Credit Risk Prediction Using Artificial Neural Network Algorithm

No abstract is available for this paper.

• Publication year

  2018

• Venue

  International Journal of Modern Education and Computer Science

• Publication date

  2018-05-08

• Fields of study

  Business, Computer Science

• Identifiers
  DOI 10.5815/IJMECS.2018.05.02
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Customer Credit Risk Assessment using Artificial Neural Networks

  2016cited by this paper
• Credit scoring using the clustered support vector machine

  2015cited by this paper
• Genetic algorithms for credit scoring: Alternative fitness function performance comparison

  2015cited by this paper
• Credit risk prediction: A comparative study between discriminant analysis and the neural network approach

  2015cited by this paper
• Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research

  2015cited by this paper
• The Relationship between Liquidity Risk and Credit Risk in Banks

  2014cited by this paper
• Bank Size and Systemic Risk

  2014cited by this paper
• Nearest Neighbor Classifier Method for Making Loan Decision in Commercial Bank

  2014cited by this paper
• Bank Customer Credit Scoring by Using Fuzzy Expert System

  2014cited by this paper
• A novel model for credit card fraud detection using Artificial Immune Systems

  2014cited by this paper
• Cash Holdings and Credit Risk

  2012cited by this paper
• Incentivizing Calculated Risk-Taking: Evidence from an Experiment with Commercial Bank Loan Officers

  2012cited by this paper
• Determining Contribution of Features in Clustering Multidimensional Data Using Neural Network

  2012cited by this paper
• An Artificial Neural Network Approach for Credit Risk Management

  2011cited by this paper
• Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests

  2011cited by this paper
• A neural network approach for credit risk evaluation

  2008cited by this paper
• A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines

  2005cited by this paper
• Credit Risk Management

  2004cited by this paper
• Credit Risk: Modeling, Valuation And Hedging

  2004cited by this paper
• Multicriteria classification and sorting methods: A literature review

  2002cited by this paper
• Credit scoring model based on neural network

  2002cited by this paper
• Bankruptcy prediction for credit risk using neural networks: A survey and new results

  2001cited by this paper
• Predicting a corporate credit analyst's risk estimate by logistic and linear models

  1999cited by this paper
• Regression Analysis: Statistical Modeling of a Response Variable

  1998cited by this paper
• Statistical Classification Methods in Consumer Credit Scoring: a Review

  1997cited by this paper
• What's the point of credit scoring?

  1997cited by this paper
• Credit card fraud detection with a neural-network

  1994cited by this paper
• Interaction of judgemental and statistical forecasting methods: issues &

  1991cited by this paper
• Multilayer feedforward networks are universal approximators

  1989cited by this paper
• Best subsets logistic regression

  1989cited by this paper
• Generalization of back-propagation to recurrent neural networks.

  1987cited by this paper
• PITFALLS IN THE APPLICATION OF DISCRIMINANT ANALYSIS IN BUSINESS, FINANCE, AND ECONOMICS

  1977cited by this paper

• Business Risk Prediction and Management Based on Artificial Intelligence by Applying Machine Learning Algorithms to Increase Business Flexibility and Social Stability: Banking Credit Risk Approach

  2025cites this paper
• Machine Learning Innovations in Credit Card Approval: A Comparative Study of Algorithms

  2025cites this paper
• Credit Risk Modelling Using RNN-LSTM Hybrid Model for Digital Financial Institutions

  2024cites this paper
• P2P credit risk management with KG-GNN: a knowledge graph and graph neural network-based approach

  2024cites this paper
• Consumer credit risk analysis through artificial intelligence: a comparative study between the classical approach of logistic regression and advanced machine learning techniques

  2024cites this paper
• Data analytics for risk forecast in financial industry

  2023cites this paper
• CREDIT CARD APPLICANT WORTHINESS PREDICTION USING MACHINE LEARNING

  2022cites this paper
• Applying Machine Learning Methods for Credit Card Payment Default Prediction With Cost Savings

  2022cites this paper
• A comprehensive modeling method of continuous and discrete variables for personal credit forecasting

  2022cites this paper
• Managing Bank Loans By Using Neural Networks

  2022cites this paper
• Default analysis in mortgage risk with conventional and deep machine learning focusing on 2008–2009

  2021cites this paper
• An Explanation Framework for Interpretable Credit Scoring

  2021cites this paper
• Prediksi Status Pinjaman Bank dengan Deep Learning Neural Network

  2021cites this paper
• Application of a Modified Logarithmic Rule for Forecasting

  2021cites this paper
• Explainable AI for Interpretable Credit Scoring

  2020cites this paper
• Risk Prediction Model for Dengue Transmission using Artificial Neural Networks

  2020cites this paper
• Weighted Random Forests for Evaluating Financial Credit Risk

  2019cites this paper
• Determinants of Domestic Credit to the Private Sector in Ghana: Application of Vector Auto-Regressive Method

  2019cites this paper
• Experiences with Improving the Transparency of AI Models and Services

  2019cites this paper
• Analysis of Oscillator Behavior Under Multi-frequency Excitation for Oscillatory Neural Networks

  2019cites this paper
• Computer Science & Information Technology

  2018cites this paper
• Predicting Default Payment of Credit Card Users: Applying Data Mining Techniques

  2018cites this paper