Abstract: Based on the theoretical framework of TEI@I methodology, for the first time, we study the calculation method of the probability of borrower's credit quality and its forecasting probability prediction application. In this paper, text mining is used to deal with the instability of borrower's credit quality, credit grade and credit score are used for classification, credit quality grade is classified by fuzzy classification, and the conversion probability of credit quality is defined. The conditional probability measurement model is extended to the conditional probability of credit grade and the conditional probability of credit score. The calculation formula of conditional probability of credit quality is obtained by using the method of total probability calculation. Referring to the calculation method of S&P credit transfer matrix, and using Markov C-K equation to predict the transfer probability of borrower's credit quality grade, this paper jointly studies the probability of borrower's loss of connection. The results show that the higher the credit quality grade is, the higher the probability of timely repayment is, the lower the credit quality grade is, and the higher the probability of bad debts is; the closer the borrower is to the "lost link" grade, the greater the possibility of losing link; with the extension of the borrowing period, it becomes less likely to keep the original credit quality grade and the transition probability to the next level will gradually increase.

Key words: TEI@I methodology, credit rating and credit scoring, credit quality transfer probability, loss probability prediction, C-K equation