End‑to‑End Group Risk Perception Modeling: From Requirement Mining to Deployment

Introduction – The session, led by Dr. Zeng Junwei from JD, uses group risk perception as a case study to illustrate the full risk‑modeling pipeline, from requirement mining through data mining, modeling, and final deployment.

1. Business Requirement Mining

Goal: Identify key variables linked to success metrics (e.g., bad‑debt rate, order volume) and define them as model targets.

Identify existing or needed data sources.

2. Implementation Methods

Define objectives with stakeholders, translate business problems into modeling problems.

Identify and collect data sources that can answer those questions.

3. Deliverables

Project requirement document (scenario definitions, KPI targets).

Data source inventory and data‑pipeline design.

4. Business Demands and Pain Points – Emphasize the four‑character mantra: "more, faster, more accurate, cheaper" (high recall, real‑time response, high AUC, low operational cost).

5. Requirement Analysis – Outline risk‑state scenarios (small‑scale vs. large‑scale loss) and black‑gray‑industry characteristics (profit‑driven, group behavior).

6. Data Acquisition & Understanding

Data sources: static (registration, login) and dynamic (browsing, ordering, coupon‑redeeming) profiles.

Goal: Build clean, high‑quality datasets and data pipelines for continuous refresh.

7. Modeling

Feature engineering: processing numeric, text, image features and selecting relevant ones.

Model training: parameter tuning, model management, cross‑validation, A/B testing.

Model evaluation: monitor performance in production.

8. Model Selection Paths – Provide Sklearn and Microsoft model‑selection flowcharts to guide algorithm choice (classification, clustering, supervised/unsupervised, deep learning, reinforcement learning).

9. Group Risk Identification Modeling

Identify user types (normal, suspected scalper, crowd‑sourced fraud).

Detect collective behavior patterns across time, space, and actions.

Output risk timestamps, locations, and behavior clusters.

10. Static & Dynamic Feature Recognition

Static: relationship graphs, community detection (Louvain, Fast Greedy, etc.).

Dynamic: clustering with distance metrics, visualized via algorithm comparison charts.

11. Deployment

Deploy models and data pipelines as API services.

Key monitoring items: scoring and effectiveness dashboards.

Deliverables: monitoring reports, model documentation, data‑pipeline docs.

12. User Application

Complete project hand‑over, verify functionality and accuracy.

Provide final delivery report and integration architecture (rule engine, model engine, decision engine).

Design multi‑level risk‑control strategies (standard, customized, scenario‑based, elastic).

Overall, the material offers a step‑by‑step guide for building, evaluating, and operationalizing group risk perception models in e‑commerce environments.