Post-loan risk assessment method and device, and storage medium

Abstract

The embodiment of the invention discloses a post-loan risk assessment method and a device, and a storage medium, and the method comprises the following steps: carrying out the preprocessing of storedloan data, including data dimension reduction, data re-classification, data combination and data cleaning, and the loan data after classification comprises static data and dynamic data; performing feature engineering on the preprocessed dynamic data to extract corresponding feature engineering features; constructing an evaluation model based on a deep learning network, and training the evaluationmodel by adopting expert features and feature engineering features in the financial model; and performing data evaluation based on the evaluation model and outputting a final approval list. Accordingto the method, the efficiency of post-loan data analysis can be improved, the precision of a risk assessment model is improved, the predictable times of default are increased, and finally, the post-loan risk can be effectively controlled.

Description

technical field [0001] The invention relates to the technical field of post-loan risk management and control, in particular to a post-loan risk assessment method and device, and a storage medium. Background technique [0002] With the deepening development of inclusive finance, the financial lending market is becoming more and more mature, and the demand for loans by small and micro enterprises is increasing. At the same time, the requirements for post-loan management, post-loan early warning, and collection are also increasing. How to integrate technology under the existing conditions to improve the level of post-loan risk management. Scientific and reasonable analysis and prediction of various risks can help bank credit departments achieve efficient operation management and reduce operating costs. [0003] At present, the traditional post-credit management scoring and rating technology for small and micro enterprises within the traditional bank is still using data related...

AI Technical Summary

Problems solved by technology

In fact, the current small and micro enterprise financing has the characteristics of "short, small, frequent, urgent, and scattered", which makes it difficult to apply the traditional post-credit management model, and it is difficult to effectively obtain real data through individual enterprises, special projects, channels, etc. , the financial report data of small and micro enterprises are easily distorted, which makes it difficult to accurately evaluate risks using the evaluation technology used in traditional methods. This not only makes the post-loan work of account managers cumbersome and disorderly, but also increases the workload, and cannot effectively identify potential post-loan risks.

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