Customer Credit Risk Assessment using Artificial Neural Networks

Nasser Mohammadi

34 citations0/0 files storedInternational Journal of Information Technology and Computer Science

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Financial Distress and Bankruptcy Prediction Neural Networks and Applications Imbalanced Data Classification Techniques

Abstract

Since the granting of banking facilities in recent years has faced problems such as customer credit risk and affects the profitability directly, customer credit risk assessment has become imperative for banks and it is used to distinguish good applicants from those who will probably default on repayments.In credit risk assessment, a score is assigned to each customer then by comparing it with the cut-off point score which distinguishes two classes of the applicants, customers are classified into two credit statuses either a good or bad applicant.Regarding good performance and their ability of classification, generalization and learning patterns, Multilayer Perceptron Neural Network model trained using various Back-Propagation (BP) algorithms considered in designing an evaluation model in this study.The BP algorithms, Levenberg-Marquardt (LM), Gradient descent, Conjugate gradient, Resilient, BFGS Quasinewton, and One-step secant were utilized.Each of these six networks runs and trains for different numbers of neurons within their hidden layer.Mean squared error (MSE) is used as a criterion to specify optimum number of neurons in the hidden layer.The results showed that LM algorithm converges faster to the network and achieves better performance than the other algorithms.At last, by comparing classification performance of neural network with a number of classification algorithms such as Logistic Regression and Decision Tree, the neural network model outperformed the others in customer credit risk assessment.In credit models, because the cost that Type II error rate imposes to the model is too high, therefore, Receiver Operating Characteristic curve is used to find appropriate cut-off point for a model that in addition to high Accuracy, has lower Type II error rate.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 34 citations exist upstream.

References

2000Neural network credit scoring models 2003Benchmarking state-of-the-art classification algorithms for credit scoring 2005Building credit scoring models using genetic programming 2008An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring 2012An empirical comparison of conventional techniques, neural networks and the three stage hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) model for credit scoring analysis: The case of Turkish credit card data 2012Credit scoring models for the microfinance industry using neural networks: Evidence from Peru 2007An intelligent-agent-based fuzzy group decision making model for financial multicriteria decision support: The case of credit scoring 2013Consumer credit risk: Individual probability estimates using machine learning

Cited by

2020Credit scoring by incorporating dynamic networked information 2021Credit ratings of Chinese households using factor scores and K-means clustering method 2017Credit Risk Assessment Algorithm Using Deep Neural Networks with Clustering and Merging 2018Credit Risk Prediction Using Artificial Neural Network Algorithm

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.