Two-stage credit rating prediction using machine learning techniques

Kybernetes ◽  
2014 ◽  
Vol 43 (7) ◽  
pp. 1098-1113 ◽  
Author(s):  
Hsu-Che Wu ◽  
Ya-Han Hu ◽  
Yen-Hao Huang
Keyword(s):  
Credit Risk ◽  
Credit Rating ◽  
Prediction Models ◽  
Data Preprocessing ◽  
Machine Learning Techniques ◽  
Risk Level ◽  
Two Stage ◽  
Content Type ◽  
Learning Techniques ◽  
Rating Prediction

Purpose – Credit ratings have become one of the primary references for financial institutions to assess credit risk. Conventional credit rating approaches mainly concentrated on two-class classification (i.e. good or bad credit), which lacks adequate precision to perform credit risk evaluations in practice. In addition, most of previous researches directly focussed on employing various data mining techniques, but rare studies discussed the influence of data preprocessing before classifier construction. The paper aims to discuss these issues. Design/methodology/approach – This study considers nine-class classification (i.e. nine credit risk level) to credit rating prediction. For the development of more accurate classifiers, the paper adopts two-stage analysis, which integrates multiple data preprocessing and supervised learning techniques. Specifically, the first stage applies feature selection, data clustering, and data resampling methods to preprocess the data, and then the second stage utilizes several classification techniques and classifier ensembles to construct prediction models. Findings – The results show that Bagging-DT with data resampling method achieves excellent accuracy (82.96 percent), indicating that the proposed two-stage prediction model is better than conventional one-stage models. Originality/value – Practical implication of this study can lower credit rating expenses and also allow corporations to gain credit rating information instantly.

Predicting corporate credit rating based on qualitative information of MD&A transformed using document vectorization techniques

2020 ◽  
Vol 54 (2) ◽  
pp. 151-168
Author(s):  
Jinwook Choi ◽  
Yongmoo Suh ◽  
Namchul Jung
Keyword(s):  
Credit Rating ◽  
Prediction Models ◽  
Quantitative Information ◽  
Qualitative Information ◽  
Rating Data ◽  
Content Type ◽  
Financial Variables ◽  
Corporate Credit ◽  
Rating Prediction ◽  
Corporate Credit Rating

PurposeThe purpose of this study is to investigate the effectiveness of qualitative information extracted from firm’s annual report in predicting corporate credit rating. Qualitative information represented by published reports or management interview has been known as an important source in addition to quantitative information represented by financial values in assigning corporate credit rating in practice. Nevertheless, prior studies have room for further research in that they rarely employed qualitative information in developing prediction model of corporate credit rating.Design/methodology/approachThis study adopted three document vectorization methods, Bag-Of-Words (BOW), Word to Vector (Word2Vec) and Document to Vector (Doc2Vec), to transform an unstructured textual data into a numeric vector, so that Machine Learning (ML) algorithms accept it as an input. For the experiments, we used the corpus of Management’s Discussion and Analysis (MD&A) section in 10-K financial reports as well as financial variables and corporate credit rating data.FindingsExperimental results from a series of multi-class classification experiments show the predictive models trained by both financial variables and vectors extracted from MD&A data outperform the benchmark models trained only by traditional financial variables.Originality/valueThis study proposed a new approach for corporate credit rating prediction by using qualitative information extracted from MD&A documents as an input to ML-based prediction models. Also, this research adopted and compared three textual vectorization methods in the domain of corporate credit rating prediction and showed that BOW mostly outperformed Word2Vec and Doc2Vec.

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.

scholarly journals Machine learning predictivity applied to consumer creditworthiness

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Maisa Cardoso Aniceto ◽  
Flavio Barboza ◽  
Herbert Kimura
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Credit Risk ◽  
Performance Metrics ◽  
Prediction Models ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques ◽  
Default Prediction

AbstractCredit risk evaluation has a relevant role to financial institutions, since lending may result in real and immediate losses. In particular, default prediction is one of the most challenging activities for managing credit risk. This study analyzes the adequacy of borrower’s classification models using a Brazilian bank’s loan database, and exploring machine learning techniques. We develop Support Vector Machine, Decision Trees, Bagging, AdaBoost and Random Forest models, and compare their predictive accuracy with a benchmark based on a Logistic Regression model. Comparisons are analyzed based on usual classification performance metrics. Our results show that Random Forest and Adaboost perform better when compared to other models. Moreover, Support Vector Machine models show poor performance using both linear and nonlinear kernels. Our findings suggest that there are value creating opportunities for banks to improve default prediction models by exploring machine learning techniques.

A Review of Statistical and Machine Learning Techniques for Microvascular Complications in Type 2 Diabetes

2020 ◽  
Vol 16 ◽  
Author(s):  
Nitigya Sambyal ◽  
Poonam Saini ◽  
Rupali Syal
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Clinical Medicine ◽  
Microvascular Complications ◽  
Descriptive Analysis ◽  
Machine Learning Techniques ◽  
World Health ◽  
Public Health Issue ◽  
Learning Techniques ◽  
Health Organization

Background and Introduction: Diabetes mellitus is a metabolic disorder that has emerged as a serious public health issue worldwide. According to the World Health Organization (WHO), without interventions, the number of diabetic incidences is expected to be at least 629 million by 2045. Uncontrolled diabetes gradually leads to progressive damage to eyes, heart, kidneys, blood vessels and nerves. Method: The paper presents a critical review of existing statistical and Artificial Intelligence (AI) based machine learning techniques with respect to DM complications namely retinopathy, neuropathy and nephropathy. The statistical and machine learning analytic techniques are used to structure the subsequent content review. Result: It has been inferred that statistical analysis can help only in inferential and descriptive analysis whereas, AI based machine learning models can even provide actionable prediction models for faster and accurate diagnose of complications associated with DM. Conclusion: The integration of AI based analytics techniques like machine learning and deep learning in clinical medicine will result in improved disease management through faster disease detection and cost reduction for disease treatment.

scholarly journals Sustainability Performance Assessment Using Self-Organizing Maps (SOM) and Classification and Ensembles of Regression Trees (CART)

2021 ◽  
Vol 13 (7) ◽  
pp. 3870
Author(s):  
Mehrbakhsh Nilashi ◽  
Shahla Asadi ◽  
Rabab Ali Abumalloh ◽  
Sarminah Samad ◽  
Fahad Ghabban ◽  
...  
Keyword(s):  
Performance Assessment ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Sustainability Assessment ◽  
Regression Trees ◽  
Machine Learning Techniques ◽  
Coefficient Of Determination ◽  
Sustainability Performance ◽  
Learning Techniques ◽  
Self Organizing

This study aims to develop a new approach based on machine learning techniques to assess sustainability performance. Two main dimensions of sustainability, ecological sustainability, and human sustainability, were considered in this study. A set of sustainability indicators was used, and the research method in this study was developed using cluster analysis and prediction learning techniques. A Self-Organizing Map (SOM) was applied for data clustering, while Classification and Regression Trees (CART) were applied to assess sustainability performance. The proposed method was evaluated through Sustainability Assessment by Fuzzy Evaluation (SAFE) dataset, which comprises various indicators of sustainability performance in 128 countries. Eight clusters from the data were found through the SOM clustering technique. A prediction model was found in each cluster through the CART technique. In addition, an ensemble of CART was constructed in each cluster of SOM to increase the prediction accuracy of CART. All prediction models were assessed through the adjusted coefficient of determination approach. The results demonstrated that the prediction accuracy values were high in all CART models. The results indicated that the method developed by ensembles of CART and clustering provide higher prediction accuracy than individual CART models. The main advantage of integrating the proposed method is its ability to automate decision rules from big data for prediction models. The method proposed in this study could be implemented as an effective tool for sustainability performance assessment.

Feasibility study of automatically performing the concrete delivery dispatching through machine learning techniques

2015 ◽  
Vol 22 (5) ◽  
pp. 573-590 ◽  
Author(s):  
Mojtaba Maghrebi ◽  
Claude Sammut ◽  
S. Travis Waller
Keyword(s):  
Machine Learning ◽  
Human Resources ◽  
Design Methodology ◽  
Machine Learning Techniques ◽  
Mixing Process ◽  
Content Type ◽  
Practical Solution ◽  
Learning Techniques ◽  
Ready Mixed Concrete ◽  
Practical Implications

Purpose – The purpose of this paper is to study the implementation of machine learning (ML) techniques in order to automatically measure the feasibility of performing ready mixed concrete (RMC) dispatching jobs. Design/methodology/approach – Six ML techniques were selected and tested on data that was extracted from a developed simulation model and answered by a human expert. Findings – The results show that the performance of most of selected algorithms were the same and achieved an accuracy of around 80 per cent in terms of accuracy for the examined cases. Practical implications – This approach can be applied in practice to match experts’ decisions. Originality/value – In this paper the feasibility of handling complex concrete delivery problems by ML techniques is studied. Currently, most of the concrete mixing process is done by machines. However, RMC dispatching still relies on human resources to complete many tasks. In this paper the authors are addressing to reconstruct experts’ decisions as only practical solution.

Autism Spectrum Disorder Identification Using Polynomial Distribution based Convolutional Neural Network

2021 ◽  
Vol 19 (2) ◽  
pp. 19-30
Author(s):  
G. Nagarajan ◽  
Dr.A. Mahabub Basha ◽  
R. Poornima
Keyword(s):  
Neural Network ◽  
Prediction Models ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Machine Learning Techniques ◽  
Hybrid Technique ◽  
Learning Techniques ◽  
Chicken Swarm Optimization ◽  
Sensitivity Specificity ◽  
Polynomial Distribution

One main psychiatric disorder found in humans is ASD (Autistic Spectrum Disorder). The disease manifests in a mental disorder that restricts humans from communications, language, speech in terms of their individual abilities. Even though its cure is complex and literally impossible, its early detection is required for mitigating its intensity. ASD does not have a pre-defined age for affecting humans. A system for effectively predicting ASD based on MLTs (Machine Learning Techniques) is proposed in this work. Hybrid APMs (Autism Prediction Models) combining multiple techniques like RF (Random Forest), CART (Classification and Regression Trees), RF-ID3 (RF-Iterative Dichotomiser 3) perform well, but face issues in memory usage, execution times and inadequate feature selections. Taking these issues into account, this work overcomes these hurdles in this proposed work with a hybrid technique that combines MCSO (Modified Chicken Swarm Optimization) and PDCNN (Polynomial Distribution based Convolution Neural Network) algorithms for its objective. The proposed scheme’s experimental results prove its higher levels of accuracy, precision, sensitivity, specificity, FPRs (False Positive Rates) and lowered time complexity when compared to other methods.

scholarly journals Survival prediction models since liver transplantation - comparisons between Cox models and machine learning techniques

2020 ◽  
Author(s):  
Georgios Kantidakis ◽  
Hein Putter ◽  
Carlo Lancia ◽  
Jacob de Boer ◽  
Andries E Braat ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Liver Transplantation ◽  
Prediction Models ◽  
Machine Learning Techniques ◽  
Brier Score ◽  
Survival Prediction ◽  
Cox Models ◽  
Learning Techniques ◽  
Random Survival Forest

Abstract Background: Predicting survival of recipients after liver transplantation is regarded as one of the most important challenges in contemporary medicine. Hence, improving on current prediction models is of great interest.Nowadays, there is a strong discussion in the medical field about machine learning (ML) and whether it has greater potential than traditional regression models when dealing with complex data. Criticism to ML is related to unsuitable performance measures and lack of interpretability which is important for clinicians.Methods: In this paper, ML techniques such as random forests and neural networks are applied to large data of 62294 patients from the United States with 97 predictors selected on clinical/statistical grounds, over more than 600, to predict survival from transplantation. Of particular interest is also the identification of potential risk factors. A comparison is performed between 3 different Cox models (with all variables, backward selection and LASSO) and 3 machine learning techniques: a random survival forest and 2 partial logistic artificial neural networks (PLANNs). For PLANNs, novel extensions to their original specification are tested. Emphasis is given on the advantages and pitfalls of each method and on the interpretability of the ML techniques.Results: Well-established predictive measures are employed from the survival field (C-index, Brier score and Integrated Brier Score) and the strongest prognostic factors are identified for each model. Clinical endpoint is overall graft-survival defined as the time between transplantation and the date of graft-failure or death. The random survival forest shows slightly better predictive performance than Cox models based on the C-index. Neural networks show better performance than both Cox models and random survival forest based on the Integrated Brier Score at 10 years.Conclusion: In this work, it is shown that machine learning techniques can be a useful tool for both prediction and interpretation in the survival context. From the ML techniques examined here, PLANN with 1 hidden layer predicts survival probabilities the most accurately, being as calibrated as the Cox model with all variables.

scholarly journals Machine Learning Frameworks in Cancer Detection

2021 ◽  
Vol 297 ◽  
pp. 01073
Author(s):  
Sabyasachi Pramanik ◽  
K. Martin Sagayam ◽  
Om Prakash Jena
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Cancer Development ◽  
Support Vector ◽  
Learning Approaches ◽  
Learning Techniques ◽  
Fact Finding ◽  
Risk Of Cancer

Cancer has been described as a diverse illness with several distinct subtypes that may occur simultaneously. As a result, early detection and forecast of cancer types have graced essentially in cancer fact-finding methods since they may help to improve the clinical treatment of cancer survivors. The significance of categorizing cancer suffers into higher or lower-threat categories has prompted numerous fact-finding associates from the bioscience and genomics field to investigate the utilization of machine learning (ML) algorithms in cancer diagnosis and treatment. Because of this, these methods have been used with the goal of simulating the development and treatment of malignant diseases in humans. Furthermore, the capacity of machine learning techniques to identify important characteristics from complicated datasets demonstrates the significance of these technologies. These technologies include Bayesian networks and artificial neural networks, along with a number of other approaches. Decision Trees and Support Vector Machines which have already been extensively used in cancer research for the creation of predictive models, also lead to accurate decision making. The application of machine learning techniques may undoubtedly enhance our knowledge of cancer development; nevertheless, a sufficient degree of validation is required before these approaches can be considered for use in daily clinical practice. An overview of current machine learning approaches utilized in the simulation of cancer development is presented in this paper. All of the supervised machine learning approaches described here, along with a variety of input characteristics and data samples, are used to build the prediction models. In light of the increasing trend towards the use of machine learning methods in biomedical research, we offer the most current papers that have used these approaches to predict risk of cancer or patient outcomes in order to better understand cancer.

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