1,511 research outputs found
Credit Scoring Using Machine Learning
For financial institutions and the economy at large, the role of credit scoring in lending decisions cannot be overemphasised. An accurate and well-performing credit scorecard allows lenders to control their risk exposure through the selective allocation of credit based on the statistical analysis of historical customer data. This thesis identifies and investigates a number of specific challenges that occur during the development of credit scorecards. Four main contributions are made in this thesis. First, we examine the performance of a number supervised classification techniques on a collection of imbalanced credit scoring datasets. Class imbalance occurs when there are significantly fewer examples in one or more classes in a dataset compared to the remaining classes. We demonstrate that oversampling the minority class leads to no overall improvement to the best performing classifiers. We find that, in contrast, adjusting the threshold on classifier output yields, in many cases, an improvement in classification performance. Our second contribution investigates a particularly severe form of class imbalance, which, in credit scoring, is referred to as the low-default portfolio problem. To address this issue, we compare the performance of a number of semi-supervised classification algorithms with that of logistic regression. Based on the detailed comparison of classifier performance, we conclude that both approaches merit consideration when dealing with low-default portfolios. Third, we quantify the differences in classifier performance arising from various implementations of a real-world behavioural scoring dataset. Due to commercial sensitivities surrounding the use of behavioural scoring data, very few empirical studies which directly address this topic are published. This thesis describes the quantitative comparison of a range of dataset parameters impacting classification performance, including: (i) varying durations of historical customer behaviour for model training; (ii) different lengths of time from which a borrower’s class label is defined; and (iii) using alternative approaches to define a customer’s default status in behavioural scoring. Finally, this thesis demonstrates how artificial data may be used to overcome the difficulties associated with obtaining and using real-world data. The limitations of artificial data, in terms of its usefulness in evaluating classification performance, are also highlighted. In this work, we are interested in generating artificial data, for credit scoring, in the absence of any available real-world data
Credit scoring: comparison of non‐parametric techniques against logistic regression
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceOver the past decades, financial institutions have been giving increased importance to credit risk
management as a critical tool to control their profitability. More than ever, it became crucial for
these institutions to be able to well discriminate between good and bad clients for only
accepting the credit applications that are not likely to default. To calculate the probability of
default of a particular client, most financial institutions have credit scoring models based on
parametric techniques. Logistic regression is the current industry standard technique in credit
scoring models, and it is one of the techniques under study in this dissertation. Although it is
regarded as a robust and intuitive technique, it is still not free from several critics towards the
model assumptions it takes that can compromise its predictions. This dissertation intends to
evaluate the gains in performance resulting from using more modern non-parametric
techniques instead of logistic regression, performing a model comparison over four different
real-life credit datasets. Specifically, the techniques compared against logistic regression in this
study consist of two single classifiers (decision tree and SVM with RBF kernel) and two ensemble
methods (random forest and stacking with cross-validation). The literature review demonstrates
that heterogeneous ensemble approaches have a weaker presence in credit scoring studies and,
because of that, stacking with cross-validation was considered in this study. The results
demonstrate that logistic regression outperforms the decision tree classifier, has similar
performance in relation to SVM and slightly underperforms both ensemble approaches in similar
extents
Improved credit scoring model using XGBoost with Bayesian hyper-parameter optimization
Several credit-scoring models have been developed using ensemble classifiers in order to improve the accuracy of assessment. However, among the ensemble models, little consideration has been focused on the hyper-parameters tuning of base learners, although these are crucial to constructing ensemble models. This study proposes an improved credit scoring model based on the extreme gradient boosting (XGB) classifier using Bayesian hyper-parameters optimization (XGB-BO). The model comprises two steps. Firstly, data pre-processing is utilized to handle missing values and scale the data. Secondly, Bayesian hyper-parameter optimization is applied to tune the hyper-parameters of the XGB classifier and used to train the model. The model is evaluated on four widely public datasets, i.e., the German, Australia, lending club, and Polish datasets. Several state-of-the-art classification algorithms are implemented for predictive comparison with the proposed method. The results of the proposed model showed promising results, with an improvement in accuracy of 4.10%, 3.03%, and 2.76% on the German, lending club, and Australian datasets, respectively. The proposed model outperformed commonly used techniques, e.g., decision tree, support vector machine, neural network, logistic regression, random forest, and bagging, according to the evaluation results. The experimental results confirmed that the XGB-BO model is suitable for assessing the creditworthiness of applicants
Feature selection in credit risk modeling: an international evidence
This paper aims to discover a suitable combination of contemporary feature selection techniques and robust prediction classifiers.
As such, to examine the impact of the feature selection method
on classifier performance, we use two Chinese and three other
real-world credit scoring datasets. The utilized feature selection
methods are the least absolute shrinkage and selection operator
(LASSO), multivariate adaptive regression splines (MARS). In contrast, the examined classifiers are the classification and regression
trees (CART), logistic regression (LR), artificial neural network
(ANN), and support vector machines (SVM). Empirical findings
confirm that LASSO’s feature selection method, followed by
robust classifier SVM, demonstrates remarkable improvement and
outperforms other competitive classifiers. Moreover, ANN also
offers improved accuracy with feature selection methods; LR only
can improve classification efficiency through performing feature
selection via LASSO. Nonetheless, CART does not provide any
indication of improvement in any combination. The proposed
credit scoring modeling strategy may use to develop policy, progressive ideas, operational guidelines for effective credit risk management of lending, and other financial institutions. The finding
of this study has practical value, as to date, there is no consensus
about the combination of feature selection method and prediction classifiers
Credit Risk Management Using Automatic Machine Learning
The article presents the basic techniques of data mining implemented in typical commercial software. They were used to assess the risk of credit card debt repayment. The article assesses the quality of classification models derived from data mining techniques and compares their results with the traditional approach using a logit model to assess credit risk. It turns out that data mining models provide similar accuracy of classification compared to the logit model, but they require much less work and facilitate the automation of the process of building scoring models
A Descriptive Study of Variable Discretization and Cost-Sensitive Logistic Regression on Imbalanced Credit Data
Training classification models on imbalanced data tends to result in bias towards the majority class. In this paper, we demonstrate how variable discretization and cost-sensitive logistic regression help mitigate this bias on an imbalanced credit scoring dataset, and further show the application of the variable discretization technique on the data from other domains, demonstrating its potential as a generic technique for classifying imbalanced data beyond credit scoring. The performance measurements include ROC curves, Area under ROC Curve (AUC), Type I Error, Type II Error, accuracy, and F1 score. The results show that proper variable discretization and cost-sensitive logistic regression with the best class weights can reduce the model bias and/or variance. From the perspective of the algorithm, cost-sensitive logistic regression is beneficial for increasing the value of predictors even if they are not in their optimized forms while maintaining monotonicity. From the perspective of predictors, the variable discretization performs better than cost-sensitive logistic regression, provides more reasonable coefficient estimates for predictors which have nonlinear relationships against their empirical logit, and is robust to penalty weights on misclassifications of events and non-events determined by their apriori proportions
Improving Risk Predictions by Preprocessing Imbalanced Credit Data
Imbalanced credit data sets refer to databases in which the class of defaulters is heavily under-represented in comparison to the class of non-defaulters. This is a very common situation in real-life credit scoring applications, but it has still received little attention. This paper investigates whether data resampling can be used to improve the performance of learners built from imbalanced credit data sets, and whether the effectiveness of resampling is related to the type of classifier. Experimental results demonstrate that learning with the resampled sets consistently outperforms the use of the original imbalanced credit data, independently of the classifier used
Credit risk prediction in an imbalanced social lending environment
© 2018, the Authors. Credit risk prediction is an effective way of evaluating whether a potential borrower will repay a loan, particularly in peer-to-peer lending where class imbalance problems are prevalent. However, few credit risk prediction models for social lending consider imbalanced data and, further, the best resampling technique to use with imbalanced data is still controversial. In an attempt to address these problems, this paper presents an empirical comparison of various combinations of classifiers and resampling techniques within a novel risk assessment methodology that incorporates imbalanced data. The credit predictions from each combination are evaluated with a G-mean measure to avoid bias towards the majority class, which has not been considered in similar studies. The results reveal that combining random forest and random under-sampling may be an effective strategy for calculating the credit risk associated with loan applicants in social lending markets
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