Credit-Scoring Methods (in English)

The paper reviews the best-developed and most frequently applied methods of credit scoring employed by commercial banks when evaluating loan applications. The authors concentrate on retail loans – applied research in this segment is limited, though there has been a sharp increase in the volume of loans to retail clients in recent years. Logit analysis is identified as the most frequent credit-scoring method used by banks. However, other nonparametric methods are widespread in terms of pattern recognition. The methods reviewed have potential for application in post-transition countries.banking sector, credit scoring, discrimination analysis, pattern recognition, retail loans

Would credit scoring work for Islamic finance? A neural network approach

Purpose – The main aim of this paper is to distinguish whether the decision making process of the Islamic financial houses in the UK can be improved through the use of credit scoring modeling techniques as opposed to the currently used judgmental approaches. Subsidiary aims are to identify how scoring models can reclassify accepted applicants who later are considered as having bad credit and how many of the rejected applicants are later considered as having good credit; and highlight significant variables that are crucial in terms of accepting and rejecting applicants which can further aid the decision making process. Design/methodology/approach – A real data-set of 487 applicants are used consisting of 336 accepted credit applications and 151 rejected credit applications make to an Islamic finance house in the UK. In order to build the proposed scoring models, the data-set is divided into training and hold-out sub-set. The training sub-set is used to build the scoring models and the hold-out sub-set is used to test the predictive capabilities of the scoring models.70 percent of the overall applicants will be used for the training sub-set and 30 percent will be used for the testing sub-set. Three statistical modeling techniques namely Discriminant Analysis (DA), Logistic Regression (LR) and Multi-layer Perceptron (MP) neural network are used to build the proposed scoring models. Findings – Our findings reveal that the LR model has the highest Correct Classification (CC) rate in the training sub-set whereas MP outperforms other techniques and has the highest CC rate in the hold-out sub-set. MP also outperforms other techniques in terms of predicting the rejected credit applications and has the lowest Misclassification Cost (MC) above other techniques. In addition, results from MP models show that monthly expenses, age and marital status are identified as the key factors affecting the decision making process. Research limitations/implications – Although our sample is small and restricted to an Islamic Finance house in the UK the results are robust. Future research could consider enlarging the sample in the UK and also internationally allowing for cultural differences to be identified. The results indicate that the scoring models can be of great benefit to Islamic finance houses in regards to their decision making processes of accepting and rejecting new credit applications and thus improve their efficiency and effectiveness. Originality/value –Our contribution is the first to apply credit scoring modeling techniques in Islamic Finance. Also in building a scoring model our application applies a different approach by using accepted and rejected credit applications instead of good and bad credit histories. This identifies opportunity costs of misclassifying credit applications as rejected

Building Credit-Risk Evaluation Expert Systems Using Neural Network Rule Extraction and Decision Tables.

In this paper, we evaluate and contrast four neural network rule extraction approaches for credit scoring. Experiments are carried out on three real life credit scoring data sets. Both the continuous and the discretised versions of all data sets are analysed. The rule extraction algorithms, Neurolinear, Neurorule, Trepan and Nefclass, have different characteristics with respect to their perception of the neural network and their way of representing the generated rules or knowledge. It is shown that Neurolinear, Neurorule and Trepan are able to extract very concise rule sets or trees with a high predictive accuracy when compared to classical decision tree (rule) induction algorithms like C4.5(rules). Especially Neurorule extracted easy to understand and powerful propositional ifthen rules for all discretised data sets. Hence, the Neurorule algorithm may offer a viable alternative for rule generation and knowledge discovery in the domain of credit scoring.Credit; Information systems; International; Systems;

Model Klasifikasi Kelayakan Kredit Koperasi Karyawan dengan Algoritma Decision Tree

Koperasi adalah badan USAha yang beranggotakan orang-orang atau badan hukum koperasi yang melandaskan kegiatannya berdasarkan prinsip koperasi sekaligus sebagai gerakan ekonomi rakyat yang berdasarkan asas kekeluargaan. Prosedur pemberian kredit kepada anggota akan sangat berpengaruh terhadap tumbuh kembangnya USAha yang dijalankan oleh sebuah koperasi. Klasifikasi adalah jenis analisis data yang dapat membantu orang memprediksi label kelas dari sampel yang akan diklasifikasikan. Salah satu teknik klasifikasi adalah pohon keputusan (decision tree). Pohon (tree) adalah sebuah struktur data yang terdiri dari simpul (node) dan rusuk (edge). Penelitian ini adalah penelitian eksperimen. Desain eksperimen yang digunakan adalah Cross Standard Industry Process for Data Mining (CRISP-DM). Hasil penelitian menunjukkan akurasi dari algoritma Decision Tree sebesar 92,28% untuk memodelkan kelayakan kredit sebuah koperasi karyawan

Poniendo el cerebro a trabajar: Evaluación del índice de crédito para préstamos P2P basados en el modelo de redes neuronales artificiales

Effective assessment of a borrower's various credit indexes is key for unravelling the problem of information asymmetry in the context of Peer-to-Peer Lending (P2P). Mitigating adverse selection of high default potential borrowers continues to plague P2P lending platforms. In order to understand which factors determine borrower credit status (ie. loan approval, loan repayment potential, risk of default), this study renders an Artificial Neural Network Model on one of the most popular P2P lending platforms. Our results show that the interest rate, the ratio of loan to income and the loan term are the most important indicators in reflecting the borrower’s credit status, while the frequency of inquiries, the borrowing category have a relatively low degree of importance. This study finds that the borrower’s credit index status is better explained at the lower quantiles and becomes more difficult to discern at higher quantiles. This work also finds that for longer loan terms, the borrower repayment pressure and the default rates rise with higher loan-to-income ratios and higher interest rates. Additionally, we find that higher credit rankings and higher expected returns lead to higher probabilities of defaulting. To reduce the probability of borrower default, this study recommends building lending groups or lending pools, selecting higher income credit candidates and increasing credit limits. To validate our results, we perform robustness tests that modify the learning coefficient and the training-to-validation data ratio in order to show that the empirical results of this paper are robust and effective.La evaluación efectiva de los diversos índices de crédito de un prestatario es clave para desentrañar el problema de la asimetría de la información en el contexto del préstamo entre pares (P2P). La mitigación de la selección adversa de prestatarios con alto potencial de incumplimiento continúa plagando las plataformas de préstamos P2P. Para comprender cuales son los factores que determinan el estado crediticio del prestatario (es decir, la aprobación del préstamo, el potencial de pago del préstamo, y el riesgo de incumplimiento), este estudio presenta un Modelo de Redes Neurales en una de las plataformas de préstamos P2P más populares. Nuestros resultados muestran que la tasa de interés, la relación entre el préstamo y el ingreso, y el plazo del préstamo son los indicadores más importantes para reflejar el estado crediticio del prestatario, mientras que la frecuencia de las consultas, la categoría de endeudamiento tiene un grado relativamente bajo de importancia. Este estudio encuentra que el estado del índice de crédito del prestatario se explica mejor en los cuantiles más bajos y se vuelve más difícil de discernir en cuantiles superiores. Este trabajo también concluye que para plazos de préstamo más largos, la presión de la amortización del prestatario y las tasas de incumplimiento aumentan con mayores ratios de préstamo en relación al ingreso y mayores tasas de interés. Además, encontramos que las clasificaciones de crédito más altas y los rendimientos esperados más altos conducen a mayores probabilidades de incumplimiento. Para reducir la probabilidad de impago del prestatario, este estudio recomienda construir grupos de préstamos, seleccionar candidatos de mayor ingreso y aumentar los límites de crédito. Para validar nuestros resultados, realizamos pruebas de robustez que modifican el coeficiente de aprendizaje y la relación de datos de entrenamiento a validación para mostrar que los resultados empíricos de este documento son sólidos y efectivos

Hybrid model using logit and nonparametric methods for predicting micro-entity failure

Following the calls from literature on bankruptcy, a parsimonious hybrid bankruptcy model is developed in this paper by combining parametric and non-parametric approaches.To this end, the variables with the highest predictive power to detect bankruptcy are selected using logistic regression (LR). Subsequently, alternative non-parametric methods (Multilayer Perceptron, Rough Set, and Classification-Regression Trees) are applied, in turn, to firms classified as either “bankrupt” or “not bankrupt”. Our findings show that hybrid models, particularly those combining LR and Multilayer Perceptron, offer better accuracy performance and interpretability and converge faster than each method implemented in isolation. Moreover, the authors demonstrate that the introduction of non-financial and macroeconomic variables complement financial ratios for bankruptcy prediction

Forecasting creditworthiness in retail banking: a comparison of cascade correlation neural networks, CART and logistic regression scoring models

The preoccupation with modelling credit scoring systems including their relevance to forecasting and decision making in the financial sector has been with developed countries whilst developing countries have been largely neglected. The focus of our investigation is the Cameroonian commercial banking sector with implications for fellow members of the Banque des Etats de L’Afrique Centrale (BEAC) family which apply the same system. We investigate their currently used approaches to assessing personal loans and we construct appropriate scoring models. Three statistical modelling scoring techniques are applied, namely Logistic Regression (LR), Classification and Regression Tree (CART) and Cascade Correlation Neural Network (CCNN). To compare various scoring models’ performances we use Average Correct Classification (ACC) rates, error rates, ROC curve and GINI coefficient as evaluation criteria. The results demonstrate that a reduction in terms of forecasting power from 15.69% default cases under the current system, to 3.34% based on the best scoring model, namely CART can be achieved. The predictive capabilities of all three models are rated as at least very good using GINI coefficient; and rated excellent using the ROC curve for both CART and CCNN. It should be emphasised that in terms of prediction rate, CCNN is superior to the other techniques investigated in this paper. Also, a sensitivity analysis of the variables identifies borrower’s account functioning, previous occupation, guarantees, car ownership, and loan purpose as key variables in the forecasting and decision making process which are at the heart of overall credit policy