Credit Risk Prediction Using Fuzzy Immune Learning

Abstract

The use of credit has grown considerably in recent years. Banks and financial institutions confront credit risks to conduct their business. Good management of these risks is a key factor to increase profitability. Therefore, every bank needs to predict the credit risks of its customers. Credit risk prediction has been widely studied in the field of data mining as a classification problem. This paper proposes a new classifier using immune principles and fuzzy rules to predict quality factors of individuals in banks. The proposed model is combined with fuzzy pattern classification to extract accurate fuzzy if-then rules. In our proposed model, we have used immune memory to remember good B cells during the cloning process. We have designed two forms of memory: simple memory and k-layer memory. Two real world credit data sets in UCI machine learning repository are selected as experimental data to show the accuracy of the proposed classifier. We compare the performance of our immune-based learning system with results obtained by several well-known classifiers. Results indicate that the proposed immune-based classification system is accurate in detecting credit risks.

Machine learning predictivity applied to consumer creditworthiness

“…This plot shows that this model is potentially not overfitting, since both curves are decreasing and going in the same way. Taking into account the SVM algorithms, we build two models: one with a linear kernel function [23,31] and the other with a radial basis Kernel function [14,20,22,35], implemented by the R package called e1071 and parallel SVM. A Kernel K is a function that takes two points x i and x j from the input space and computes the scalar product of that data in the feature space.…”

“…Many authors, e.g. [14,18,21,26,28,27], and [34], use similar variables, such as income, past loans, savings amount, marital status, type of job, and number of dependents to analyze credit risk with machine learning techniques. Notwithstanding, great part of them is also available in the German and Australian credit data.…”

“…Lei et al, Shen et al, Yeh and Lien [ 18 , 26 , 34 ] investigated a Taiwanese credit card database, and Twala [ 30 ] analyzed credit operations in United States, Germany and Australia. These two last countries are also studied by Damrongsakmethee and Neagoe, Feng et al, Kamalloo and Saniee Abadeh, Kozodoi et al, Moula et al, Shi et al, Siami et al and Xiao et al [ 9 , 12 , 14 , 16 , 22 , 27 , 28 , 33 ].…”

Business Analytics using Random Forest Trees for Credit Risk Prediction: A Comparison Study

“…Support Vector Machines [6] Artificial Neural Networks [7,8] Feature Selection Based using ANN [9] Ensemble ANN [10] ANN and Decision Tables [11] Evolutionary Product-ANN [12] Fuzzy Immune Learning [13] Genetic Programming [14] Genetic Programming and SVM [15] Wavelet Networks and Particle Swarm Optimization [16] Various AI Techniques [17,18] ML homogenous and hybrid approaches show promising results; nevertheless they do not overperform simpler approaches significantly. Per contra, simpler approaches ask for attention with an opportunity for efficient prediction performance.…”

Improved Fuzzy K-Nearest Neighbor Using Modified Particle Swarm Optimization

“…Network [6], Support Vector Machine [7], Artificial Neural Network [8], Fuzzy Immune Learning [9] and Logistic Regression [10]. The k-Nearest Neighbor (k-NN) method is the most popular machine-learning method, simple and easy to implement (Wang & Li, 2010), k-NN has two weaknesses.…”