The impact of credit scoring on consumer lending

Feature selection is crucial to the credit-scoring process, allowing for the removal of irrelevant variables with low predictive power. Conventional credit-scoring techniques treat this as a separate process wherein features are selected based on improving a single statistical measure, such as accuracy; however, recent research has focused on meaningful business parameters such as profit. More than one factor may be important to the selection process, making multi-objective optimization methods a necessity. However, the comparative performance of multi-objective methods has been known to vary depending on the test problem and specific implementation. This research employed a recent hybrid non-dominated sorting binary Grasshopper Optimization Algorithm and compared its performance on multi-objective feature selection for credit scoring to that of two popular benchmark algorithms in this space. Further comparison is made to determine the impact of changing the profit-maximizing base classifiers on algorithm performance. Experiments demonstrate that, of the base classifiers used, the neural network classifier improved the profit-based measure and minimized the mean number of features in the population the most. Additionally, the NSBGOA algorithm gave relatively smaller hypervolumes and increased computational time across all base classifiers, while giving the highest mean objective values for the solutions. It is clear that the base classifier has a significant impact on the results of multi-objective optimization. Therefore, careful consideration should be made of the base classifier to use in the scenarios.

Download Full-text

On the impact of disproportional samples in credit scoring models: An application to a Brazilian bank data

Expert Systems with Applications ◽

10.1016/j.eswa.2012.01.134 ◽

2012 ◽

Vol 39 (9) ◽

pp. 8071-8078 ◽

Cited By ~ 22

Author(s):

Francisco Louzada ◽

Paulo H. Ferreira-Silva ◽

Carlos A.R. Diniz

Keyword(s):

Credit Scoring ◽

The Impact

Download Full-text

Comparison Between Dichotomous and Polytomous Scoring of Innovative Items in a Large-Scale Computerized Adaptive Test

Educational and Psychological Measurement ◽

10.1177/0013164411422903 ◽

2011 ◽

Vol 72 (3) ◽

pp. 493-509 ◽

Cited By ~ 2

Author(s):

Hong Jiao ◽

Junhui Liu ◽

Kathleen Haynie ◽

Ada Woo ◽

Jerry Gorham

Keyword(s):

Large Scale ◽

Credit Scoring ◽

Real Data ◽

Simulation Studies ◽

Partial Credit ◽

Ability Estimates ◽

Operational Test ◽

Dichotomous Scoring ◽

Scoring Algorithms ◽

The Impact

This study explored the impact of partial credit scoring of one type of innovative items (multiple-response items) in a computerized adaptive version of a large-scale licensure pretest and operational test settings. The impacts of partial credit scoring on the estimation of the ability parameters and classification decisions in operational test settings were explored in one real data analysis and two simulation studies when two different polytomous scoring algorithms, automated polytomous scoring and rater-generated polytomous scoring, were applied. For the real data analyses, the ability estimates from dichotomous and polytomous scoring were highly correlated; the classification consistency between different scoring algorithms was nearly perfect. Information distribution changed slightly in the operational item bank. In the two simulation studies comparing each polytomous scoring with dichotomous scoring, the ability estimates resulting from polytomous scoring had slightly higher measurement precision than those resulting from dichotomous scoring. The practical impact related to classification decision was minor because of the extremely small number of items that could be scored polytomously in this current study.

Download Full-text

How Secure Are Good Loans: Validating Loan-Granting Decisions And Predicting Default Rates On Consumer Loans

Review of Business Information Systems (RBIS) ◽

10.19030/rbis.v6i3.4563 ◽

2002 ◽

Vol 6 (3) ◽

pp. 65-84 ◽

Cited By ~ 3

Author(s):

Jozef Zurada ◽

Martin Zurada

Keyword(s):

Data Mining ◽

Credit Risk ◽

Banking Industry ◽

Financial Institution ◽

Credit Scoring ◽

Risk Groups ◽

Consumer Loans ◽

Consumer Lending ◽

Default Rates ◽

Scoring Accuracy

The failure or success of the banking industry depends largely on the industrys ability to properly evaluate credit risk. In the consumer-lending context, the banks goal is to maximize income by issuing as many good loans to consumers as possible while avoiding losses associated with bad loans. Mistakes could severely affect profits because the losses associated with one bad loan may undermine the income earned on many good loans. Therefore banks carefully evaluate the financial status of each customer as well as their credit worthiness and weigh them against the banks internal loan-granting policies. Recognizing that even a small improvement in credit scoring accuracy translates into significant future savings, the banking industry and the scientific community have been employing various machine learning and traditional statistical techniques to improve credit risk prediction accuracy.This paper examines historical data from consumer loans issued by a financial institution to individuals that the financial institution deemed to be qualified customers. The data consists of the financial attributes of each customer and includes a mixture of loans that the customers paid off and defaulted upon. The paper uses three different data mining techniques (decision trees, neural networks, logit regression) and the ensemble model, which combines the three techniques, to predict whether a particular customer defaulted or paid off his/her loan. The paper then compares the effectiveness of each technique and analyzes the risk of default inherent in each loan and group of loans. The data mining classification techniques and analysis can enable banks to more precisely classify consumers into various credit risk groups. Knowing what risk group a consumer falls into would allow a bank to fine tune its lending policies by recognizing high risk groups of consumers to whom loans should not be issued, and identifying safer loans that should be issued, on terms commensurate with the risk of default.

Download Full-text

The impact of pre-selected variance inflation factor thresholds on the stability and predictive power of logistic regression models in credit scoring

ORiON ◽

10.5784/31-1-162 ◽

2015 ◽

Vol 31 (1) ◽

pp. 17 ◽

Cited By ~ 6

Author(s):

PJ De Jongh ◽

E De Jongh ◽

M Pienaar ◽

H Gordon-Grant ◽

M Oberholzer ◽

...

Keyword(s):

Logistic Regression ◽

Regression Models ◽

Predictive Power ◽

Credit Scoring ◽

Variance Inflation Factor ◽

Logistic Regression Models ◽

Inflation Factor ◽

The Stability ◽

The Impact

Download Full-text

Understanding the impact of borrowers' behavioural and psychological traits on credit default: review and conceptual model

Review of Behavioral Finance ◽

10.1108/rbf-03-2021-0051 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Akanksha Goel ◽

Shailesh Rastogi

Keyword(s):

Conceptual Model ◽

Credit Scoring ◽

Psychological Traits ◽

Content Type ◽

Scoring Model ◽

Psychological Constructs ◽

Credit Default ◽

Credit History ◽

The Impact ◽

Credit Scoring Model

PurposeThe purpose of the study is to identify certain behavioural and psychological traits of the borrowers which have the tendency to predict the credit risk of the borrowers. And the second objective is to draw a conceptual model that reveals the impact of those traits on credit default.Design/methodology/approachThe study has adopted a systematic Literature Review approach to identify those behavioural and psychological traits of borrowers that reflect on the tendency to predict the credit default of borrowers.FindingsThe findings of this study have revealed that there are some non-financial factors, which can be looked into while granting a loan to a borrower. The identified factors can be used to develop a subjective credit scoring model that can quantify and verify the soft information (character and reliability) of debtors. Further, a behavioural credit scoring model will help in easing the assessment of those borrowers, who do not have an appropriate credit history and reliable financial statements.Practical implicationsThe proposed model would help banks and financial institutions to evaluate those borrowers who lack substantial financial information. Further, a subjective credit scoring model would help to evaluate the credit worthiness of such borrowers who do not have any credit history. The model would also reduce the biasness of subjective scoring and would reduce the financial constraints of borrowers.Originality/valueBy reviewing the literature, it has been observed that there are very few studies that have exclusively considered the behavioural and psychological factors in credit scoring. Several studies have linked the psychological constructs with debts, but very few researchers have considered it while constructing a behavioural scoring model. Thus, it can be inferred that this area of behavioural finance is still unexplored and needs attention of researchers worldwide. In addition, most of the studies are carried out in European, African and American regions but are almost non-existent in the Asian markets.

Download Full-text