Modeling Credit Risk of Portfolio of Consumer Loans

Author(s):  
Madhur Malik ◽  
Lyn C. Thomas
Keyword(s):  

Author(s):  
Fernando A. F. Ferreira ◽  
Ieva Meidutė-Kavaliauskienė ◽  
Edmundas K. Zavadskas ◽  
Marjan S. Jalali ◽  
Sandra M. J. Catarino

Credit to personal consumption is an important activity of the financial system and crucial to the socioeconomic development of a country. It is important, therefore, that the methods and techniques used to evaluate consumer credit risk be as efficient and informative as possible, in order to strengthen decisions to approve or reject credit and promote sustainable economic growth. This study aims to create a multiple criteria expert system which integrates cognitive maps and the measuring attractiveness by a categorical-based evaluation technique (MACBETH) to create a complementary framework for consumer credit risk assessment. The results show that this integrated approach allows the evaluation process of consumer credit risk to be more informed and transparent, providing value for the evaluation processes of this type of credit application as a result of the privileged contact established with a panel of credit analysts. Advantages, limitations, and managerial implications are also discussed.



2016 ◽  
Vol 8 (7) ◽  
pp. 93-105
Author(s):  
Anthony Wagacha ◽  
Othieno Ferdinand


2002 ◽  
Vol 6 (3) ◽  
pp. 65-84 ◽  
Author(s):  
Jozef Zurada ◽  
Martin Zurada

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.





2010 ◽  
Vol 61 (3) ◽  
pp. 411-420 ◽  
Author(s):  
M Malik ◽  
L C Thomas
Keyword(s):  


2009 ◽  
Author(s):  
Kelly D. Dages ◽  
John W. Jones ◽  
Bailey Klinger
Keyword(s):  


ICLEM 2010 ◽  
2010 ◽  
Author(s):  
Juan He ◽  
Liwei Kang ◽  
Zhonghua Ma ◽  
Ming Li


2018 ◽  
pp. 49-68 ◽  
Author(s):  
M. E. Mamonov

Our analysis documents that the existence of hidden “holes” in the capital of not yet failed banks - while creating intertemporal pressure on the actual level of capital - leads to changing of maturity of loans supplied rather than to contracting of their volume. Long-term loans decrease, whereas short-term loans rise - and, what is most remarkably, by approximately the same amounts. Standardly, the higher the maturity of loans the higher the credit risk and, thus, the more loan loss reserves (LLP) banks are forced to create, increasing the pressure on capital. Banks that already hide “holes” in the capital, but have not yet faced with license withdrawal, must possess strong incentives to shorten the maturity of supplied loans. On the one hand, it raises the turnovers of LLP and facilitates the flexibility of capital management; on the other hand, it allows increasing the speed of shifting of attracted deposits to loans to related parties in domestic or foreign jurisdictions. This enlarges the potential size of ex post revealed “hole” in the capital and, therefore, allows us to assume that not every loan might be viewed as a good for the economy: excessive short-term and insufficient long-term loans can produce the source for future losses.



2012 ◽  
Vol 3 (8) ◽  
pp. 31-37
Author(s):  
Nayan J. Nayan J. ◽  
◽  
Dr. M. Kumaraswamy Dr. M. Kumaraswamy


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