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Register a new userSimplifying credit scoring rules using LVQ+PSO
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Added by
Aurelio Fernandez Bariviera
Publication date
2017
fields
Financial
and research's language is
English
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No Arabic abstract
One of the key elements in the banking industry rely on the appropriate selection of customers. In order to manage credit risk, banks dedicate special efforts in order to classify customers according to their risk. The usual decision making process consists in gathering personal and financial information about the borrower. Processing this information can be time consuming, and presents some difficulties due to the heterogeneous structure of data. We offer in this paper an alternative method that is able to classify customers profiles from numerical and nominal attributes. The key feature of our method, called LVQ+PSO, is the finding of a reduced set of classifying rules. This is possible, due to the combination of a competitive neural network with an optimization technique. These rules constitute a predictive model for credit risk approval. The reduced quantity of rules makes this method not only useful for credit officers aiming to make quick decisions about granting a credit, but also could act as borrowers self selection. Our method was applied to an actual database of a credit consumer financial institution in Ecuador. We obtain very satisfactory results. Future research lines are exposed.
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In this work we build a stack of machine learning models aimed at composing a state-of-the-art credit rating and default prediction system, obtaining excellent out-of-sample performances. Our approach is an excursion through the most recent ML / AI concepts, starting from natural language processes (NLP) applied to economic sectors (textual) descriptions using embedding and autoencoders (AE), going through the classification of defaultable firms on the base of a wide range of economic features using gradient boosting machines (GBM) and calibrating their probabilities paying due attention to the treatment of unbalanced samples. Finally we assign credit ratings through genetic algorithms (differential evolution, DE). Model interpretability is achieved by implementing recent techniques such as SHAP and LIME, which explain predictions locally in features space.
The 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 cut-off 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 scores 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 Credit Agricole Consumer Finance. ----- Un syst`eme doctroi de credit peut refuser des demandes de pr^et jugees trop risquees. Au sein de ce syst`eme, le score de credit fournit une valeur mesurant un risque de defaut, valeur qui est comparee `a un seuil dacceptabilite. Ce score est construit exclusivement sur des donnees de clients finances, contenant en particulier linformation `bon ou mauvais payeur, alors quil est par la suite applique `a lensemble des demandes. Un tel score est-il statistiquement pertinent ? Dans cette note, nous precisons et formalisons cette question et etudions leffet de labsence des non-finances sur les scores elabores. Nous presentons ensuite des methodes pour reintegrer les non-finances et concluons sur leur inefficacite en pratique, `a partir de donnees issues de Credit Agricole Consumer Finance.
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