Recently, not only traditional statistical techniques but also machine learning algorithms have been used to make more accurate bankruptcy predictions. But the insolvency rate of companies dealing with financial institutions is very low, resulting in a data imbalance problem. In particular, since data imbalance negatively affects the performance of artificial intelligence models, it is necessary to first perform the data imbalance process. In additional, as artificial intelligence algorithms are advanced for precise decision-making, regulatory pressure related to securing transparency of Artificial Intelligence models is gradually increasing, such as mandating the installation of explanation functions for Artificial Intelligence models. Therefore, this study aims to present guidelines for eXplainable Artificial Intelligence-based corporate bankruptcy prediction methodology applying SMOTE techniques and LIME algorithms to solve a data imbalance problem and model transparency problem in predicting corporate bankruptcy. The implications of this study are as follows. First, it was confirmed that SMOTE can effectively solve the data imbalance issue, a problem that can be easily overlooked in predicting corporate bankruptcy. Second, through the LIME algorithm, the basis for predicting bankruptcy of the machine learning model was visualized, and derive improvement priorities of financial variables that increase the possibility of bankruptcy of companies. Third, the scope of application of the algorithm in future research was expanded by confirming the possibility of using SMOTE and LIME through case application.

The aim of the study was to assess the safety of methionine sulfoxide reductase B2(CaMsrB2) protein as toxicity, allergenecity and identity of inserted gene product that transformed rice. Through bioinfomatical research of CaMsrB2, amino acid sequence of CaMsrB2 did not share overall homology with any known or suspected to be allergen or toxin protein. For the biochemical research, CaMsrB2 protein was expressed and purified. Using purified protein, we made a specific antibody. Purified protein was sequenced by Edman degradation methods and confirmed sequence identify.. The amino acid sequences of purified protein were the same as deduced amino acid sequences exclude N-terminal Histidine. And for the internal sequences of CaMsrB2, we performed MALDI-TOF Mass. The results of MALDI-TOF MS was compared Mascot Database and confirmed the sequence coverage was 56%. These results mean bacterially produced CaMsrB2 was the same with inserted gene product. With these purified and identified CaMsrB2 protein, we performed acute toxicity test. Following the OECD guideline 423, 2,000mg/Kg body weight protein were injected as oral administration. After 2 weeks, there did not shown any death and special symptoms.