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Register a new userReintegration des refuses en Credit Scoring
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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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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.
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.
The aim of this project is to develop and test advanced analytical methods to improve the prediction accuracy of Credit Risk Models, preserving at the same time the model interpretability. In particular, the project focuses on applying an explainable machine learning model to bank-related databases. The input data were obtained from open data. Over the total proven models, CatBoost has shown the highest performance. The algorithm implementation produces a GINI of 0.68 after tuning the hyper-parameters. SHAP package is used to provide a global and local interpretation of the model predictions to formulate a human-comprehensive approach to understanding the decision-maker algorithm. The 20 most important features are selected using the Shapley values to present a full human-understandable model that reveals how the attributes of an individual are related to its model prediction.
Credit scoring is a major application of machine learning for financial institutions to decide whether to approve or reject a credit loan. For sake of reliability, it is necessary for credit scoring models to be both accurate and globally interpretable. Simple classifiers, e.g., Logistic Regression (LR), are white-box models, but not powerful enough to model complex nonlinear interactions among features. Fortunately, automatic feature crossing is a promising way to find cross features to make simple classifiers to be more accurate without heavy handcrafted feature engineering. However, credit scoring is usually based on different aspects of users, and the data usually contains hundreds of feature fields. This makes existing automatic feature crossing methods not efficient for credit scoring. In this work, we find local piece-wise interpretations in Deep Neural Networks (DNNs) of a specific feature are usually inconsistent in different samples, which is caused by feature interactions in the hidden layers. Accordingly, we can design an automatic feature crossing method to find feature interactions in DNN, and use them as cross features in LR. We give definition of the interpretation inconsistency in DNN, based on which a novel feature crossing method for credit scoring prediction called DNN2LR is proposed. Apparently, the final model, i.e., a LR model empowered with cross features, generated by DNN2LR is a white-box model. Extensive experiments have been conducted on both public and business datasets from real-world credit scoring applications. Experimental shows that, DNN2LR can outperform the DNN model, as well as several feature crossing methods. Moreover, comparing with the state-of-the-art feature crossing methods, i.e., AutoCross, DNN2LR can accelerate the speed for feature crossing by about 10 to 40 times on datasets with large numbers of feature fields.
Credit scoring models, which are among the most potent risk management tools that banks and financial institutes rely on, have been a popular subject for research in the past few decades. Accordingly, many approaches have been developed to address the challenges in classifying loan applicants and improve and facilitate decision-making. The imbalanced nature of credit scoring datasets, as well as the heterogeneous nature of features in credit scoring datasets, pose difficulties in developing and implementing effective credit scoring models, targeting the generalization power of classification models on unseen data. In this paper, we propose the Bagging Supervised Autoencoder Classifier (BSAC) that mainly leverages the superior performance of the Supervised Autoencoder, which learns low-dimensional embeddings of the input data exclusively with regards to the ultimate classification task of credit scoring, based on the principles of multi-task learning. BSAC also addresses the data imbalance problem by employing a variant of the Bagging process based on the undersampling of the majority class. The obtained results from our experiments on the benchmark and real-life credit scoring datasets illustrate the robustness and effectiveness of the Bagging Supervised Autoencoder Classifier in the classification of loan applicants that can be regarded as a positive development in credit scoring models.
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