etétudionsetétudions l'effet de l'absence des non-financés sur les scoresélaborés scoresélaborés. Nous présentons ensuite des méthodes pour réintégrer les non-financés et con-cluons sur leur inefficacité en pratique, ` a partir de données issues de Crédit Agricole Consumer Financ

SUPPORT OF MANAGERIAL DECISION MAKING BY TRANSDUCTIVE LEARNING

Transductive inference has been introduced as a novelparadigm towards building predictive classi¯cation modelsfrom empirical data. Such models are routinely employedto support decision making in, e.g., marketing, risk manage-ment and manufacturing. To that end, the characteristics ofthe new philosophy are reviewed and their implications fortypical decision problems are examined. The paper\u27s objec-tive is to explore the potential of transductive learning forcorporate planning. The analysis reveals two main factorsthat govern the applicability of transduction in business set-tings, decision scope and urgency. In a similar fashion, twomajor drivers for its e®ectiveness are identi¯ed and empir-ical experiments are undertaken to con¯rm their in°uence.The results evidence that transductive classi¯ers are wellsuperior to their inductive counterparts if their speci¯c ap-plication requirements are ful¯lled