scholarly journals The Vicious Circle of Non-performing Assets: An introspection for Indian banks to ensure their profitability amidst COVID – 19 pandemic

2020 ◽  
pp. 179-185
Keyword(s):  
Risk Assessment ◽  
Credit Risk ◽  
Financial Institutions ◽  
Service Providers ◽  
Credit Scoring ◽  
Developed Countries ◽  
Basic Function ◽  
Credit Risk Assessment ◽  
Indian Banks ◽  
Risk Assessment Mechanism

The non performing assets (NPAs) or bad loans, as we understand generally, have always been one of the key challenges for Indian banks and financial institutions and they have been adversely affecting the sustainability of these financial service providers. While performing the basic function of extending credit in order to earn interest income, however, it is also important for these institutions to have an efficient and effective credit risk assessment mechanism in place, so that, a proper balance between profitability and sustainability is maintained. Credit scoring models are one of the most important components of credit risk assessment mechanism and banks and financial institutions of many developed countries have developed credit scoring models based on advanced technologies. On the contrary, most of the Indian banks are still dependent on the traditional way of developing credit scoring models, which might be a deterrent against ensuring safe credit policy amidst the COVID – 19 pandemic.

Credit risk assessment using purchase order information

2018 ◽  
Vol 05 (04) ◽  
pp. 1850041
Author(s):  
Suguru Yamanaka
Keyword(s):  
Risk Assessment ◽  
Credit Risk ◽  
Financial Institutions ◽  
High Frequency ◽  
Risk Model ◽  
Financial Statements ◽  
Order Information ◽  
Purchase Order ◽  
Credit Risk Assessment ◽  
Business Conditions

This paper proposes advanced credit risk assessment and lending operations using purchase order information from borrower firms. Purchase order information from a borrower firm is useful for financial institutions to evaluate the actual business conditions of the firm. This paper shows the application of purchase order information to lending operations and credit risk assessment, and reveals its effectiveness. First, we propose a “purchase order based” credit risk model for real-time credit risk monitoring of firms. Financial institutions can monitor the actual business conditions of borrower firms by evaluating the firm’s asset value using purchase order information. A combination of traditional firm monitoring using financial statements and high-frequency monitoring using purchase order information enables financial institutions to assess the business conditions of borrower firms more precisely and efficiently. Then, with high-frequency data, financial institutions can give borrower firms appropriate support if necessary on a timely basis. Second, we illustrate purchase order financing, which is the lending method backed by purchase order information from borrowers. With purchase order financing, firms that consistently receive purchase orders from credit-worthy firms can borrow money under more favorable lending terms than the usual lending terms based on the financial statements of the borrower firm.

Identification of a standard AI based technique for credit risk analysis

2016 ◽  
Vol 23 (5) ◽  
pp. 1381-1390 ◽  
Author(s):  
M. Punniyamoorthy ◽  
P. Sridevi
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Credit Risk ◽  
Financial Institutions ◽  
Support Vector ◽  
Data Set ◽  
Content Type ◽  
Machine Learning Classification ◽  
Credit Risk Assessment ◽  
Data Points

Purpose – Credit risk assessment has gained importance in recent years due to global financial crisis and credit crunch. Financial institutions therefore seek the support of credit rating agencies to predict the ability of creditors to meet financial persuasions. The purpose of this paper is to construct neural network (NN) and fuzzy support vector machine (FSVM) classifiers to discriminate good creditors from bad ones and identify a best classifier for credit risk assessment. Design/methodology/approach – This study uses artificial neural network, the most popular AI technique used in the field of financial applications for classification and prediction and the new machine learning classification algorithm, FSVM to differentiate good creditors from bad. As membership value on data points influence the classification problem, this paper presents the new FSVM model. The instances membership is computed using fuzzy c-means by evolving a new membership. The FSVM model is also tested on different kernels and compared and the classifier with highest classification accuracy for a kernel is identified. Findings – The paper identifies a standard AI model by comparing the performances of the NN model and FSVM model for a credit risk data set. This work proves that that FSVM model performs better than back propagation-neural network. Practical implications – The proposed model can be used by financial institutions to accurately assess the credit risk pattern of customers and make better decisions. Originality/value – This paper has developed a new membership for data points and has proposed a new FCM-based FSVM model for more accurate predictions.

scholarly journals Financial Inclusion in Emerging Economies: The Application of Machine Learning and Artificial Intelligence in Credit Risk Assessment

2021 ◽  
Vol 9 (3) ◽  
pp. 39
Author(s):  
David Mhlanga
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Risk Assessment ◽  
Credit Risk ◽  
Financial Institutions ◽  
Strong Impact ◽  
Credit Risk Assessment ◽  
Public Data ◽  
Credit Risk Analysis ◽  
The Impact

In banking and finance, credit risk is among the important topics because the process of issuing a loan requires a lot of attention to assessing the possibilities of getting the loaned money back. At the same time in emerging markets, the underbanked individuals cannot access traditional forms of collateral or identification that is required by financial institutions for them to be granted loans. Using the literature review approach through documentary and conceptual analysis to investigate the impact of machine learning and artificial intelligence in credit risk assessment, this study discovered that artificial intelligence and machine learning have a strong impact on credit risk assessments using alternative data sources such as public data to deal with the problems of information asymmetry, adverse selection, and moral hazard. This allows lenders to do serious credit risk analysis, to assess the behaviour of the customer, and subsequently to verify the ability of the clients to repay the loans, permitting less privileged people to access credit. Therefore, this study recommends that financial institutions such as banks and credit lending institutions invest more in artificial intelligence and machine learning to ensure that financially excluded households can obtain credit.

scholarly journals Credit Risk Assessment and Racial Minority Lending at the Farm Service Agency

2006 ◽  
Vol 38 (1) ◽  
pp. 61-75 ◽  
Author(s):  
Cesar L. Escalante ◽  
Rodney L. Brooks ◽  
James E. Epperson ◽  
Forrest E. Stegelin
Keyword(s):  
Risk Assessment ◽  
Credit Risk ◽  
Credit Scoring ◽  
Convincing Evidence ◽  
Class Action ◽  
Service Agency ◽  
Credit Risk Assessment ◽  
Black Farmers ◽  
Farm Service Agency ◽  
Loan Approval

The nature of credit risk assessment and basis of loan approval decisions of the Farm Service Agency are analyzed in the aftermath of the black farmers' 1997 class action suit against the U.S. Department of Agriculture. This study did not uncover convincing evidence of racial discrimination against nonwhite borrowers under a binomial logistic framework based on the probability of a loan application's approval. Moreover, the collective use of more stringent and objective credit-scoring measures usually employed by commercial lenders is less evident in the Farm Service Agency's evaluation of loan applications.

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