Skip to main content
SpringerLink
Log in

A novel multi-objective particle swarm optimization for comprehensible credit scoring

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Credit scoring is an important tool for banks and financial institutions to measure credit risk. Linear discriminant analysis (LDA) which according to the score of each credit applicant categorizes these applicants by a cutoff is a comprehensible and robust method in the credit scoring domain. This work presents a novel multi-objective particle swarm optimization for credit scoring (MOPSO-CS), and MOPSO-CS focuses on enhancing credit scoring models based on LDA in three aspects: (i) to construct a higher accuracy credit scoring model which is easy to be interpreted; (ii) to find the most suitable cutoff for discriminating “good credit” customers and “bad credit” customers; and (iii) to improve the sensitivity of the classifier by using multi-objective particle swarm optimization. Finally, through the experiments with two real-world data sets and two benchmark data sets, our proposed MOPSO-CS is compared with 11 counterparts: NaiveBayes, LR, SVM, ANN, DT, CART, bagging-DT, bagging-ANN, RF, MC2 and XGBoost, the results of experiments demonstrate MOPSO-CS outperforms the above-mentioned counterparts in term of sensitivity while maintaining an acceptable accuracy rate.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Akkoc S (2012) 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. Eur J Oper Res 222:168–178

    Article  Google Scholar 

  • Aliehyaei R, Khan S (2014) Ant colony optimization, genetic programming and a hybrid approach for credit scoring: a comparative study. In: 8th international conference on software, knowledge, information management and applications (SKIMA), pp 1–5

  • Bahnsen AC, Aouada D (2014) Example-dependent cost-sensitive logistic regression for credit scoring. In: 13th international conference on machine learning and applications (ICMLA), pp 263–269

  • Bellotti T, Crook J (2009) Support vector machines for credit scoring and discovery of significant features. Expert Syst Appl 36:3302–3308

    Article  Google Scholar 

  • Bhattacharyya S, Jha S, Tharakunnel K, Westland JC (2011) Data mining for credit card fraud: a comparative study. Decis Support Syst 50:602–613

    Article  Google Scholar 

  • Blanco A, Mejias RP, Lara J, Rayo S (2013) Credit scoring models for the microfinance industry using neural networks: evidence from Peru. Expert Syst Appl 40:356–364

    Article  Google Scholar 

  • Breiman L (1996) Bagging predictors. Mach Learn 24:123–140

    MATH  Google Scholar 

  • Chen MC, Huang SH (2003) Credit scoring and rejected instances reassigning through evolutionary computation techniques. Expert Syst Appl 24:433–441

    Article  Google Scholar 

  • Chi BW, Hsu CC (2012) A hybrid approach to integrate genetic algorithm into dual scoring model in enhancing the performance of credit scoring model. Expert Syst Appl 39:2650–2661

    Article  Google Scholar 

  • Das S, Abraham A, Konar A (2008) Automatic kernel clustering with a multi-elitist particle swarm optimization algorithm. Pattern Recognit Lett 29:688–699

    Article  Google Scholar 

  • Dehuri S, Cho SB (2009) Multi-criterion Pareto based particle swarm optimized polynomial neural network for classification: a review and state-of-the-art. Comput Sci Rev 3:19–40

    Article  MATH  Google Scholar 

  • Farquad M, Bose I (2012) Preprocessing unbalanced data using support vector machine. Decis Support Syst 53:226–233

    Article  Google Scholar 

  • Fernandes GB, Artes R (2016) Spatial dependence in credit risk and its improvement in credit scoring. Eur J Oper Res 249:517–524

    Article  MathSciNet  MATH  Google Scholar 

  • Goh CK, Tan KC, Liu DS, Chiam SC (2010) A competitive and cooperative co-evolutionary approach to multi-objective particle swarm optimization algorithm design. Eur J Oper Res 202:42–54

    Article  MATH  Google Scholar 

  • Hajek P (2011) Municipal credit rating modelling by neural networks. Decis Support Syst 51:108–118

    Article  Google Scholar 

  • Hand DJ, Henley WE (1997) Statistical classification methods in consumer credit scoring: a review. J R Stat Soc Ser A (Stat Soc) 160:523–54

    Article  Google Scholar 

  • Harris T (2015) Credit scoring using the clustered support vector machine. Expert Syst Appl 42:741–750

    Article  Google Scholar 

  • He J, Zhang YC, Shi Y (2010) Domain-driven classification based on multiple criteria and multiple constraint-level programming for intelligent credit scoring. IEEE Trans Knowl Data Eng 22:826–838

    Article  Google Scholar 

  • Huang CL, Chen MC, Wang CJ (2007) Credit scoring with a data mining approach based on support vector machines. Expert Syst Appl 33:847–856

    Article  Google Scholar 

  • Huang JJ, Tzeng GH, Ong CS (2006) Two-stage genetic programming (2SGP) for the credit scoring model. Appl Math Comput 174:1039–1053

    MathSciNet  MATH  Google Scholar 

  • Jo H, Han I (1997) Bankruptcy prediction using case-based reasoning, neural networks, and discriminant analysis. Expert Syst Appl 13:97–108

    Article  Google Scholar 

  • Kennedy J, Eberhart R (1995) Particle swarm optimization. Proc IEEE Int Conf Neural Netw 4:1942–1948

    Article  Google Scholar 

  • Kim JC, Kim DH, Kim J, Ye JS, Lee HS (2012) Segmenting the Korean housing market using multiple discriminant analysis. Constr Manag Econ 18:2650–2661

    Google Scholar 

  • Koutanaei FN, Sajedi H, Khanbabaei M (2015) A hybrid data mining model of feature selection algorithms and ensemble learning classifiers for credit scoring. J Retail Consum Serv 27:11–23

    Article  Google Scholar 

  • Lee TS, Chen IF (2005) A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines. Expert Syst Appl 28:743–752

    Article  Google Scholar 

  • Lee TS, Chen NJ (2002) Investigating the information content of non-cash-trading index futures using neural networks. Expert Syst Appl 22:225–234

    Article  Google Scholar 

  • Lessmann S, Baesens B, Seow HV, Thomas LC (2015) Benchmarking state-of-the-art classification algorithms for credit scoring: an update of research. Eur J Oper Res 247:124–136

    Article  MATH  Google Scholar 

  • Martens D, Baesens B, Gestel TV, Vanthienen J (2007) Comprehensible credit scoring models using rule extraction from support vector machines. Eur J Oper Res 183:1466–1476

    Article  MATH  Google Scholar 

  • Nanni L, Lumini A (2009) An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring. Expert Syst Appl 36:3028–3033

    Article  Google Scholar 

  • Niklis D, Doumpos M, Zopounidis C (2014) Combining market and accounting-based models for credit scoring using a classification scheme based on support vector machines. Appl Math Comput 234:69–81

    MathSciNet  MATH  Google Scholar 

  • Olson DL, Delen D, Meng Y (2012) Comparative analysis of data mining methods for bankruptcy prediction. Decis Support Syst 52:464–473

    Article  Google Scholar 

  • Omkar SN, Mudigere D, Naik GN, Gopalakrishnan S (2008) Vector evaluated particle swarm optimization (VEPSO) for multi-objective design optimization of composite structures. Comput Struct 86:1–14

    Article  Google Scholar 

  • Omran MGH, Salman A, Engelbrecht AP (2006) Dynamic clustering using particle swarm optimization with application in image segmentation. Pattern Anal Appl 8:332–344

    Article  MathSciNet  Google Scholar 

  • Rezac M (2011) Advanced empirical estimate of information value for credit scoring models. Acta Univ Agric Silvic Mendel Brunenis 35:267–274

    Article  Google Scholar 

  • Thomas LC (2000) A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers. Int J Forecast 16:149–172

    Article  Google Scholar 

  • Tomczak JM, Ziȩba M (2015) Classification restricted Boltzmann machine for credit comprehensible scoring model. Expert Syst Appl 42:1789–1796

    Article  Google Scholar 

  • Wang G, Ma J, Huang LH, Xu KQ (2012) Two credit scoring models based on dual strategy ensemble trees. Knowl Based Syst 26:61–68

    Article  Google Scholar 

  • West D (2011) Neural network credit scoring models. Comput Oper Res 27:1131–1152

    Article  MATH  Google Scholar 

  • Xia Y, Liu C, Li Y, Liu N (2017) A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring. Expert Syst Appl 78:225–241

    Article  Google Scholar 

  • Xiao H, Xiao Z, Wang Y (2016) Ensemble classification based on supervised clustering for credit scoring. Appl Soft Comput 43:73–86

    Article  Google Scholar 

  • Yeh CC, Lin FY, Hsu CY (2012) A hybrid KMV model, random forests and rough set theory approach for credit rating. Knowl Based Syst 33:166–172

    Article  Google Scholar 

  • Zhao ZY, Xu SX, Kang BH, Kabir MMJ, Liu YL, Wasinger R (2015) Investigation and improvement of multi-layer perception neural networks for credit scoring. Expert Syst Appl 42:3508–3516

    Article  Google Scholar 

Download references

Funding

This study was funded by Zhejiang provincial education department project (Y201636906), Ningbo innovative team project (2016C11024), Zhejiang provincial natural science foundation of China (Y16G010035) and national natural science foundation of China (71271191).

Author information

Authors and Affiliations

  1. Ningbo Institute of Technology, Zhejiang University, Ningbo, 315100, China

    Yan Guo, Libo Xu & Wei Liu

  2. Institute of Information Technology, Nanjing University of Finance and Economics, Nanjing, China

    Jing He

  3. Department of Software and Electronical Engineering, Swinburne University of Technology, Melbourne, Australia

    Jing He

Authors
  1. Yan Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jing He
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Libo Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wei Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yan Guo.

Ethics declarations

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Communicated by V. Loia.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guo, Y., He, J., Xu, L. et al. A novel multi-objective particle swarm optimization for comprehensible credit scoring. Soft Comput 23, 9009–9023 (2019). https://doi.org/10.1007/s00500-018-3509-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-018-3509-y

Keywords

Search

Navigation