scholarly journals Credit Risk Management Using Automatic Machine Learning

2020 ◽  
Vol 14 (2) ◽  
Author(s):  
Bartłomiej Gaweł ◽  
Andrzej Paliński
Keyword(s):  
Data Mining ◽  
Credit Risk ◽  
Logit Model ◽  
Credit Card ◽  
Traditional Approach ◽  
Automatic Machine ◽  
Classification Models ◽  
Credit Risk Management ◽  
Similar Accuracy

The article presents the basic techniques of data mining implemented in typical commercial software. They were used to assess the risk of credit card debt repayment. The article assesses the quality of classification models derived from data mining techniques and compares their results with the traditional approach using a logit model to assess credit risk. It turns out that data mining models provide similar accuracy of classification compared to the logit model, but they require much less work and facilitate the automation of the process of building scoring models.

Beyond Classification

2008 ◽  
pp. 1855-1876
Author(s):  
Anna Olecka
Keyword(s):  
Data Mining ◽  
Credit Risk ◽  
Credit Card ◽  
Classification Scheme ◽  
Credit Scoring ◽  
Complex Nature ◽  
Acquisition Process ◽  
Business Objective ◽  
Simplifying Assumptions ◽  
History Of

This chapter will focus on challenges in modeling credit risk for new accounts acquisition process in the credit card industry. First section provides an overview and a brief history of credit scoring. The second section looks at some of the challenges specific to the credit industry. In many of these applications business objective is tied only indirectly to the classification scheme. Opposing objectives, such as response, profit and risk, often play a tug of war with each other. Solving a business problem of such complex nature often requires a multiple of models working jointly. Challenges to data mining lie in exploring solutions that go beyond traditional, well-documented methodology and need for simplifying assumptions; often necessitated by the reality of dataset sizes and/or implementation issues. Examples of such challenges form an illustrative example of a compromise between data mining theory and applications.

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.

Beyond Classification

2011 ◽  
pp. 139-161
Author(s):  
Anna Olecka
Keyword(s):  
Data Mining ◽  
Credit Risk ◽  
Credit Card ◽  
Classification Scheme ◽  
Credit Scoring ◽  
Complex Nature ◽  
Acquisition Process ◽  
Business Objective ◽  
Simplifying Assumptions ◽  
History Of

This chapter will focus on challenges in modeling credit risk for new accounts acquisition process in the credit card industry. First section provides an overview and a brief history of credit scoring. The second section looks at some of the challenges specific to the credit industry. In many of these applications business objective is tied only indirectly to the classification scheme. Opposing objectives, such as response, profit and risk, often play a tug of war with each other. Solving a business problem of such complex nature often requires a multiple of models working jointly. Challenges to data mining lie in exploring solutions that go beyond traditional, well-documented methodology and need for simplifying assumptions; often necessitated by the reality of dataset sizes and/or implementation issues. Examples of such challenges form an illustrative example of a compromise between data mining theory and applications.

scholarly journals ROC curve, lift chart and calibration plot

2006 ◽  
Vol 3 (1) ◽  
Author(s):  
Miha Vuk ◽  
Tomaž Curk
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Roc Curve ◽  
Classification Accuracy ◽  
Empirical Evaluation ◽  
Calibration Plot ◽  
Mathematical Framework ◽  
Classification Models ◽  
Classification Quality

This paper presents ROC curve, lift chart and calibration plot, three well known graphical techniques that are useful for evaluating the quality of classification models used in data mining and machine learning. Each technique, normally used and studied separately, defines its own measure of classification quality and its visualization. Here, we give a brief survey of the methods and establish a common mathematical framework which adds some new aspects, explanations and interrelations between these techniques. We conclude with an empirical evaluation and a few examples on how to use the presented techniques to boost classification accuracy.

scholarly journals IDENTIFICATION OF THE BANK’S DEFAULT CLIENTS BY MACHINE LEARNING METHODS ON THE BASIS OF BINNING

2021 ◽  
pp. 133-142
Author(s):  
Yurii Kleban ◽  
Nataliia Horoshko
Keyword(s):  
Credit Risk ◽  
Gradient Boosting ◽  
Credit Risk Management ◽  
Risk Assessment Models ◽  
Loan Portfolios ◽  
Extreme Gradient Boosting ◽  
Methodological Approaches

Introduction. In the current global crisis, the problem of the quality of banks’ loan portfolios is a topical issue. Among the methods of effective credit risk management is the assessment of the borrower’s creditworthiness. Improving the quality of analysis of the strengths and weaknesses of the counterparty will reduce the occurrence of unforeseen risks in the process of conducting credit operations. Given the importance of the role of creditworthiness assessment for decision-making, there is a need to improve and choose a methodology that will ensure the most accurate classification of the bank’s clients. Purpose. The aim of the work is to choose the best method for predicting the probability of default of commercial bank customers based on the analysis of approaches and testing of the built models. Method (methodology). The paper considers methodological approaches to modeling the insolvency of bank customers and determining the probability of repayment of loans based on binning indicators. Also, the credit risk assessment models based on the use of logit and probit regressions, the algorithm of extreme gradient boosting and artificial neural networks are constructed. The comparative analysis of the efficiency of the application of the used approaches is carried out. Results. The obtained results demonstrated the high accuracy of the models and their ability to identify non-creditworthy customers. The findings of the study and evaluation of mathematical approaches can be implemented in the work of banking structures and other credit institutions to spread the amount of problem fees in their loan portfolios.

scholarly journals Performance Comparison of Data Mining Algorithm to Predict Approval of Credit Card

SinkrOn ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 149
Author(s):  
Ipin Sugiyarto ◽  
Bibit Sudarsono ◽  
Umi Faddillah
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Feature Selection ◽  
Credit Risk ◽  
Credit Card ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Performance Comparison ◽  
Support Vector ◽  
Data Mining Algorithm

Credit analysis needs to identify and assess the factors that can affect customers in returning credit. Accurate measurement and good management ability in dealing with credit risk is an effort to save the economic operations unit and be beneficial for a stable and healthy financial system. Data mining prediction techniques are used to determine credit risk. Using the Cross-Industry Standard Process for Data Mining (CRISP-DM) method which consists of several stages, namely Business Understanding (dataset), Data Processing (Feature Selection PCA & Dimension Reduce), Algorithm Models (NN+PSO, SVM, LR), Evaluation (Validation and Accuracy). This study has tested the model using a neural network using the PCA selection feature and optimized with the Particle Swarm Optimize (PSO) algorithm to predict credit card approval. Several experiments were conducted to see the best results. The results of this study prove that the use of a single Neural Network method produces an accuracy of 80.33%. whereas the use of PCA + Neural Network + PSO hybrid method has been proven to increase accuracy to 82.67%. Likewise, the AUC NN value of 0.706 increased to 0.749 when the Neural Network was optimized using PSO and used feature selection. The purpose of this study is to implement and compare Support Vector Machine, Logistic Regression and Neural Network algorithms based on PCA and optimize PSO (Particle Swarm Optimization) to improve accuracy in predictions of credit card approvals.

scholarly journals Feasibility Study of Credit Risk Rating Systems in Banks

2017 ◽  
Vol 6 (12) ◽  
pp. 294-306
Author(s):  
Lakshmi P
Keyword(s):  
Risk Management ◽  
Credit Risk ◽  
Credit Risk Management ◽  
Credit Policy ◽  
Rating Systems ◽  
Public And Private ◽  
Risk Rating ◽  
Private Banks ◽  
Rating Model

Credit risk rating is an important tool used by banks to quantify risk associated with lending. Accuracy of the rating mechanism is an important aspect as it affects the nature and quality of credit decisions made. A wrong rating may affect not only the sustainability and goodwill of the banks; it can even affect the overall economic harmony and balance, as banks are barometers of the economy. Recent global economic crisis of 2008, itself showcases a need for very strict and accurate credit policy. Under this back drop, present study aims to analyze the credit risk rating mechanism of banks. A comparative study of the different risk rating models adopted by public and private banks in Thiruvananthapuram district (Kerala, India) is made and study attempts to determine the lacuna in the present risk rating model, if any. The study aims to provide suggestions to improve the credit risk management of banks.

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