Problem probability prediction method and device, computer equipment and storage medium

Abstract

The invention relates to a deep neural network in artificial intelligence, and provides a default probability prediction method and device, computer equipment and a storage medium. The method comprises the steps of acquiring an insurance source identifier, and inquiring whether a matched target entity exists in a preset knowledge graph or not according to the insurance source identifier; When thematched target entity exists, obtaining a to-be-predicted entity associated with the target entity in a preset knowledge graph; And based on the state attribute information of the to-be-predicted entity and the association relationship between the to-be-predicted entity and the target entity, obtaining a risk early warning feature vector, inputting the risk early warning feature vector into a trained risk early warning model for prediction, obtaining a default probability vector, and obtaining the default probability of the to-be-predicted entity according to the default probability vector. Byadopting the method, the efficiency and accuracy of default probability prediction can be improved.

Description

technical field [0001] The present application relates to the field of computer technology, in particular to a default probability prediction method, device, computer equipment and storage medium. Background technique [0002] The probability of default refers to the possibility that the borrowing enterprise will not be able to repay the principal and interest of the bank loan or perform related obligations according to the contract requirements within a certain period of time in the future. The probability of default is the basis for calculating the expected loss of the loan, loan pricing and credit portfolio management. At present, banks can only collect early warnings for corporate defaults after the time occurs, and the time lags behind. Most banks now use expert risk control models to carry out risk early warnings. Due to different standards for analysis and early warnings based on expert opinions, subjective It is highly sensitive, but its efficiency and accuracy are r...

AI Technical Summary

Problems solved by technology

At present, banks can only collect early warnings for corporate defaults after the time occurs, and the time lags behind. Most banks now use expert risk control models to carry out risk early warnings. Due to different standards for analysis and early warnings based on expert opinions, subjective It is highly sensitive, but its efficiency and accuracy are relatively low, which cannot meet the needs of banks.

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