成都市外贸企业综合价值评估数据

我司基于外贸企业的特征，结合数据的可获得性和有效性，从外贸企业的基本资质、企业性质、交易行为、合规信用等多个维度构建外贸企业的综合价值评估体系，并完成数据收集、数据清洗、特征衍生、模型构建、模型验证全过程，形成涵盖成都市外贸企业近一年进出口交易总额、交易笔数等多维度的综合价值评分，能有效帮助金融机构准确全面地评价外贸企业的优质程度和动态变化，筛选出优质的外贸企业并精准匹配相应的金融服务，辅助金融机构在外贸结算和授信业务等业务场景下的高效决策。外贸企业综合价值评分 = ∑ Si * Xi ，其中Si是指标相应的权重系数，Xi是评价指标，i=1,2,3,……,28，28个指标包括“近一年进出口交易总金额（美元）”“近一年最大进出口客户贸易金额占比”等，指标经离散化与分数映射后参与计算。该模型结合专家经验和机器学习算法得出，专家主要基于外贸业务特点、行业市场趋势与业务运营实践，帮助筛选关键指标并初步设定权重范围，然后通过梯度提升树集成算法进一步筛选指标并对初始权重进行优化。

Our company, based on the characteristics of foreign trade enterprises and combined with data availability and validity, constructed a comprehensive value evaluation system for foreign trade enterprises from multiple dimensions including basic qualifications, enterprise nature, transaction behavior, compliance and credit, and completed the entire process including data collection, data cleaning, feature derivation, model construction and model validation, thus generating a comprehensive value score covering multiple dimensions such as the total import and export transaction amount and number of transactions of Chengdu's foreign trade enterprises in the past year. This system can effectively help financial institutions accurately and comprehensively evaluate the quality and dynamic changes of foreign trade enterprises, screen high-quality foreign trade enterprises and accurately match corresponding financial services, and assist financial institutions in making efficient decisions in business scenarios such as foreign trade settlement and credit granting. The comprehensive value score of foreign trade enterprises is calculated as: $ ext{Score} = sum S_i imes X_i$, where $S_i$ is the corresponding weight coefficient of each indicator, $X_i$ is the evaluation indicator, and $i=1,2,3,dots,28$. The 28 indicators include "total import and export transaction amount (USD) in the past year", "proportion of trade amount with the largest import and export customer in the past year", etc. All indicators are discretized and score-mapped before being involved in the calculation. This model is developed by combining expert experience and machine learning algorithms. Experts mainly screen key indicators and initially set the weight range based on the characteristics of foreign trade business, industry market trends and business operation practices, and then further screen indicators and optimize the initial weights through the gradient boosting tree ensemble algorithm.

搜集汇总

背景与挑战

背景概述

该数据集聚焦成都市外贸企业，包含1000条企业数据，每周更新，以Excel格式提供。它通过33个字段（如综合价值评分、进出口交易金额和合规指标）评估企业价值，结合专家经验和机器学习算法构建评分模型，旨在帮助金融机构筛选优质企业并支持金融服务决策。