Financing risk monitoring method and device based on credit asset batch classification

Abstract

The invention provides a financing risk monitoring method and device based on credit asset batch classification, and relates to the technical field of finance, and the method comprises the steps: carrying out batch initial classification of debt items of basic data and debt item data of a user; performing batch cross adjustment on the debt item data after batch initial classification to obtain classified debt item data; and determining whether the classified debt item data has an abnormal risk or not, and if so, feeding back abnormal information to a manager, so that batch classification and financing risk monitoring can be performed on the credit assets of the user, and the classification efficiency, the asset safety and the risk monitoring capability of the credit assets are improved.

Description

technical field [0001] The present invention relates to the field of information processing technology, in particular to the field of financial technology, in particular to a financing risk monitoring method and device based on batch classification of credit assets. Background technique [0002] Credit asset classification includes asset quality classification and risk classification. It refers to the twelve-level classification of on-balance sheet assets and five-level classification of off-balance sheet credit assets of legal person customers based on the judgment of risk level and expected credit loss of specific debt items. The process of asset classification and division, so that the corresponding financial instrument measurement method can be selected according to the corresponding accounting standards for risk provision management. The essence of credit asset classification is to judge the possibility of debtors repaying the principal and interest of credit assets in ...

AI Technical Summary

Problems solved by technology

At present, the traditional method of classifying credit assets according to debts, although some information is collected automatically, but due to the large number of debts, the handling personnel need to spend a lot of time on repetitive tasks, and the labor cost of classification operations is high , poor user experience

At the same time, the manual operation of large business volume may cause inconsistent classification results under the same customer or contract due to operational errors, and the convenience of classification operation is low

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