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 Application of Machine Learning in Credit Risk Assessment: A Prelude to Smart Banking

2019 ◽  
Author(s):  
Syed Zamil Hasan Shoumo ◽  
Mir Ishrak Maheer Dhruba ◽  
Sazzad Hossain ◽  
Nawab Haider Ghani ◽  
Hossain Arif ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Credit Risk ◽  
Credit Risk Assessment

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 Machine Learning Algorithms

2017 ◽  
Vol 23 (4) ◽  
pp. 3649-3653 ◽  
Author(s):  
Girija V. Attigeri ◽  
M. M. Manohara Pai ◽  
Radhika M Pai
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Credit Risk ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Credit Risk Assessment

CRAM: A Credit Risk Assessment Model by Analyzing Different Machine Learning Algorithms

2021 ◽  
Author(s):  
Aquib Abtahi Turjo ◽  
Yeaminur Rahman ◽  
S.M. Mynul Karim ◽  
Tausif Hossain Biswas ◽  
Ifroim Dewan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Credit Risk ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Credit Risk Assessment
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