Credit lender–borrower relationship in the credit card market – Implications for credit risk management strategy and relationship marketing

2014 ◽  
Vol 23 (6) ◽  
pp. 1086-1095 ◽  
Author(s):  
Joon-Hee Oh ◽  
Wesley J. Johnston
Keyword(s):  
Risk Management ◽  
Credit Risk ◽  
Relationship Marketing ◽  
Credit Card ◽  
Management Strategy ◽  
Credit Risk Management ◽  
Risk Management Strategy ◽  
Credit Card Market

Estimation Procedures of Using Five Alternative Machine Learning Methods for Predicting Credit Card Default

2019 ◽  
Vol 22 (03) ◽  
pp. 1950021 ◽  
Author(s):  
Huei-Wen Teng ◽  
Michael Lee
Keyword(s):  
Machine Learning ◽  
Risk Management ◽  
Credit Risk ◽  
Portfolio Management ◽  
Credit Card ◽  
Nearest Neighbors ◽  
Support Vector ◽  
Credit Risk Management ◽  
Learning Methods ◽  
Machine Learning Methods

Machine learning has successful applications in credit risk management, portfolio management, automatic trading, and fraud detection, to name a few, in the domain of finance technology. Reformulating and solving these topics adequately and accurately is problem specific and challenging along with the availability of complex and voluminous data. In credit risk management, one major problem is to predict the default of credit card holders using real dataset. We review five machine learning methods: the [Formula: see text]-nearest neighbors decision trees, boosting, support vector machine, and neural networks, and apply them to the above problem. In addition, we give explicit Python scripts to conduct analysis using a dataset of 29,999 instances with 23 features collected from a major bank in Taiwan, downloadable in the UC Irvine Machine Learning Repository. We show that the decision tree performs best among others in terms of validation curves.

Lending Relationship in the Traditional Credit Market — Implications for Credit Risk Management Strategy in Micro Credit Institutions

2021 ◽  
pp. 2150012
Author(s):  
Embun Suryani ◽  
Hermanto ◽  
Siti Aisyah Hidayati ◽  
I Nyoman Nugraha Ardana Putra ◽  
Donny Oktavian Syah
Keyword(s):  
Risk Management ◽  
Management Strategy ◽  
Critical Role ◽  
Credit Rationing ◽  
Microfinance Institutions ◽  
Credit Risk Management ◽  
Risk Management Strategy ◽  
Credit Access ◽  
Credit Term

Asymmetric information increases the credit rationing of micro-enterprises. Lender–borrower relationships help to provide this information, thereby increasing the availability of loans. This study aims to investigate the relationship between micro-lenders and micro clients. It is accomplished by describing how such relationships are developed, and analyzing these relationships’ impact on the availability and credit term using multivariate regression. The results showed that the strength of lender–borrower relationships positively impacted credit access, but it did not significantly impact the credit term. Furthermore, the amount of income and loan purpose, as the proxies of business characteristics, negatively impacted credit access. These results highlight the critical role of the lender–borrower relationship and business characteristics in the risk management strategy and the sustainability of microfinance institutions.

Retail Credit Risk Management in Indian Public Sector Banks

2012 ◽  
Vol 3 (8) ◽  
pp. 31-37
Author(s):  
Nayan J. Nayan J. ◽  
◽  
Dr. M. Kumaraswamy Dr. M. Kumaraswamy
Keyword(s):  
Risk Management ◽  
Public Sector ◽  
Credit Risk ◽  
Credit Risk Management ◽  
Public Sector Banks ◽  
Retail Credit

scholarly journals Modeling of Bank Credit Risk Management Using the Cost Risk Model

2021 ◽  
Vol 14 (5) ◽  
pp. 211
Author(s):  
Iryna Yanenkova ◽  
Yuliia Nehoda ◽  
Svetlana Drobyazko ◽  
Andrii Zavhorodnii ◽  
Lyudmyla Berezovska
Keyword(s):  
Risk Management ◽  
Decision Support ◽  
Credit Risk ◽  
Risk Model ◽  
Bank Credit ◽  
Credit Risk Management ◽  
Loan Default ◽  
Increased Risk ◽  
Credit Portfolio ◽  
Cost Risk

This article deals with the issue of managing bank credit risk using a cost risk model. Modeling of bank credit risk management was proposed based on neural-cell technologies, which expand the possibilities of modeling complex objects and processes and provide high reliability of credit risk determination. The purpose of the article is to improve and develop methodical support and practical recommendations for reducing the level of risk based on the value-at-risk (VaR) methodology and its subsequent combination with methods of fuzzy programming and symbiotic methodical support. The model makes it possible to create decision support subsystems for nonperforming loan management based on the neuro-fuzzy approach. For this paper, economic and mathematical tools (based on the VaR methodology) were used, which made it possible to analyze and forecast the dynamics of overdue payment; assess the quality of the credit portfolio of the bank; determine possible trends in bank development. A scientific and practical approach is taken to assess and forecast the degree of credit problematicity by qualitative criteria using a mathematical model based on a fuzzy technology, which can forecast the increased risk of loan default at an early stage in the process of monitoring the loan portfolio and model forecasting changes in the degree of credit problematicity on change of indicators. A methodology is proposed for the analysis and forecasting of indicators of troubled loan debt, which should be implemented as software and included in the decision support system during the process of monitoring the risk of the bank’s credit portfolio.

An Investigation of Credit Risk Management Strategies of Private Commercial Banks of Bangladesh

2014 ◽  
Vol 2 (2) ◽  
pp. 77-87
Author(s):  
Abu Hanifa Md. Noman ◽  
Md. Amzad Hossain ◽  
Sajeda Pervin
Keyword(s):  
Risk Management ◽  
Credit Risk ◽  
Commercial Banks ◽  
Management Practices ◽  
Management Practice ◽  
Management Strategies ◽  
Third Party ◽  
Primary Data ◽  
Credit Risk Management ◽  
Risk Management Strategies

Objective - The study aims to investigate credit risk management practices and credit risk management strategies of the local private commercial banks in Bangladesh. Methodology -The investigation is conducted based on primary data collected from a set of both closed end and open end questionnaire from 23 out of 39 local private commercial banks in Bangladesh. Descriptive statistics has been used in processing the data and interpreting the results. Findings - The results reveal that credit risk management practice of the sample banks is sound which is attributed to the appropriate implementation of Basel II and credit risk management guidelines the country's central bank. The findings further show that use of Credit risk grading is most popular and effective criteria for measuring the borrowing capacity of the borrowers. In order to control credit risk and preventing losses from credit exposure banks give more focus on collateralization, accurate loan pricing and third party guarantee. Loan is monitored properly and credit reminder is given to the client if principal and interest remain outstanding for three months. The study further reveals that lack of experienced and trained credit officers, lack of genuine market information and Lack of awareness regarding non-genuine borrower are the most important problems of current credit risk management practices in Bangladesh. Novelty - To the best of the knowledge of the authors the study is the first that investigates credit risk management strategies of private commercial banks, especially on Bangladesh. Type of Paper - Empirical Keyword : Bangladesh; Commercial Bank; Credit risk; Credit risk management; Credit risk management strategies.

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