数字风控模型数据

依托国家浙江电力公司准确、实时的电力大数据构建企业用电标签，可反映企业生产经营状况，为金融行业客户提供贷前、贷中、贷后的全面高效的风险管理服务，帮助银行分析客户的生产经营风险，制定风险管理策略，提高风险管理效率，并且可以通过灵活化组合模式辅助金融机构开展风险评估。1.数据清洗：对数据集进行清洗，包括去除异常数据、处理缺失值。
2.算法模型：本数据产品构建了企业数字风控模型，该模型根据企业基本信息、企业电量信息、企业电费信息、企业交费行为、企业用电容量五个维度的分析结果来评价分析企业生产经营情况，进行风险评估，具体计算指标计算逻辑规则如下：1.企业名称2.统一社会信用代码3.统计年月:指标统计时间4.合同容量5.运行容量6.开户距今天数7.有效开户数8.用电时长是否大于12个月9.用电时长是否大于24个月10.省份代码11.高耗能行业分类:高耗能行业划分（碳化硅、钢铁、炭素、铁合金等）12.用电客户类别:客户用电类别（普通工业、大工业用电、商业用电、非工业等）13.客户电表数:户号对应的表计数量14.电压等级:电压等级（交流10kV、交流380V、交流220V等）15.最近一次电费结算类型16.最近一次交费渠道17.近12个月应收电费小于300元的月份数:近12个月应收电费小于300元的月份数（按照企业统计）18.近12个月应收电费大于等于300元的月份数:近12个月应收电费大于等于300元的月份数（按照企业统计）19.近12个月应收电费最小值20.近12个月应收电费最大值21.近3个月应收电费平均值22.近6个月应收电费平23.近9个月应收电费平均值24.近12个月应收电费平均值25.近24个月应收电费平均值26.近3个月应收电费行业水平:企业近3个月应收电费平均值/该企业所处行业近3月应收电费平均值27.近6个月应收电费行业水平:企业近6个月应收电费平均值/该企业所处行业近6月应收电费平均值28.近9个月应收电费行业水平:企业近9个月应收电费平均值/该企业所处行业近9月应收电费平均值29.近12个月应收电费行业水平:企业近12个月应收电费平均值/该企业所处行业近12月应收电费平均值30.近24个月应收电费行业水平:企业近24个月应收电费平均值/该企业所处行业近24月应收电费平均值31.近3个月应收电费波动情况:近3个月应收电费同比32.近6个月应收电费波动情况:近6个月应收电费同比33.近9个月应收电费波动情况:近9个月应收电费同比34.近12个月应收电费波动情况:近12个月应收电费同比35.近24个月应收电费波动情况:近24个月应收电费同比36.近3个月应收电费增长趋势:近3个月应收电费环比等共计180个指标的加工逻辑。

Built upon the accurate and real-time large-scale power data from State Grid Zhejiang Electric Power Co., Ltd., this dataset constructs enterprise electricity usage tags that reflect the production and operation status of enterprises. It provides comprehensive and efficient risk management services for financial industry clients across pre-loan, in-loan, and post-loan stages, helping banks analyze the production and operation risks of their customers, formulate risk management strategies, improve risk management efficiency, and assist financial institutions in conducting risk assessments via flexible combination models.
1. Data Cleaning: Clean the dataset by removing abnormal data and handling missing values.
2. Algorithm Model: This data product develops an enterprise digital risk control model, which evaluates and analyzes the production and operation status of enterprises and conducts risk assessment based on the analysis results of five dimensions: basic enterprise information, enterprise electricity consumption information, enterprise electricity fee information, enterprise payment behavior, and enterprise electricity capacity. The specific calculation logic rules for the 180 indicators are as follows:
1. Enterprise Name
2. Unified Social Credit Identifier
3. Statistical Year and Month: Indicator statistical time
4. Contracted Capacity
5. Operating Capacity
6. Days Since Account Opening
7. Valid Number of Accounts
8. Whether the electricity usage duration exceeds 12 months
9. Whether the electricity usage duration exceeds 24 months
10. Province Code
11. High-energy-consuming Industry Classification: Division of high-energy-consuming industries (silicon carbide, steel, carbon, ferroalloy, etc.)
12. Customer Electricity Category: Customer electricity consumption types (general industrial, large industrial electricity, commercial electricity, non-industrial, etc.)
13. Number of Customer Meters: Number of meters corresponding to the household number
14. Voltage Level: Voltage levels (AC 10kV, AC 380V, AC 220V, etc.)
15. Last Electricity Fee Settlement Type
16. Last Payment Channel
17. Number of months with accounts receivable electricity fee less than 300 yuan in the past 12 months (counted by enterprise)
18. Number of months with accounts receivable electricity fee greater than or equal to 300 yuan in the past 12 months (counted by enterprise)
19. Minimum accounts receivable electricity fee in the past 12 months
20. Maximum accounts receivable electricity fee in the past 12 months
21. Average accounts receivable electricity fee in the past 3 months
22. Average accounts receivable electricity fee in the past 6 months
23. Average accounts receivable electricity fee in the past 9 months
24. Average accounts receivable electricity fee in the past 12 months
25. Average accounts receivable electricity fee in the past 24 months
26. Industry level of accounts receivable electricity fee in the past 3 months: Average accounts receivable electricity fee of the enterprise in the past 3 months / Average accounts receivable electricity fee of the industry where the enterprise is located in the past 3 months
27. Industry level of accounts receivable electricity fee in the past 6 months: Average accounts receivable electricity fee of the enterprise in the past 6 months / Average accounts receivable electricity fee of the industry where the enterprise is located in the past 6 months
28. Industry level of accounts receivable electricity fee in the past 9 months: Average accounts receivable electricity fee of the enterprise in the past 9 months / Average accounts receivable electricity fee of the industry where the enterprise is located in the past 9 months
29. Industry level of accounts receivable electricity fee in the past 12 months: Average accounts receivable electricity fee of the enterprise in the past 12 months / Average accounts receivable electricity fee of the industry where the enterprise is located in the past 12 months
30. Industry level of accounts receivable electricity fee in the past 24 months: Average accounts receivable electricity fee of the enterprise in the past 24 months / Average accounts receivable electricity fee of the industry where the enterprise is located in the past 24 months
31. Fluctuation of accounts receivable electricity fee in the past 3 months: Year-on-year change of accounts receivable electricity fee in the past 3 months
32. Fluctuation of accounts receivable electricity fee in the past 6 months: Year-on-year change of accounts receivable electricity fee in the past 6 months
33. Fluctuation of accounts receivable electricity fee in the past 9 months: Year-on-year change of accounts receivable electricity fee in the past 9 months
34. Fluctuation of accounts receivable electricity fee in the past 12 months: Year-on-year change of accounts receivable electricity fee in the past 12 months
35. Fluctuation of accounts receivable electricity fee in the past 24 months: Year-on-year change of accounts receivable electricity fee in the past 24 months
36. Growth trend of accounts receivable electricity fee in the past 3 months: Month-on-month change of accounts receivable electricity fee in the past 3 months
A total of 180 indicators' processing logic are covered in the above rules.

搜集汇总

背景与挑战

背景概述

该数据集是电力行业的企业数据，包含180个指标，用于构建企业数字风控模型，支持金融行业的风险管理服务。数据规模为500条，每月更新。