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Monitoring and early warning system of a certain bank

customer pain points

, with the development of the Internet and the continuous online of various business systems of banks, the original risk management methods have not adapted to the requirements of risk management under the new situation, and the regular post-event supervision system also has great limitations. Relying solely on traditional rules and regulations, ideological education, post-inspection and other methods to solve errors, violations, and improve execution and other measures, it has become increasingly inadequate, and scientific and technological means must be used to assist in solving the problem of risk prevention.

Project Objectives

A bank early warning system project is to establish an active monitoring and early warning system of "model-driven, classified management, early warning, active exit and effective conduction", which can realize the whole process management from loan issuance to settlement.

Project Construction

construction cycle:
2022.1-2022.10 one-time online;
Project members:
Projectinputhas 6 members, including 2 in the full stack, 2 in the back end, 1 in the front end and 1 in the test.

Customer Value

This early warning system project is the industry's first high-level ML model-driven monitoring and early warning system. The system implements the concept of big data risk control. It focuses on the 20-character policy of "model-driven, classified management, early warning, active exit and effective transmission" of the retail asset management system. It uses 12 ML models of "full business varieties" and "multi-time windows" to efficiently drive risk identification in combination with the business experience index system, accurate prediction of customer risk is achieved.

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