Integration of Iterative Dichotomizer 3 and Boosted Decision Tree to Form Credit Scoring Profile

Alditama Agung Prasetyo, Budhi Kristianto

Abstract


Loan is becoming essential need in this modern life. Banks need to keep their NPL ratio low in order to maintain their financial health. One of customer’s screening techniques is credit scoring. Decision tree is a simple method to classify a condition into two different classes using given classifier, and widely used to perform credit scoring in the financial industry. We integrated Iterative Dichotomizer 3 and Boosted Decision Tree methods and used Microsoft Azure Machine Learning tools to perform credit score profiling. This study is cross sectional in time and using 600 instances data of loan submission in Tangerang, Indonesia. The result shows good performance with performance evaluation metric of accuracy, precision, recall, and F1 score are 0.85, 0.885, 0.793 and 0.836 respectively.

Keywords


Boosted Decision Tree, Credit Scoring, Iterative Dichotomizer 3

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References


Kontan. (2019), “NPL Indonesia salah satu tertinggi di Asean, begini kata pengamat (Indonesia’s NPL is one of the highest in Asean, according to observer)”, available at https://keuangan.kontan.co.id/news/npl-indonesia-salah-satu-tertinggi-di-asean-begini-kata-pengamat (accessed 1 May 2020).

Ilter, D. and Kocadagli, O. (2019), “Credit scoring by artificial neural networks based cross-entropy and fuzzy relations”. Sigma Journal of Engineering and Natural Sciences, Vol. 37 No. 3, pp.855-870.

Bastos, J. (2008), “Credit scoring with boosted decision trees”, working paper, Munich Personal RePEc Archive, Munich, Germany.

Kabari, L. and Nwachukwu, E. (2013). “Credit risk evaluating system using decision tree – neuro based model”. International Journal of Engineering Research & Technology, Vol. 2 No. 6, pp.2738-2745.

Ilayani, Nangi, J. and Pasrun, Y. (2018). “Aplikasi data mining untuk penilaian kredit menggunakan decision tree algoritma ID3 studi kasus PT. Mandala Multi Finance cabang Kendari (Data mining application for credit scoring using ID3 decision tree, case study of Mandala Multi Finance Corporation Kendari branch)”. semanTIK, Vol. 4 No. 1, pp.65-76.

Baesens, B., Setiono, R., Mues, C., and Vanthienen, J. (2003). “Using neural network rule extraction and decision tables for credit-risk evaluation”. Management Science, Vol. 49 No. 3, pp.312-329.

Bijak, K. and Thomas, L. (2012). “Does segmentation always improve model performance in credit scoring?”. Expert Systems with Applications, Vol. 39, pp.2433-2442.

Tsipouras, M., Exarchos, T., and Fotiadis, D. (2008). “A methodology for automated fuzzy model generation”. Fuzzy Sets and Systems, Vol. 159, pp.3201–3220.

Wahyuningtyas, G., Mukhlash, I., and Soetrisno. (2014). “Aplikasi data mining untuk penilaian kredit menggunakan metode Fuzzy Decision Tree (Data mining application for credit scoring using Fuzzy Decision Tree method)”. Jurnal Sains dan Seni POMITS, Vol. 2 No.1, pp.1-6.

Abdou, H. and Pointon, J. (2011). “Credit scoring, statistical techniques and evaluation criteria: A review of the literature”. Intelligent Systems in Accounting, Finance & Management, Vol. 18 (2-3), pp.59-88.

Eddy, Y. and Bakar, E. (2017). “Credit scoring models: Techniques and issues”. Journal of Advanced Research in Business and Management Studies, Vol. 7, No. 2, pp.29-41.

Faddoul, J., Chidlovskii, B., Gilleron, R., and Torre, F. (2012). “Learning multiple tasks with boosted decision trees”. Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Bristol, United Kingdom, pp.681-696.

Madadipouya, K. (2015). “A new decision tree method for data mining in medicine”. Advanced Computational Intelligence: An International Journal (ACII), Vol.2 No.3, pp.31-37.

Melissa, I. and Oetama, R. (2013). “Analisis data pembayaran kredit nasabah bank menggunakan metode data mining (Analysis of bank’s customer credit payment using data mining method)”. ULTIMA InfoSys, Vol. 4 No. 1, pp.18-27.

Ponomareva, N., Colthrust, T., Hendry, G., Haykal, S., and Radpour, S. (2017). “Compact multi-class boosted trees”. 2017 IEEE International Conference on Big Data, Boston, MA, USA.

Rahman, S., Irfan, M., Raza, M., Ghori, K., Yaqoob, S., and Awais, M. (2020). “Performance analysis of boosting classifiers in recognizing activities of daily living”. International Journal of Environmental Research and Public Health, Vol. 17 No. 3, pp.1082-1097.




DOI: https://doi.org/10.24167/sisforma.v7i2.2659

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