互联网及金融风控行业可信度指数模型

1.数据采集与验证：协议获取用户个人手机舆情、银行卡舆情及典型犯罪记录相关数据，进行清洗操作去除重复和无效内容，使用自研模型通过算法规则进行真实性验证。 2.数据处理与加工：算法模型根据各类犯罪记录数据的不同权重，结合数据真实性评分计算各项判断数据的得分值，并用户个人风险评价综合分数作为分级判断依据，并根据分值划分等级设定得到最终个人风险评级结果。 3.数据存储安全与反馈优化：根据用户及市场反馈，优化数据采集、清晰、评分等流程，提高数据有效性与评分准确性。涉及个人敏感信息使用md5+aes加密，确保数据无法被破解，有效保证数据安全性。1. Data Collection and Verification: Collect data concerning users' personal mobile public sentiment, bank card-related public sentiment and typical criminal records in compliance with relevant protocols, conduct data cleaning to eliminate duplicates and invalid content, and perform authenticity verification via algorithmic rules using a self-developed model. 2. Data Processing and Refinement: The algorithm model calculates the score values of various judgment data based on the distinct weights of different types of criminal record data and the data authenticity scores. The comprehensive score of the user's personal risk assessment is taken as the basis for hierarchical judgment, and the final personal risk rating result is obtained by dividing into levels according to the calculated scores. 3. Data Storage Security and Feedback Optimization: Optimize processes including data collection, cleaning, scoring and others based on user and market feedback, so as to improve data validity and scoring accuracy. Sensitive personal information is encrypted using MD5 + AES to ensure that the data cannot be cracked, effectively guaranteeing data security.

搜集汇总

背景与挑战

背景概述

该数据集是一个互联网及金融风控行业的可信度指数模型，数据规模为29.5GB，每月更新，通过个人身份信息、手机号和银行卡记录等数据训练，用于评估个人风险等级。它主要应用于金融银行业、互联网平台和企业经营场景，辅助识别高风险客户、清理不良账号以及筛查用工风险。模型采用数据清洗、真实性验证和加密存储等算法流程，确保数据安全性和评分准确性。