scholarly journals Automated credit decision process - an insight into developing a credit-scoring model within the Nepalese banking sector

2012 ◽  
Vol 4 (3/4) ◽  
pp. 233 ◽  
Author(s):  
Satish Sharma ◽  
Jackie Harvey ◽  
Andrew Robson
Keyword(s):  
Decision Process ◽  
Banking Sector ◽  
Credit Scoring ◽  
Scoring Model ◽  
Credit Scoring Model ◽  
Insight Into

scholarly journals Dampak Jaminan terhadap Peluang Memperoleh Kredit bagi UMKM di Sumatera Barat

2015 ◽  
Vol 16 (1) ◽  
pp. 1-14
Author(s):  
Joan Marta ◽  
Doni Satria
Keyword(s):  
Logistic Model ◽  
Banking Sector ◽  
Small And Medium Enterprises ◽  
Credit Scoring ◽  
Bank Loan ◽  
Scoring Model ◽  
Collateral Effect ◽  
Medium Enterprises ◽  
Banking Credit ◽  
Credit Scoring Model

Collateral Effect to Bank Loan Probability for Microenterprises in West SumateraThis paper reveals that West Sumatera banking sector are more likely using the availability of collateral for the credit to micro, small and medium enterprises (MSME). Using 384 sample size from MSME in West Sumatera, if a MSME have enough collateral, the probability of their credit application to be rejected by banks will fall from 59.9% to 11.7% comparing to they don’t have enough collateral. This finding proved a credit guarantee scheme is needed, and for the further study it is recommended to conduct research on the characteristic of the potentials of MSME as a credit scoring model for banks.Keywords: Micro, Small and Medium Enterprises; Banking Credit; Logistic Model AbstrakPenelitian ini membuktikan bahwa perbankan di Sumatera Barat cenderung untuk menggunakan jaminan yang cukup sebagai dasar penolakan dan pemberian kredit yang diajukan Usaha Mikro Kecil dan Menengah (UMKM). Menggunakan data hasil survei dengan ukuran sampel sebanyak 384 UMKM di Sumatera Barat, penulis menemukan bahwa peluang sebuah aplikasi kredit yang diajukan UMKM ditolak akan berkurang dari 59,9% menjadi 11,7% jika UMKM tersebut memiliki jaminan yang cukup. Hasil penelitian ini merekomendasikan pentingnya sistem penjaminan kredit untuk UMKM dan sekaligus merekomendasikan untuk melakukan penelitian lanjutan untuk mengindentifikasi karakteristik UMKM yang memiliki potensi yang baik dan pembuatan model kredit scoring.

Building a Credit Scoring Model Based on Data Mining Approaches

2020 ◽  
Vol 30 (02) ◽  
pp. 147-169
Author(s):  
Jasmina Nalić ◽  
Goran Martinovic
Keyword(s):  
Data Mining ◽  
High Performance ◽  
Banking Sector ◽  
Credit Scoring ◽  
Model Performance ◽  
Real Data ◽  
Support Vector ◽  
Scoring Model ◽  
Adopted Model ◽  
Credit Scoring Model

Nowadays, one of the biggest challenges in banking sector, certainly, is assessment of the client’s creditworthiness. In order to improve the decision-making process and risk management, banks resort to using data mining techniques for hidden patterns recognition within a wide data. The main objective of this study is to build a high-performance customized credit scoring model. The model named Reliable client is based on Bank’s real dataset and originally built by applying four different classification algorithms: decision tree (DT), naive Bayes (NB), generalized linear model (GLM) and support vector machine (SVM). Since it showed the greatest results, but also seemed as the most appropriate algorithm, the adopted model is based on GLM algorithm. The results of this model are presented based on many performance measures that showed great predictive confidence and accuracy, but we also demonstrated significant impact of data pre-processing on model performance. Statistical analysis of the model identified the most significant parameters on the model outcome. In the end, created credit scoring model was evaluated using another set of real data of the same Bank.

scholarly journals Measuring and Managing Credit Risk for Chinese Microfinance Institutions

2018 ◽  
Vol 10 (7) ◽  
pp. 56
Author(s):  
Jie Li ◽  
Zhenyu Sheng
Keyword(s):  
Credit Risk ◽  
Predictive Power ◽  
Direct Method ◽  
Credit Scoring ◽  
Microfinance Institutions ◽  
Linear Discriminant ◽  
Management Level ◽  
Scoring Model ◽  
Subjective Appraisals ◽  
Credit Scoring Model

Chinese microfinance institutions need to measure and manage credit risk in a quantitative way in order to improve competitiveness. To establish a credit scoring model (CSM) with sound predictive power, they should examine various models carefully, identify variables, assign values to variables and reduce variable dimensions in an appropriate way. Microfinance institutions could employ both CSM and loan officer’s subjective appraisals to improve risk management level gradually. The paper sets up a CSM based on the data of a microfinance company running from October 2009 to June 2014 in Jiangsu province. As for establishing the model, the paper uses Linear Discriminant Analysis (LDA) method, selects 16 initial variables, employs direct method to assign variables and adopts all the variables into the model. Ten samples are constructed by randomly selecting records. Based on the samples, the coefficients are determined and the final none-standardized discriminant function is established. It is found that Bank credit, Education, Old client and Rate variables have the greatest impact on the discriminant effect. Compared with the same international models, this model’s classification effect is fine. The paper displays the key technical points to build a credit scoring model based on a practical application, which provides help and references for Chinese microfinance institutions to measure and manage credit risk quantitatively.

A novel multi-stage ensemble model for credit scoring based on synthetic sampling and feature transformation

2021 ◽  
pp. 1-16
Author(s):  
Fang He ◽  
Wenyu Zhang ◽  
Zhijia Yan
Keyword(s):  
Credit Scoring ◽  
Imbalanced Data ◽  
Transformation Method ◽  
Classification Performance ◽  
Ensemble Model ◽  
Feature Transformation ◽  
Learning Methods ◽  
Scoring Model ◽  
Multi Stage ◽  
Credit Scoring Model

Credit scoring has become increasingly important for financial institutions. With the advancement of artificial intelligence, machine learning methods, especially ensemble learning methods, have become increasingly popular for credit scoring. However, the problems of imbalanced data distribution and underutilized feature information have not been well addressed sufficiently. To make the credit scoring model more adaptable to imbalanced datasets, the original model-based synthetic sampling method is extended herein to balance the datasets by generating appropriate minority samples to alleviate class overlap. To enable the credit scoring model to extract inherent correlations from features, a new bagging-based feature transformation method is proposed, which transforms features using a tree-based algorithm and selects features using the chi-square statistic. Furthermore, a two-layer ensemble method that combines the advantages of dynamic ensemble selection and stacking is proposed to improve the classification performance of the proposed multi-stage ensemble model. Finally, four standardized datasets are used to evaluate the performance of the proposed ensemble model using six evaluation metrics. The experimental results confirm that the proposed ensemble model is effective in improving classification performance and is superior to other benchmark models.

scholarly journals Credit concession through credit scoring: Analysis and application proposal

2017 ◽  
Vol 13 (1) ◽  
pp. 51 ◽  
Author(s):  
Oriol Amat ◽  
Raffaele Manini ◽  
Marcos Antón Renart
Keyword(s):  
Financial Institutions ◽  
Design Methodology ◽  
Credit Scoring ◽  
Real Data ◽  
Statistical Techniques ◽  
Probability Of Default ◽  
Relative Importance ◽  
Linear Discriminant ◽  
Scoring Model ◽  
Credit Scoring Model

Purpose: The study herein develops and tests a credit scoring model which can help financial institutions in assessing credit requests. Design/methodology/approach: The empirical study has the objective of answering two questions: (1) Which ratios better discriminate the companies based on their being solvent or insolvent? and (2) What is the relative importance of these ratios? To do this, several statistical techniques with a multifactorial focus have been used (Multivariate Analysis of Variance, Linear Discriminant Analysis, Logit and Probit Models). Several samples of companies have been used in order to obtain and to test the model. Findings: Through the application of several statistical techniques, the credit scoring model has been proved to be effective in discriminating between good and bad creditors. Research limitations:  This study focuses on manufacturing, commercial and services companies of all sizes in Spain; Therefore, the conclusions may differ for other geographical locations.Practical implications:  Because credit is one of the main drivers of growth, a solid credit scoring model can help financial institutions assessing to whom to grant credit and to whom not to grant credit.Social implications: Because of the growing importance of credit for our society and the fear of granting it due to the latest financial turmoil, a solid credit scoring model can strengthen the trust toward the financial institutions assessment’s. Originality/value: There is already a stream of literature related to credit scoring. However, this paper focuses on Spanish firms and proves the results of our model based on real data. The application of the model to detect the probability of default in loans is original.

scholarly journals A multicriteria credit scoring model for SMEs using hybrid BWM and TOPSIS

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Pranith Kumar Roy ◽  
Krishnendu Shaw
Keyword(s):  
Credit Risk ◽  
Financial Institutions ◽  
Credit Scoring ◽  
Real Life ◽  
Financial Data ◽  
Loan Pricing ◽  
Scoring Model ◽  
Record Keeping ◽  
Return On Capital ◽  
Credit Scoring Model

AbstractSmall- and medium-sized enterprises (SMEs) have a crucial influence on the economic development of every nation, but access to formal finance remains a barrier. Similarly, financial institutions encounter challenges in the assessment of SMEs’ creditworthiness for the provision of financing. Financial institutions employ credit scoring models to identify potential borrowers and to determine loan pricing and collateral requirements. SMEs are perceived as unorganized in terms of financial data management compared to large corporations, making the assessment of credit risk based on inadequate financial data a cause for financial institutions’ concern. The majority of existing models are data-driven and have faced criticism for failing to meet their assumptions. To address the issue of limited financial record keeping, this study developed and validated a system to predict SMEs’ credit risk by introducing a multicriteria credit scoring model. The model was constructed using a hybrid best–worst method (BWM) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Initially, the BWM determines the weight criteria, and TOPSIS is applied to score SMEs. A real-life case study was examined to demonstrate the effectiveness of the proposed model, and a sensitivity analysis varying the weight of the criteria was performed to assess robustness against unpredictable financial situations. The findings indicated that SMEs’ credit history, cash liquidity, and repayment period are the most crucial factors in lending, followed by return on capital, financial flexibility, and integrity. The proposed credit scoring model outperformed the existing commercial model in terms of its accuracy in predicting defaults. This model could assist financial institutions, providing a simple means for identifying potential SMEs to grant credit, and advance further research using alternative approaches.

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