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.

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.

Machine learning-based classifiers ensemble for credit risk assessment

2013 ◽  
Vol 7 (3/4) ◽  
pp. 227 ◽  
Author(s):  
Trilok Nath Pandey ◽  
Alok Kumar Jagadev ◽  
D. Choudhury ◽  
Satchidananda Dehuri
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Credit Risk ◽  
Credit Risk Assessment

scholarly journals Credit Risk Assessment using Machine Learning Techniques

2019 ◽  
Vol 9 (1) ◽  
pp. 3482-3486
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Random Forest ◽  
Credit Risk ◽  
Banking Sector ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Credit Risk Assessment ◽  
Learning Techniques ◽  
Cart Algorithm

Analysis of credit scoring is an effective credit risk assessment technique, which is one of the major research fields in the banking sector. Machine learning has a variety of applications in the banking sector and it has been widely used for data analysis. Modern techniques such as machine learning have provided a self-regulating process to analyze the data using classification techniques. The classification method is a supervised learning process in which the computer learns from the input data provided and makes use of this information to classify the new dataset. This research paper presents a comparison of various machine learning techniques used to evaluate the credit risk. A credit transaction that needs to be accepted or rejected is trained and implemented on the dataset using different machine learning algorithms. The techniques are implemented on the German credit dataset taken from UCI repository which has 1000 instances and 21 attributes, depending on which the transactions are either accepted or rejected. This paper compares algorithms such as Support Vector Network, Neural Network, Logistic Regression, Naive Bayes, Random Forest, and Classification and Regression Trees (CART) algorithm and the results obtained show that Random Forest algorithm was able to predict credit risk with higher accuracy

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 Forecasting Financial Distress With Machine Learning – A Review

2020 ◽  
Vol 12 (3) ◽  
pp. 528-574
Author(s):  
Denize Lemos Duarte ◽  
Flávio Luiz de Moraes Barboza
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Credit Risk ◽  
Financial Distress ◽  
Computational Models ◽  
Web Of Science ◽  
Time Period ◽  
Credit Risk Analysis ◽  
New Variables ◽  
Computational Technology

Purpose – Evaluate the various academic researches with multiple views on credit risk and artificial intelligence (AI) and their evolution.Theoretical framework – The study is divided as follows: Section 1 introduces the article. Section 2 deals with credit risk and its relationship with computational models and techniques. Section 3 presents the methodology. Section 4 addresses a discussion of the results and challenges on the topic. Finally, section 5 presents the conclusions.Design/methodology/approach – A systematic review of the literature was carried out without defining the time period and using the Web of Science and Scopus database.Findings – The application of computational technology in the scope of credit risk analysis has drawn attention in a unique way. It was found that the demand for identification and introduction of new variables, classifiers and more assertive methods is constant. The effort to improve the interpretation of data and models is intense.Research, Practical Social implications – It contributes to the verification of the theory, providing information in relation to the most used methods and techniques, it brings a wide analysis to deepen the knowledge of the factors and variables on the theme. It categorizes the lines of research and provides a summary of the literature, which serves as a reference, in addition to suggesting future research.Originality/value – Research in the area of Artificial Intelligence and Machine Learning is recent and requires attention and investigation, thus, this study contributes to the opening of new views in order to deepen the work on this topic.

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