个人信贷宝数据

1. 数据适用范围: 衢州市开化县。
2. 数据内容: 通过统计居民的不动产权信息、住户信息、社保等数据集，来分析居民的信用度及偿贷能力。
3. 数据应用场景: 利用公共数据授权运营平台进行数据分析和挖掘，形成个人信贷模型分值，这为金融机构提供了贷前业务授信额度的测算依据，同时支持贷中管理，帮助金融机构评估借款人还款情况的风险，从而实现金融机构贷款业务的全流程管理闭环。个人信贷宝的数据基于开化县公共数据授权，利用“房产套数”、“房产坐落”等公共数据集参与运算得出。
模型使用经过平台标准化预处理的数据，结合历史数据，采用机器学习建立信用评分模型。
在评估过程中，模型首先结合开化当地信息，计算出关键影响因子，这些因子与个人信息、不动产权证、房产坐落（地段）等信息相结合。再利用梯度提升决策树（GBDT）和随机森林（RF）等复杂模型来捕捉房产价值和个人财务稳定性之间关系的非线性方面，并将这些模型的输出作为特征输入到线性回归模型中，以建立更全面的预测模型。同时，使用K-最近邻算法（KNN）对老旧或者残缺的数据进行数据更正、插补，以确保数据集的完整性和预测的准确性。
最终，个人信贷宝评估模型输出个人信用评分，帮助金融机构决策贷款额度和利率，并提供客户建议，有利于优化个人财务管理、增加信用额度。
数据的汇集、计算和存储均在浙江省公共数据授权运营平台上完成，遵循“原始数据不出域、数据可用不可见”的要求，保护个人信息、商业秘密和公共安全。
目前，每日数据处理量约为50万条。

1. Scope of Application: Kaihua County, Quzhou City.
2. Data Content: Analyze residents' credit ratings and loan repayment capabilities by leveraging datasets including real estate property right information, household information and social security records of residents.
3. Data Application Scenarios: Conduct data analysis and mining through the Zhejiang Public Data Authorized Operation Platform to generate personal credit scoring model scores, which serve as the basis for financial institutions to calculate pre-loan credit limits. Meanwhile, it supports in-loan management, helping financial institutions assess the repayment risk of borrowers, thus realizing the closed-loop of full-process management for financial institutions' loan businesses.
The data of the Personal Credit Loan Tool is based on the authorized public data of Kaihua County, and is derived from operations using public datasets such as "number of real estate properties" and "property location".
The model uses standardized preprocessed data from the platform, combined with historical data, to establish a credit scoring model via machine learning.
During the evaluation process, the model first calculates key influencing factors combined with local information of Kaihua County, which are integrated with personal information, real estate ownership certificates, property location and other information. Then, complex models such as Gradient Boosting Decision Tree (GBDT) and Random Forest (RF) are used to capture the nonlinear aspects of the relationship between property value and personal financial stability, and the outputs of these models are fed as features into the linear regression model to build a more comprehensive predictive model. Meanwhile, the K-Nearest Neighbors (KNN) algorithm is employed to perform data correction and imputation for outdated or incomplete data to ensure the integrity of the dataset and the accuracy of predictive results.
Finally, the Personal Credit Loan Tool evaluation model outputs personal credit scores, helping financial institutions decide loan amounts and interest rates, and providing customer suggestions, which is conducive to optimizing personal financial management and increasing credit limits.
The collection, computation and storage of all data are completed on the Zhejiang Public Data Authorized Operation Platform, following the principle of "raw data not leaving the designated domain, data accessible but not visible" to protect personal information, commercial secrets and public security.
Currently, the daily data processing volume is approximately 500,000 records.

搜集汇总

数据集介绍

特点

该数据集名为'个人信贷宝数据'，属于金融业，数据来源于公共数据，规模为502条，每日更新。数据集通过分析居民的信用度及偿贷能力，为金融机构提供贷前业务授信额度的测算依据，支持贷中管理。