Risk Management and Credit Evaluation System for DHgate Foreign Trade Order Data in Spreadsheets-ACBuy spreadsheet-The best and new spreadsheet

AirPods 3-0102

$7.53

Jordan 4 Retro (40 color)-0001

$34.25

casabianca Fashion Shirts Casual Suits (40 styles)-1146

$25.89

Prada Casual Sneakers (22 color)-0015

$38.08

New Balance 9060 series (20 style)-0011

$38.36

2024 New Jordan 4 (12 colors)-0010

$38.36

Dior B30 Series (20 style)-0416

$45.21

OG Balenciaga speed-1047

$44.93

dior b22-b23-0027

$45.21

dunk low （30 color）-0003

$19.18

AMIRI MA-1-0024

$71.23

Denim tears DT Loose foamed kapok casual men's and women's T-shirt suit -0930

$9.32

Rolex watches (3 qualities 14 styles)-0396

$52.05

Balenciaga Triples (7 styles) -0515

$82.19

GOYARD Fashion bags-0182

$9.45

Louis Vuitton LV belt（36 style）-0246

$11.64

Amiri Cap（11 styles）-0662

$13.56

Louis Vuitton LV bucket bag（10 styles）-0187

$54.66

Louis Vuitton fashion bag-0185

$47.95

24-25 Football Jerseys (40+ Colors)-0948

$9.32

By [Your Name] | [Date]

Introduction

In the dynamic landscape of global e-commerce, managing foreign trade risks effectively is crucial for platforms like DHgate. By leveraging spreadsheet tools (e.g., Excel, Google Sheets) to organize and analyze transactional data, businesses can build robust risk assessment modelscredit evaluation frameworks. This article outlines a systematic approach to mitigate trade risks through data-driven decision-making.

1. Data Structuring in Spreadsheets

1.1 Key Data Fields

Order Metrics:
Client Information:
Payment Methods:
Logistics Data:

Tip: Use pivot tables to segment data by client/region for pattern analysis.

1.2 Automated Data Validation

Implement Data ValidationVLOOKUP =IF(AND(Amount>5000,PaymentMethod="TT"), "High Risk", "Review")

2. Building the Risk Scoring Model 2.1 Weighted Scoring Factor Weight Indicators Payment History 30% Late payments, chargebacks Order Size 25% Transactions >$10k score higher risk Country Risk 20% Sanctioned countries = +50 points Client Tenure 15% New clients (<6 mos) = +30 Total Score Threshold: 2.2 Model Implementation Use SUMIFS =([Payment_Score]*0.3) + ([Order_Size]*0.25) + ... Conditional formatting highlights high-risk orders in red automatically. 3. Dynamic Credit Assessment Tiered Credit Limits: AA (Score<20): $50k limit | C (Score>75): Prepaid only Escalation Protocols: Scores >60 trigger managerial review before shipment. 4. Operational Impact Case study: After implementation, DHgate Partner X - 32% reduction in unpaid invoices - 89% faster risk evaluation (8hrs → under 1hr) - Dynamic adjustment of credit terms for 2,300+ clients Conclusion Spreadsheets remain a powerful yet underutilized tool for trade compliance. By systematically integrating risk scoring algorithmsautomated alerts, merchants can transform raw DHgate export data into actionable risk intelligence—without expensive software. Future enhancements could incorporate API connections to live market data.

Factor	Weight	Indicators
Payment History	30%	Late payments, chargebacks
Order Size	25%	Transactions >$10k score higher risk
Country Risk	20%	Sanctioned countries = +50 points
Client Tenure	15%	New clients (<6 mos) = +30
Total Score Threshold:

```