Abstract
Neural network models have achieved a human-level performance in many application domains, including image classification, speech recognition and machine translation. However, in credit scoring application, neural network approach has been useless because of its black box nature that the relationship between contextual input and output cannot be completely understood. In this study, we investigate the advanced neural network approach and its’ explanation for credit scoring. We use the LIME technique to interpret the black box of such neural network and verify its’ trustworthiness by comparing a high interpretable logistic model. The results show that neural network models give higher accuracy and equivalent explanation with the logistic model.
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Acknowledgements
This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (No. 2017R1A2B4010826), by the Business for Cooperative R&D between Industry, Academy, and Research Institute funded Korea Small and Medium Business Administration (Grants No. C0541451), by the Private Intelligence Information Service Expansion (No. C0511-18-1001) funded by the NIPA (National IT Industry Promotion Agency) and by National Natural Science Foundation of China (Grant No. 61701104).
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Munkhdalai, L., Wang, L., Park, H.W., Ryu, K.H. (2019). Advanced Neural Network Approach, Its Explanation with LIME for Credit Scoring Application. In: Nguyen, N., Gaol, F., Hong, TP., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2019. Lecture Notes in Computer Science(), vol 11432. Springer, Cham. https://doi.org/10.1007/978-3-030-14802-7_35
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DOI: https://doi.org/10.1007/978-3-030-14802-7_35
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