scholarly journals Trust, Automation Bias and Aversion: Algorithmic Decision-Making in the Context of Credit Scoring

2021 ◽  
Vol 19 (4) ◽  
pp. 542-560
Author(s):  
Rita Gsenger ◽  
Toma Strle
Keyword(s):  
Decision Making ◽  
Credit Scoring

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

2014 ◽  
Vol 7 (1) ◽  
pp. 112-125 ◽  
Author(s):  
Hussein A. Abdou ◽  
Shaair T. Alam ◽  
James Mulkeen
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Credit Scoring ◽  
Islamic Finance ◽  
Decision Making Process ◽  
Data Set ◽  
Content Type ◽  
Modeling Techniques ◽  
The Uk ◽  
Training Subset

Purpose – This paper aims 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 is used consisting of 336 accepted credit applications and 151 rejected credit applications made to an Islamic finance house in the UK. To build the proposed scoring models, the data set is divided into training and hold-out subsets. The training subset is used to build the scoring models, and the hold-out subset is used to test the predictive capabilities of the scoring models. Seventy per cent of the overall applicants will be used for the training subset, and 30 per cent will be used for the testing subset. Three statistical modeling techniques, namely, discriminant analysis, logistic regression (LR) and multilayer perceptron (MP) neural network, are used to build the proposed scoring models. Findings – The findings reveal that the LR model has the highest correct classification (CC) rate in the training subset, whereas MP outperforms other techniques and has the highest CC rate in the hold-out subset. MP also outperforms other techniques in terms of predicting the rejected credit applications and has the lowest misclassification cost 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. Originality/value – This contribution is the first to apply credit scoring modeling techniques in Islamic finance. Also in building a scoring model, the authors' 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.

CURRENT RESEARCH TREND: INFORMATION TECHNOLOGY AND DECISION MAKING IN 2008

2009 ◽  
Vol 08 (01) ◽  
pp. 1-5 ◽  
Author(s):  
YONG SHI
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Information Technology ◽  
Decision Analysis ◽  
Credit Scoring ◽  
Advisory Board ◽  
Research Trend ◽  
Web Based ◽  
Mining Methods ◽  
New Research

On behalf of the editorial advisory board of the International Journal of Information Technology and Decision Making (IT&DM), the Editor-in-Chief reviews the current research trend of this journal based on all the papers published in 2008. They are web-based decision analysis, credit scoring techniques and new data mining methods which combine both decision-making techniques and information technology tools. In addition, the Editor-in-Chief summarizes the key ideas of contributions in this new issue that may contain new research trend of IT&DM in 2009.

scholarly journals Intelligent credit scoring system using knowledge management

2019 ◽  
Vol 8 (4) ◽  
pp. 391
Author(s):  
Bazzi Mehdi ◽  
Chamlal Hasna ◽  
El Kharroubi Ahmed ◽  
Ouaderhman Tayeb
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Credit Scoring ◽  
Second Step ◽  
Decision Making System ◽  
The Government ◽  
Set Up ◽  
The Arab Spring ◽  
Credit Granting ◽  
Young Entrepreneurs

Promoting entrepreneurship in Morocco among young people has been a challenge for some years of economic and social problems, especially after the events of the Arab Spring. Several programs have been set up by the government for young entrepreneurs. Thus, faced with the large number of credit applications solicited by these young entrepreneurs, banks are obliged to resort to artificial intelligence techniques. For this purpose, the aim of this article is to propose a decision-making system enabling the bank to automate its credit granting process. It is a tool that allows the bank, in the first instance, to select promising projects through a scoring approach adapted to this segment of young entrepreneurs. In a second step, the tool allows the setting of the maximum credit amount to be allocated to the selected project. Finally, based on the knowledge of the bank's experts, the tool proposes a breakdown of the amount granted by the bank into several products adapted to the needs of the entrepreneur.

scholarly journals Can System Logs Enhance the Performance of Credit Scoring? – Evidence from an Internet Bank in Korea

2021 ◽  
Author(s):  
Sunghyun Kyeong ◽  
Daehee Kim ◽  
Jinho Shin
Keyword(s):  
Decision Making ◽  
Credit Rating ◽  
Low Cost ◽  
Credit Scoring ◽  
Banking System ◽  
Mobile App ◽  
Discrimination Power ◽  
Log Data ◽  
Internet Bank ◽  
System Logs

This study is the first to examine whether the performance of credit rating, one of the most important data-based decision-making of banks, can be improved by using banking system log data that is extensively accumulated inside the bank for system operation. This study uses the log data recorded for the mobile app system of Kakaobank, a leading internet bank used by more than 14 million people in Korea. After generating candidate variables from Kakaobank's vast log data, we develop a credit scoring model by utilizing variables with high information values. Consequently, the discrimination power of the new model compared to the credit bureau grades was significantly improved by 1.84% points based on the Kolmogorov–Smirnov statistics. Therefore, the results of this study imply that if a bank utilizes its log data that have already been extensively accumulated inside the bank, decision-making systems, including credit scoring, can be efficiently improved at a low cost.

Application of multiple discriminant analysis (MDA) as a credit scoring and risk assessment model

2011 ◽  
Vol 6 (2) ◽  
pp. 132-147 ◽  
Author(s):  
Marcellina Mvula Chijoriga
Keyword(s):  
Risk Assessment ◽  
Decision Making ◽  
Credit Risk ◽  
Credit Scoring ◽  
Assessment Model ◽  
Management Decision ◽  
Multiple Discriminant Analysis ◽  
Content Type ◽  
Management Decision Making ◽  
Customer Classification

PurposeThe purpose of this research is to investigate whether inclusion of risk assessment variables in the multiple discriminant analysis (MDA) model improved the banks ability in making correct customer classification, predict firm's performance and credit risk assessment.Design/methodology/approachThe paper reviews literature on the application of financial distress and credit scoring methods, and the use of risk assessment variables in classification models. The study used a sample of 56 performing and non‐performing assets (NPA) of a privatized commercial bank in Tanzania. Financial ratios were used as independent variables for building the MDA model with a variation of five MDA models. Different statistical tests for normality, equality of covariance, goodness of fit and multi‐colinearity were performed. Using the estimation and validation samples, test results showed that the MDA base model had a higher level of predictability hence classifying correctly the performing and NPA with a correctness of 92.9 and 96.4 percent, respectively. Lagging the classification two years, the results showed that the model could predict correctly two years in advance. When MDA was used as a risk assessment model, it showed improved correct customer classification and credit risk assessment.FindingsThe findings confirmed financial ratios as good classification and predictor variables of firm's performance. If the bank had used the MDA for classifying and evaluating its customers, the probability of failure could have been known two years before actual failure, and the misclassification costs could have been calculated objectively. In this way, the bank could have reduced its non‐performing loans and its credit risk exposure.Research limitations/implicationsThe valiadation sample used in the study was smaller compared to the estimation sample. MDA works better as a credit scoring method in the banking environment two years before and after failure. The study was done on the current financial crisis of 2009.Practical implicationsUse of MDA helps banks to determine objectively the misclassification costs and its expected misclassification errors plus determining the provisions for bad debts. Banks could have reduced the non‐performing loans and their credit risks exposure if they had used the MDA method in the loan‐evaluation and classification process. The study has proved that quantitative credit scoring models improve management decision making as compared to subjective assessment methods. For improved credit and risk assessment, a combination of both qualitative and quantitave methods should be considered.Originality/valueThe findings have shown that using the MDA, commercial banks could have improved their objective decision making by correctly classifying the credit worthiness of a customer, predicting firm's future performance as well as assessing their credit risk. It has also shown that other than financial variables, inclusion of stability measures improves management decision making and objective provisioning of bad debts. The recent financial crisis emphasizes the need for developing objective credit scoring methods and instituting prudent risk assessment culture to limit the extent and potential of failure.

scholarly journals Can System Log Data Enhance the Performance of Credit Scoring?—Evidence from an Internet Bank in Korea

2021 ◽  
Vol 14 (1) ◽  
pp. 130
Author(s):  
Sunghyon Kyeong ◽  
Daehee Kim ◽  
Jinho Shin
Keyword(s):  
Decision Making ◽  
Logistic Regression ◽  
Credit Scoring ◽  
Model Performance ◽  
Sampled Data ◽  
Discrimination Power ◽  
Log Data ◽  
Scoring Model ◽  
Internet Bank ◽  
Credit Scoring Model

The credit scoring model is one of the most important decision-making tools for the sustainability of banking systems. This study is the first to examine whether it can be improved by using system log data that are stoed extensively for system operation. We used the log data recorded by the mobile application system of KakaoBank, a leading internet bank used by more than 14 million people in Korea. After generating candidate variables from KakaoBank’s log data, we created a credit scoring model by utilizing variables with high information values and logistic regression, the most common method for developing credit scoring models in financial institutions. To prove our hypothesis on the improvement of credit scoring model performance, we performed an independent sample t-test using the simulation results of repeated model development and performance measurement based on randomly sampled data. Consequently, the discrimination power of the proposed model using logistic regression (neural network) compared to the credit bureau-based model significantly improved by 1.84 (2.22) percentage points based on the Kolmogorov–Smirnov statistics. The results of this study suggest that a bank can utilize the accumulated log data inside the bank to improve decision-making systems, including credit scoring, at a low cost.

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