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Bankruptcy Prediction with Explainable Artificial Intelligence for Early-Stage Business Models
Tuguldur Enkhtuya,강대기 한국인터넷방송통신학회 2023 International Journal of Internet, Broadcasting an Vol.15 No.3
Bankruptcy is a significant risk for start-up companies, but with the help of cutting-edge artificial intelligence technology, we can now predict bankruptcy with detailed explanations. In this paper, we implemented the Category Boosting algorithm following data cleaning and editing using OpenRefine. We further explained our model using the Shapash library, incorporating domain knowledge. By leveraging the 5C's credit domain knowledge, financial analysts in banks or investors can utilize the detailed results provided by our model to enhance their decision-making processes, even without extensive knowledge about AI. This empowers investors to identify potential bankruptcy risks in their business models, enabling them to make necessary improvements or reconsider their ventures before proceeding. As a result, our model serves as a "glass-box" model, allowing end-users to understand which specific financial indicators contribute to the prediction of bankruptcy. This transparency enhances trust and provides valuable insights for decision-makers in mitigating bankruptcy risks.
Experimental Analysis of Bankruptcy Prediction with SHAP framework on Polish Companies
Tuguldur Enkhtuya,강대기 한국인터넷방송통신학회 2023 International journal of advanced smart convergenc Vol.12 No.1
With the fast development of artificial intelligence day by day, users are demanding explanations about the results of algorithms and want to know what parameters influence the results. In this paper, we propose a model for bankruptcy prediction with interpretability using the SHAP framework. SHAP (SHAPley Additive exPlanations) is framework that gives a visualized result that can be used for explanation and interpretation of machine learning models. As a result, we can describe which features are important for the result of our deep learning model. SHAP framework Force plot result gives us top features which are mainly reflecting overall model score. Even though Fully Connected Neural Networks are a “black box” model, Shapley values help us to alleviate the “black box” problem. FCNNs perform well with complex dataset with more than 60 financial ratios. Combined with SHAP framework, we create an effective model with understandable interpretation. Bankruptcy is a rare event, then we avoid imbalanced dataset problem with the help of SMOTE. SMOTE is one of the oversampling technique that resulting synthetic samples are generated for the minority class. It uses Knearest neighbors algorithm for line connecting method in order to producing examples. We expect our model results assist financial analysts who are interested in forecasting bankruptcy prediction of companies in detail.
Cellular Membrane Composition Requirement by Antimicrobial and Anticancer Peptide GA-K4
Mishig-Ochir, Tsogbadrakh,Gombosuren, Davaadulam,Jigjid, Altanchimeg,Tuguldur, Badamkhatan,Chuluunbaatar, Galbadrakh,Urnukhsaikhan, Enerelt,Pathak, Chinar,Lee, Bong-Jin Bentham Science Publishers 2017 Protein and peptide letters Vol.24 No.3