An insight into the experimental design for credit risk and corporate bankruptcy prediction systems

Over the last years, it has been observed an increasing interest of the finance and business communities in any application tool related to the prediction of credit and bankruptcy risk, probably due to the need of more robust decision-making systems capable of managing and analyzing complex data. As a result, plentiful techniques have been developed with the aim of producing accurate prediction models that are able to tackle these issues. However, the design of experiments to assess and compare these models has attracted little attention so far, even though it plays an important role in validating and supporting the theoretical evidence of performance. The experimental design should be done carefully for the results to hold significance; otherwise, it might be a potential source of misleading and contradictory conclusions about the benefits of using a particular prediction system. In this work, we review more than 140 papers published in refereed journals within the period 2000–2013, putting the emphasis on the bases of the experimental design in credit scoring and bankruptcy prediction applications. We provide some caveats and guidelines for the usage of databases, data splitting methods, performance evaluation metrics and hypothesis testing procedures in order to converge on a systematic, consistent validation standard.This work has partially been supported by the Mexican Science and Technology Council (CONACYT-Mexico) through a Postdoctoral Fellowship [223351], the Spanish Ministry of Economy under grant TIN2013-46522-P and the Generalitat Valenciana under grant PROMETEOII/2014/062

Apoio à definição de plafonds de crédito em cartões bancários: uma proposta metodológica com recurso à aboradgem MCDA

A definição de plafonds em cartões de crédito influencia as famílias que a ele recorrem e constitui, por norma, um desafio para as instituições bancárias. Com efeito, avaliar as capacidades financeiras do cliente e perceber se vai ser cumpridor, no sentido de definir o plafond mais adequado ao seu perfil, constitui uma tarefa difícil e de grande complexidade, pois são inúmeros e diferenciados os fatores que influenciam a definição de cada plafond. Sustentado nessa dificuldade, o presente estudo propõe o uso integrado de mapas cognitivos com a técnica Decision EXpert (DEX), no sentido de tornar o processo de apoio à definição de plafonds de crédito em cartões bancários mais completo, transparente e informado. Para o efeito, e assumindo uma lógica construtivista, o sistema de avaliação a construir recorrerá à partilha de conhecimentos e experiências dos membros de um painel de especialistas na área dos cartões de crédito. Os resultados demonstram que o uso integrado de mapas cognitivos com a técnica DEX contribui para uma compreensão mais precisa e detalhada do problema de decisão em análise, potenciando uma tomada de decisão tendencialmente mais informada. Vantagens, limitações, implicações práticas e perspetivas de futura investigação serão também alvo de discussão.The definition of credit limits in credit cards is generally considered a risky challenge for banks, and impacts families’ decisions. In practice, assessing clients’ financial capabilities, and thus anticipate whether they will assume their credit responsibilities, is a very difficult and highly complex endeavor, namely because there are numerous and differentiated variables that influence the definition of credit limits. Grounded on this difficulty, this study proposes the integrated use of cognitive maps with the Decision EXpert (DEX) technique, with the aim of making the process of definition of credit limits more complete, transparent and better informed. Assuming a constructivist stance, the evaluation system created resorts to knowledge- and experience-sharing with the members of a panel of experts in the field of credit cards. The results show that the integrated use of cognitive maps with the DEX technique contributes to a more accurate understanding of the decision problem at hand, leveraging a somewhat more informed decision making. Advantages, limitations, practical implications and perspectives for future research are also discussed

MCLP-based Methods for Improving "Bad" Catching Rate in Credit Cardholder Behavior Analysis

Cardholders’ behavior prediction is an important issue in credit card portfolio management. As a promising data mining approach, multiple criteria programming (MCLP) has been successfully applied to classify credit cardholders’ behavior into two groups. In order to better control credit risk for financial institutes, this paper proposes three methods based on MCLP to improve the ‘‘Bad’’ catching accuracy rate. One is called MCLP with unbalanced training set selection, the second is called fuzzy linear programming (FLP) method with moving boundary, and the third is called penalized multi criteria linear programming (PMCLP). The experimental examples demonstrate the promising performance of these methods