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.

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.

scholarly journals Improving Loan Loss Provisioning Framework as a Driver of Economic Growth

2020 ◽  
Vol 14 (2) ◽  
pp. 72-82
Author(s):  
Svetlana Stepanova ◽  
Viktoria Karakchieva
Keyword(s):  
Economic Growth ◽  
Risk Assessment ◽  
Credit Risk ◽  
Financial Statements ◽  
Assessment Method ◽  
Capital Adequacy ◽  
Loan Loss ◽  
Credit Risk Assessment ◽  
Loan Loss Provisions ◽  
Credit Institutions

Various aspects of credit risk have been studied by many researchers. Scientists and practitioners consider different credit risk assessment methods depending on its application, e.g. to determine capital adequacy, to make loss loan provisions, or to estimate its influence on the interest rate. At the same time, there are almost no studies that consider the relationship between loan loss provisioning framework and loan decisions. The study seeks to 1) understand how the practices and procedures of loan loss provisioning impact total gross loans of Russian banks, and 2) identify constraints for insufficient levels of lending and factors that can foster lending.With the use of an econometric model we estimate a quantitative effect of credit portfolio on the growth of loan loss provisions. We base our model on data derived from financial statements of 400 Russian credit institutions between 2014 and 2019. In addition to our empirical model, we analyze statistical data on the development of the Russian banking system and compare the loan loss provisions in Russian and foreign financial organizations. The estimates are based on Russian official statistics and financial statements of banks within and outside Russia. The study reveals that the existing credit risk assessment method that rests on the regulations provided by the Bank of Russia is responsible for excessive loan loss provisions accumulated by Russian banks. This, in turn, affects the volumes of bank loans.In our research we have arrived at the conclusion that the existing loan loss provisioning is excessive. Current loan loss provisions do not correspond to real lending losses. They negatively affect the financial results of credit institutions, resulting in ungrounded refusals to lend, which in turn limits economic growth. These results support the rationale for reinventing the existing framework of loan loss provisioning.

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.

Hybridisation of Feature Selection and Classification Techniques in Credit Risk Assessment Modelling

2020 ◽  
Author(s):  
Sapiah Sakri ◽  
Jaizah Othman ◽  
Noreha Halid
Keyword(s):  
Risk Assessment ◽  
Feature Selection ◽  
Random Forest ◽  
Credit Risk ◽  
Performance Metrics ◽  
Risk Model ◽  
Information Gain ◽  
Credit Risk Assessment ◽  
Credit Risk Model ◽  
Feature Selection Techniques

In recent years, the use of artificial intelligence techniques to manage credit risk has represented an improvement over conventional methods. Furthermore, small improvements to credit scoring systems and default forecasting can support huge profits. Accordingly, banks and financial institutions have a high interest in any changes. The literature shows that the use of feature selection techniques can reduce the dimensionality problems in most credit risk datasets, and, thus, improve the performance of the credit risk model. Many other works also indicated that various classification approaches would also affect the performance of the credit risk assessment modelling. In this research, based on the new proposed framework, we investigated the effect of various filter-based feature selection techniques with various classification approaches, namely, single and ensemble classifiers, on three credit datasets (German, Australian, and Japanese credit risk datasets) with the aim of improving the performance of the credit risk model. All single and ensemble classifier-based models were evaluated using four of the most used performance metrics for assessing financial stress models. From the comparison analysis between, with, and without applying the feature selection and across the three credit datasets, the Random-Forest + Information-Gain model achieved a better trade-off in improving the model’s accuracy rate with the value of 96% for the Australian credit dataset. This model also obtained the lowest Type I error with the value of 4% for the German credit dataset, the lowest Type II error with the value of 2% for the German credit dataset and the highest value of G-mean of 95% for the Australian credit dataset. The results clearly indicate that the Random-Forest + Information-Gain model is an excellent predictor for the credit risk cases.

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