Credit Risk Analysis Using Machine and Deep Learning Models

Addo, Peter Martey; Guégan, Dominique; Hassani, Bertrand K.

doi:10.3390/risks6020038

Cited by 175 publications

(102 citation statements)

References 29 publications

(27 reference statements)

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“…The obtained analytical information is used to simulate risk analysis processes based on machine learning algorithms. [13] Curators of credit institutions use a number of criteria by which one can assess the presence of the threat of bankruptcy. A significant number of factors taken into account when choosing a criterion and evaluating its significance makes it difficult to obtain a guaranteed objective solution.…”

Section: Suggestions and Recommendationsmentioning

confidence: 99%

Information technology as a tool for improving banking supervision

Khitrova¹,

Хитрова²

2019

Proceedings of the 1st International Scientific Conference "Modern Management Trends and the Digital Economy: From Regional Dev

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The problem of ensuring of the credit institutions' stability requires solving a number of tasks. Analysis of the problem allowed to determine the direction of development of the banking supervision system. Restructuring of the information support system based on business intelligence technologies, such as OLAP, Data Mining, Warehouse will provide a process of assessing the state of a credit institution with representative information. The specifics of the conditions for risk assessment and the formation of a regulatory impact based on the analysis of the parameters of a credit institution's activity determined the need for organizational changes. It should be separated the risk assessment service from the service of curators of credit institutions. The uncertainty of conditions in making a managerial decision by the curator requires the use of special tools for the curator to assess the presence of the threat of bankruptcy. The decision support system uses modeling and forecasting technologies. The diagnostics of a credit organization is considered as a task of classification. Pattern recognition technologies can be used as an effective tool for solving it.

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Section: Suggestions and Recommendationsmentioning

confidence: 99%

Information technology as a tool for improving banking supervision

Khitrova¹,

Хитрова²

2019

Proceedings of the 1st International Scientific Conference "Modern Management Trends and the Digital Economy: From Regional Dev

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“…When it comes to the methodological framework to predict defaults and credit risk, the study by Halteh et al (2018) developed cutting-edge tree-based stochastic models to model credit risk. Addo et al (2018) built binary classifiers based on machine and deep learning models on real data to predict loan default probability. The performance of classification was tested on separate data.…”

Section: Data and Modeling Approachmentioning

confidence: 99%

The Interaction of Borrower and Loan Characteristics in Predicting Risks of Subprime Automobile Loans

Ghulam

Dhruva

Naseem

et al. 2018

Risks

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We utilize the data of a very large UK automobile loan firm to study the interaction of the characteristics of borrowers and loans in predicting the subsequent loan performance. Our broader findings confirm the earlier research on the issue of subprime auto loans. More importantly, unmarried borrowers living with furnished tenancy agreements who have relatively new jobs have a probability of defaulting of more than 60% compared to an average 7% default rate in overall subprime borrowers in the dataset. Also, in the above category are those who live in a less prosperous part of the UK such as the north-west, are full-time self-employed, have other large loan arrears, fall into the bottom 25% percentile of monthly income, secure loans with high loan to total value (LTV), purchase expensive automobiles with shorter loan duration payment plans, and have a high dependency on government support. This in fact is also true of those who go into arrears, except that the highest probability in this context is around 40% compared to 6% for an overall sample. These findings shall help in the understanding of subprime auto loans performance in relation to borrowers and loan features alongside helping auto finance firms improve predictive models and decision-making.

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“…Newly developed data mining techniques and advancements in computational intelligence capabilities have been applied to build intelligent information systems for modeling complex, dynamic, and multivariate nonlinear systems (Nayak et al, 2018;Adhikari & Agrawal, 2014). In particular, soft computing methodologies have been applied successfully to areas such as data classification (Alatas, 2011;Alatas, 2012;Nayak et al, 2015), financial forecasting (Nayak et al, 2018), credit scoring (Addo et al, 2018;Tomczak & Zięba, 2015;Chow, 2018), portfolio management (Xu et al, 2011a), business failure prediction, and risk level evaluation (Daubie & Meskens, 2002;Chandra et al, 2009), and they have been found to produce significantly improved results. ANNs have proven to be an effective modeling procedure in stock market forecasting when the input-output mapping contains both regularities and exceptions (Nayak et al, 2018;Zhang, 2003;Adhikari & Agrawal, 2014;Gu et al, 2018;Board, 2017).…”

Section: Introductionmentioning

confidence: 99%

A chemical-reaction-optimization-based neuro-fuzzy hybrid network for stock closing price prediction

Nayak

Misra²

2019

Financ Innov

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Accurate prediction of stock market behavior is a challenging issue for financial forecasting. Artificial neural networks, such as multilayer perceptron have been established as better approximation and classification models for this domain. This study proposes a chemical reaction optimization (CRO) based neuro-fuzzy network model for prediction of stock indices. The input vectors to the model are fuzzified by applying a Gaussian membership function, and each input is associated with a degree of membership to different classes. A multilayer perceptron with one hidden layer is used as the base model and CRO is used to the optimal weights and biases of this model. CRO was chosen because it requires fewer control parameters and has a faster convergence rate. Five statistical parameters are used to evaluate the performance of the model, and the model is validated by forecasting the daily closing indices for five major stock markets. The performance of the proposed model is compared with four state-of-art models that are trained similarly and was found to be superior. We conducted the Deibold-Mariano test to check the statistical significance of the proposed model, and it was found to be significant. This model can be used as a promising tool for financial forecasting.

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Credit Risk Analysis Using Machine and Deep Learning Models

Cited by 175 publications

References 29 publications

Information technology as a tool for improving banking supervision

Information technology as a tool for improving banking supervision

The Interaction of Borrower and Loan Characteristics in Predicting Risks of Subprime Automobile Loans

A chemical-reaction-optimization-based neuro-fuzzy hybrid network for stock closing price prediction

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