基于大数据的地方金融组织风险监测系统  

Risk monitoring system for local financial organization based on big data

作　　者：王帆[1] 张云鹏 韦立坚[1] 李杰 覃振杰 Wang Fan;Zhang Yunpeng;Wei Lijian;Li Jie;Qin Zhenjie(School of Business,Sun Yat‑Sen University,Guangzhou 510275,China;Guangzhou Fintech Co.Ltd,Guangzhou 510623,China)

机构地区：[1]中山大学管理学院,广州510275 [2]广州金融科技股份有限公司,广州510623

出　　处：《数智技术研究与应用》2025年第1期61-72,共12页SmartTech Innovations

基　　金：国家自然科学基金项目(72171239);国家社会科学基金重大项目(19ZDA103);广东省重点领域研发计划项目(2020B010110004);广东省自然科学基金杰出青年项目(2021B1515020073)。

摘　　要：地方金融组织是金融生态的重要组成部分,然而其财务数据不够透明和风险内控流程不够完善,而专业监管才初步建立,因此地方金融组织是当前金融风险监测的薄弱领域,急需通过大数据等新技术手段破解监管难题。文章通过大数据的方法,完成了对地方金融组织风险监测系统的设计与实现。我们从信用风险、财务风险、运营风险、合规风险和传播风险五个维度来设计监测指标,进一步提取出每一个风险分类下的二级指标。通过数据处理和偏相关性分析,确定候选指标,然后利用层次分析法和G1法对指标进行主观赋权,同时利用熵权法和均方差法对指标进行客观赋权,再利用几何平均法得到风险评价指标的综合赋权结果,由此建立了地方金融风险评价模型,并以广东省P2P网贷平台为例进行系统验证,从而实现了对地方金融风险的识别与预警。Local financial organizations are an important part of the financial ecology.However,their financial data are not transparent enough and the risk internal control process is not perfect,while professional supervision has just been established.Therefore,local financial organizations are the weak field of financial risk monitoring,and it is urgent to solve the supervision problems through new technological methods such as big data.This paper completes the design and implementation of risk monitoring system for local financial organizations.We design monitoring indicators from five dimensions of credit risk,financial risk,operational risk,compliance risk and communication risk,and further extract the secondary indicators under each risk classification.In this paper,candidate indicators are determined by data processing and partial correlation analysis.We use Analytic Hierarchy Process and G1 method for subjective weighting,and use entropy weight method and mean square deviation method for objective weighting,and use geometric average method to get the comprehensive weighting results of risk evaluation indicators,so as to establish the local financial risk evaluation model.We take the P2P lending platform in Guangdong province as an example for systematic verification,so as to realize the identification and early warning of local financial risks.

关 键 词：大数据 地方金融组织 组合赋权 风险监测系统 P2P网贷平台 

分 类 号：F83[经济管理—金融学]