Using data mining to improve assessment of credit worthiness via credit scoring models

Wah, Yap Bee; Ong, S. H.; Husain, Nor Huselina Mohamed

doi:10.1016/j.eswa.2011.04.147

Cited by 147 publications

(95 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…As a result, the overall prediction accuracy ratio slightly decreases from 88.5% for the two-stage hybrid model II to 87.4%. According to Bekhet and Eletter [44], Yap et al [52], Kürüm et al [53] and West [54], we consider that the improvement of the "negative signal" prediction accuracy ratio is more important than that of the "positive signal" prediction accuracy ratio; therefore, the two-stage hybrid model III exhibits a better credit risk prediction capability than model II. The optimal cutoff point is determined by taking the point of intersection of the sensitivity and specificity curves.…”

Section: Experimental Results Of Two-stagementioning

confidence: 99%

Predicting China’s SME Credit Risk in Supply Chain Financing by Logistic Regression, Artificial Neural Network and Hybrid Models

et al. 2016

View full text Add to dashboard Cite

Based on logistic regression (LR) and artificial neural network (ANN) methods, we construct an LR model, an ANN model and three types of a two-stage hybrid model. The two-stage hybrid model is integrated by the LR and ANN approaches. We predict the credit risk of China's small and medium-sized enterprises (SMEs) for financial institutions (FIs) in the supply chain financing (SCF) by applying the above models. In the empirical analysis, the quarterly financial and non-financial data of 77 listed SMEs and 11 listed core enterprises (CEs) in the period of 2012-2013 are chosen as the samples. The empirical results show that: (i) the "negative signal" prediction accuracy ratio of the ANN model is better than that of LR model; (ii) the two-stage hybrid model type I has a better performance of predicting "positive signals" than that of the ANN model; (iii) the two-stage hybrid model type II has a stronger ability both in aspects of predicting "positive signals" and "negative signals" than that of the two-stage hybrid model type I; and (iv) "negative signal" predictive power of the two-stage hybrid model type III is stronger than that of the two-stage hybrid model type II. In summary, the two-stage hybrid model III has the best classification capability to forecast SMEs credit risk in SCF, which can be a useful prediction tool for China's FIs.

show abstract

Section: Experimental Results Of Two-stagementioning

confidence: 99%

Predicting China’s SME Credit Risk in Supply Chain Financing by Logistic Regression, Artificial Neural Network and Hybrid Models

et al. 2016

View full text Add to dashboard Cite

show abstract

“…Number of clones produced for each B cell is another parameter which is called clone Number. The age of generated B cells is calculated using (5 …”

Section: Proposedmentioning

confidence: 99%

“…In these situations banks can supervise the existing loans much easier than before [3]. Because of the fast growth of autofinancing in the last two decades, the use of data mining for credit risk prediction increases rapidly [4][5][6][7]. The first investigation into credit scoring was started by Olson and Wu in 2010 to classify credit applications as good or bad payers [8].…”

Section: Introductionmentioning

confidence: 99%

Credit Risk Prediction Using Fuzzy Immune Learning

Kamalloo

Abadeh

2014

Advances in Fuzzy Systems

View full text Add to dashboard Cite

The use of credit has grown considerably in recent years. Banks and financial institutions confront credit risks to conduct their business. Good management of these risks is a key factor to increase profitability. Therefore, every bank needs to predict the credit risks of its customers. Credit risk prediction has been widely studied in the field of data mining as a classification problem. This paper proposes a new classifier using immune principles and fuzzy rules to predict quality factors of individuals in banks. The proposed model is combined with fuzzy pattern classification to extract accurate fuzzy if-then rules. In our proposed model, we have used immune memory to remember good B cells during the cloning process. We have designed two forms of memory: simple memory and k-layer memory. Two real world credit data sets in UCI machine learning repository are selected as experimental data to show the accuracy of the proposed classifier. We compare the performance of our immune-based learning system with results obtained by several well-known classifiers. Results indicate that the proposed immune-based classification system is accurate in detecting credit risks.

show abstract

“…They have been conducted that have compared ANN with other traditional classification algorithms in the field of credit prediction models, since the prediction accuracies of ANN are better than linear discriminant analysis (LDA) and logistic regression (LR). Yap et al [2] illustrated using data mining algorithms to improve assessment of credit worthiness using credit rating prediction models. They compared a data mining based credit scorecard model, LR, and decision trees (DT) and did not detect a significant difference in classification error rates.…”

Section: Introductionmentioning

confidence: 99%

A Personal Credit Rating Prediction Model Using Data Mining in Smart Ubiquitous Environments

Bae

Kim

2015

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

This study suggests a methodology called a smart ubiquitous data mining (UDM) that consolidates homogeneous models in a smart ubiquitous computing environment. It tests the suggested model with financial datasets. It basically induces rules from the dataset using diverse rule extraction algorithms and combines the rules to build a metamodel. This paper builds several personal credit rating prediction models based on the UDM and benchmarks their performance against other models which employ logistic regression (LR), Bayesian style frequency matrix (BFM), multilayer perceptron (MLP), classification tree methods (C5.0), and neural network rule extraction (NR) algorithms. To verify the feasibility and effectiveness of UDM, personal credit data and personal loan data provided by a Financial Holding Company (FHC) were used in this study. Empirical results indicated that UDM outperforms other models such as LR, BFM, MLP, C5.0, and NR.

show abstract

Using data mining to improve assessment of credit worthiness via credit scoring models

Cited by 147 publications

References 19 publications

Predicting China’s SME Credit Risk in Supply Chain Financing by Logistic Regression, Artificial Neural Network and Hybrid Models

Predicting China’s SME Credit Risk in Supply Chain Financing by Logistic Regression, Artificial Neural Network and Hybrid Models

Credit Risk Prediction Using Fuzzy Immune Learning

A Personal Credit Rating Prediction Model Using Data Mining in Smart Ubiquitous Environments

Contact Info

Product

Resources

About