[Objective] This paper proposes a new multi-layer cascade classifier based on multiple-support vector machines, aiming to address the credit scoring issues of financial institutions. [Methods] The proposed hybrid model combines the ideas of genetic algorithm, machine learning and ensemble learning. The framework includes support vector machine classifier, normalization method, feature extraction, parameter optimization, 10-fold cross evaluation and other technologies. We tested the layer deepening strategy, attribute reuse method, and fitness function diversification by experiment. [Results] We examined the support vector machine optimized by genetic algorithm with Australian Credit Approval dataset. The prediction accuracy was improved as the increase of layers, and the overall frame prediction accuracy reached 93.33%. [Limitations] The proposed method only uses SVM, which needs to be expanded. There are many classifiers in the framework, which took long time to train and optimize. [Conclusions] The proposed classifier could effectively improve credit scoring services, and finish similar binary classification tasks.
Thomas L C, Edelman D B, Crook J N. Credit Scoring and Its Applications[M]. Philadelphia: Society for Industrial and Applied Mathematics, 2002.
[2]
Siddiqi N. Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring[M]. Hoboken: SAS Publishing, 2005.
[3]
Baesens B, Setiono R, Mues C, et al. Using Neural Network Rule Extraction and Decision Tables for Credit-Risk Evaluation[J]. Management Science, 2003, 49(3): 312-329.
doi: 10.1287/mnsc.49.3.312.12739
[4]
Thomas L C. A Survey of Credit and Behavioural Scoring: Forecasting Financial Risk of Lending to Consumers[J]. International Journal of Forecasting, 2000, 16(2): 149-172.
doi: 10.1016/S0169-2070(00)00034-0
[5]
Šušteršič M, Mramor D, Zupan J R. Consumer Credit Scoring Models with Limited Data[J]. Expert Systems with Applications, 2009, 36(3): 4736-4744.
doi: 10.1016/j.eswa.2008.06.016
[6]
Crone S F, Finlay S. Instance Sampling in Credit Scoring: An Empirical Study of Sample Size and Balancing[J]. International Journal of Forecasting, 2012, 28(1): 224-238.
doi: 10.1016/j.ijforecast.2011.07.006
[7]
Malhotra R, Malhotra D K. Evaluating Consumer Loans Using Neural Networks[J]. Omega, 2003, 31(2): 83-96.
doi: 10.1016/S0305-0483(03)00016-1
[8]
Akkoç S. Exploring the Nature of Credit Scoring: A Neuro Fuzzy Approach[J]. Fuzzy Economic Review, 2019, 24(1): 3-24.
[9]
West D. Neural Network Credit Scoring Models[J]. Computers & Operations Research, 2000, 27(11-12): 1131-1152.
doi: 10.1016/S0305-0548(99)00149-5
[10]
Khashman A. Neural Networks for Credit Risk Evaluation: Investigation of Different Neural Models and Learning Schemes[J]. Expert Systems with Applications, 2010, 37(9): 6233-6239.
doi: 10.1016/j.eswa.2010.02.101
[11]
Sasiada M, Fraczek-Szczypta A, Tadeusiewicz R. Efficiency Testing of Artificial Neural Networks in Predicting the Properties of Carbon Nanomaterials as Potential Systems for Nervous Tissue Stimulation and Regeneration[J]. Bio-Algorithms and Med-Systems, 2017, 13(1): 25-35.
[12]
Finlay S. Multiple Classifier Architectures and Their Application to Credit Risk Assessment[J]. European Journal of Operational Research, 2011, 210(2): 368-378.
doi: 10.1016/j.ejor.2010.09.029
[13]
Brown I, Mues C. An Experimental Comparison of Classification Algorithms for Imbalanced Credit Scoring Data Sets[J]. Expert Systems with Applications, 2012, 39(3): 3446-3453.
doi: 10.1016/j.eswa.2011.09.033
[14]
Ong C S, Huang J J, Tzeng G H. Building Credit Scoring Models Using Genetic Programming[J]. Expert Systems with Applications, 2005, 29(1): 41-47.
doi: 10.1016/j.eswa.2005.01.003
[15]
Hung C, Chen J H. A Selective Ensemble Based on Expected Probabilities for Bankruptcy Prediction[J]. Expert Systems with Applications, 2009, 36(3): 5297-5303.
doi: 10.1016/j.eswa.2008.06.068
[16]
Bishop C. Pattern Recognition and Machine Learning[M]. New York: Springer-Verlag, 2006.
[17]
Engelbrecht A P. Computational Intelligence[M]. Chichester: John Wiley & Sons, 2007.
[18]
Akko S. An 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[J]. European Journal of Operational Research, 2012, 222(1): 168-178.
doi: 10.1016/j.ejor.2012.04.009
[19]
Tsai C F. Feature Selection in Bankruptcy Prediction[J]. Knowledge-Based Systems, 2009, 22(2): 120-127.
doi: 10.1016/j.knosys.2008.08.002
[20]
Onay C, Öztürk E. A Review of Credit Scoring Research in the Age of Big Data[J]. Journal of Financial Regulation and Compliance, 2018, 26(3): 382-405.
doi: 10.1108/JFRC-06-2017-0054
[21]
Pratiwi H, Mukid M A, Hoyyi A, et al. Credit Scoring Analysis Using Pseudo Nearest Neighbor[J]. Journal of Physics: Conference Series, 2019, 1217(1): 0121008.
[22]
Wang G, Ma J, Huang L H, et al. Two Credit Scoring Models Based on Dual Strategy Ensemble Trees[J]. Knowledge-Based Systems, 2012, 26: 61-68.
doi: 10.1016/j.knosys.2011.06.020
[23]
Oreski S, Oreski D, Oreski G. Hybrid System with Genetic Algorithm and Artificial Neural Networks and Its Application to Retail Credit Risk Assessment[J]. Expert Systems with Applications, 2012, 39(16): 12605-12617.
doi: 10.1016/j.eswa.2012.05.023
[24]
Huang C L, Chen M C, Wang C J. Credit Scoring with a Data Mining Approach Based on Support Vector Machines[J]. Expert Systems with Applications, 2007, 33(4): 847-856.
doi: 10.1016/j.eswa.2006.07.007
[25]
Twala B. Multiple Classifier Application to Credit Risk Assessment[J]. Expert Systems with Applications, 2010, 37(4): 3326-3336.
doi: 10.1016/j.eswa.2009.10.018
[26]
Zhang D F, Zhou X Y, Leung S C H, et al. Vertical Bagging Decision Trees Model for Credit Scoring[J]. Expert Systems with Applications, 2010, 37(12): 7838-7843.
doi: 10.1016/j.eswa.2010.04.054
[27]
Ala’Raj M, Abbod M F. A New Hybrid Ensemble Credit Scoring Model Based on Classifiers Consensus System Approach[J]. Expert Systems with Applications, 2016, 64: 36-55.
doi: 10.1016/j.eswa.2016.07.017
[28]
Nanni L, Lumini A. An Experimental Comparison of Ensemble of Classifiers for Bankruptcy Prediction and Credit Scoring[J]. Expert Systems with Applications, 2009, 36(2): 3028-3033.
doi: 10.1016/j.eswa.2008.01.018
[29]
Tsai C F, Hsu Y F, Yen D C. A Comparative Study of Classifier Ensembles for Bankruptcy Prediction[J]. Applied Soft Computing, 2014, 24(11): 977-984.
doi: 10.1016/j.asoc.2014.08.047
[30]
Fogel L J. Autonomous Automata[J]. Industrial Research, 1962 (4): 14-19.
[31]
Gong D W, Sun J, Miao Z. A Set-Based Genetic Algorithm for Interval Many-Objective Optimization Problems[J]. IEEE Transactions on Evolutionary Computation, 2018, 22(1): 47-60.
doi: 10.1109/TEVC.2016.2634625
[32]
Cramer N L. A Representation for the Adaptive Generation of Simple Sequential Programs [C]//Proceedings of the 1st International Conference on Genetic Algorithms. 1985: 183-187.
[33]
Yang J, Honavar V. Feature Subset Selection Using a Genetic Algorithm[J]. IEEE Intelligent Systems and Their Applications, 1998, 13(2): 44-49.
doi: 10.1109/5254.671091
[34]
Qiu X, Xu J X, Xu Y H, et al. A New Differential Evolution Algorithm for Minimax Optimization in Robust Design[J]. IEEE Transactions on Cybernetics, 2018, 48(5): 1355-1368.
doi: 10.1109/TCYB.2017.2692963
[35]
Baranti L A, Lutfi A. Credit Scoring Model for SME Customer Assessment in a Telco Company[A]//Understanding Digital Industry[M]. Routledge, 2020: 141-144.
[36]
Kuncheva L I, Jain L C. Designing Classifier Fusion Systems by Genetic Algorithms[J]. IEEE Transactions on Evolutionary Computation, 2000, 4(4): 327-336.
doi: 10.1109/4235.887233
[37]
Raymer M L, Punch W F, Goodman E D, et al. Dimensionality Reduction Using Genetic Algorithms[J]. IEEE Transactions on Evolutionary Computation, 2000, 4(2): 164-171.
doi: 10.1109/4235.850656
[38]
Lei K, Xie Y X, Zhong S R, et al. Generative Adversarial Fusion Network for Class Imbalance Credit Scoring[J]. Neural Computing and Applications, 2020, 32(12): 8451-8462.
doi: 10.1007/s00521-019-04335-1
[39]
Gorzałczany M B, Rudziński F. A Multi-Objective Genetic Optimization for Fast, Fuzzy Rule-Based Credit Classification with Balanced Accuracy and Interpretability[J]. Applied Soft Computing, 2016, 40: 206-220.
doi: 10.1016/j.asoc.2015.11.037
[40]
Vukovic S, Delibasic B, Uzelac A, et al. A Case-Based Reasoning Model that Uses Preference Theory Functions for Credit Scoring[J]. Expert Systems with Applications, 2012, 39(9): 8389-8395.
doi: 10.1016/j.eswa.2012.01.181
[41]
Pławiak P. Novel Genetic Ensembles of Classifiers Applied to Myocardium Dysfunction Recognition Based on ECG Signals[J]. Swarm and Evolutionary Computation, 2018, 39: 192-208.
doi: 10.1016/j.swevo.2017.10.002
