Application of Machine Learning Techniques for Credit Risk Management: A Survey

2022 ◽  
pp. 180-191
Author(s):  
Adil Oualid ◽  
Abderrahim Hansali ◽  
Youssef Balouki ◽  
Lahcen Moumoun
Keyword(s):  
Machine Learning ◽  
Risk Management ◽  
Credit Risk ◽  
Machine Learning Techniques ◽  
Credit Risk Management ◽  
Learning Techniques

Estimation Procedures of Using Five Alternative Machine Learning Methods for Predicting Credit Card Default

2019 ◽  
Vol 22 (03) ◽  
pp. 1950021 ◽  
Author(s):  
Huei-Wen Teng ◽  
Michael Lee
Keyword(s):  
Machine Learning ◽  
Risk Management ◽  
Credit Risk ◽  
Portfolio Management ◽  
Credit Card ◽  
Nearest Neighbors ◽  
Support Vector ◽  
Credit Risk Management ◽  
Learning Methods ◽  
Machine Learning Methods

Machine learning has successful applications in credit risk management, portfolio management, automatic trading, and fraud detection, to name a few, in the domain of finance technology. Reformulating and solving these topics adequately and accurately is problem specific and challenging along with the availability of complex and voluminous data. In credit risk management, one major problem is to predict the default of credit card holders using real dataset. We review five machine learning methods: the [Formula: see text]-nearest neighbors decision trees, boosting, support vector machine, and neural networks, and apply them to the above problem. In addition, we give explicit Python scripts to conduct analysis using a dataset of 29,999 instances with 23 features collected from a major bank in Taiwan, downloadable in the UC Irvine Machine Learning Repository. We show that the decision tree performs best among others in terms of validation curves.

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 Crypto Exchanges and Credit Risk: Modeling and Forecasting the Probability of Closure

2021 ◽  
Vol 14 (11) ◽  
pp. 516
Author(s):  
Dean Fantazzini ◽  
Raffaella Calabrese
Keyword(s):  
Machine Learning ◽  
Credit Risk ◽  
Credit Scoring ◽  
Machine Learning Techniques ◽  
Risk Modeling ◽  
Learning Techniques ◽  
Modeling And Forecasting ◽  
Out Of Sample ◽  
Significant Covariates ◽  
Unique Dataset

While there is increasing interest in crypto assets, the credit risk of these exchanges is still relatively unexplored. To fill this gap, we considered a unique dataset of 144 exchanges, active from the first quarter of 2018 to the first quarter of 2021. We analyzed the determinants surrounding the decision to close an exchange using credit scoring and machine learning techniques. Cybersecurity grades, having a public developer team, the age of the exchange, and the number of available traded cryptocurrencies are the main significant covariates across different model specifications. Both in-sample and out-of-sample analyzes confirm these findings. These results are robust in regard to the inclusion of additional variables, considering the country of registration of these exchanges and whether they are centralized or decentralized.

scholarly journals Machine Learning in Banking Risk Management: A Literature Review

Risks ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 29 ◽  
Author(s):  
Martin Leo ◽  
Suneel Sharma ◽  
K. Maddulety
Keyword(s):  
Machine Learning ◽  
Risk Management ◽  
Global Financial Crisis ◽  
Machine Learning Techniques ◽  
Bank Risk ◽  
Business Applications ◽  
Considerable Research ◽  
Learning Techniques ◽  
Banking Risk ◽  
The Global Financial Crisis

There is an increasing influence of machine learning in business applications, with many solutions already implemented and many more being explored. Since the global financial crisis, risk management in banks has gained more prominence, and there has been a constant focus around how risks are being detected, measured, reported and managed. Considerable research in academia and industry has focused on the developments in banking and risk management and the current and emerging challenges. This paper, through a review of the available literature seeks to analyse and evaluate machine-learning techniques that have been researched in the context of banking risk management, and to identify areas or problems in risk management that have been inadequately explored and are potential areas for further research. The review has shown that the application of machine learning in the management of banking risks such as credit risk, market risk, operational risk and liquidity risk has been explored; however, it doesn’t appear commensurate with the current industry level of focus on both risk management and machine learning. A large number of areas remain in bank risk management that could significantly benefit from the study of how machine learning can be applied to address specific problems.

scholarly journals CREDIT RISK MODELLING FOR INDIAN DEBT SECURITIES USING MACHINE LEARNING

2021 ◽  
Vol 24 ◽  
pp. 107-128
Author(s):  
Charumathi Balakrishnan ◽  
Mangaiyarkarasi Thiagarajan
Keyword(s):  
Machine Learning ◽  
Credit Risk ◽  
Credit Rating ◽  
Risk Model ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Rating Agencies ◽  
Learning Techniques ◽  
Classical Models ◽  
Credit Risk Model

We develop a new credit risk model for Indian debt securities rated by major credit rating agencies in India using the ordinal logistic regression (OLR). The robustness of the model is tested by comparing it with classical models available for ratings prediction. We improved the model’s accuracy by using machine learning techniques, such as the artificial neural networks (ANN), support vector machines (SVM) and random forest (RF). We found that the accuracy of our model has improved from 68% using OLR to 82% when using ANN and above 90% when using SVM and RF.

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