Cross-bank AML federation
Banks model jointly without moving data — suspicious-path detection accuracy lifts.
Evidence notice: cases, figures and inferred project metadata are unverified scenario illustrations, not established client outcomes, credentials or return promises. Signed contracts define scope, fees, baselines, acceptance and data responsibilities; client business data stays off-chain.
1. Project Overview
- Customer type
- Top-tier Banks / brokers / insurers
- Project duration
- 6–8 weeks
- Tech stack
- Federated learning · Predictive modeling
- Deployment
- Private deployment
2. Business Context & Pain Points
The customer is a Enterprise / Group player in Finance, with the core pain concentrated in "Risk & Compliance". Manual workflows and point tools could not scale or stay consistent, driving up response time, operating cost and compliance risk. This project delivered an end-to-end AI upgrade for that workflow.
3. Solution
Anchored on "Privacy Computing & Compliance Security", Global AI DAO integrated the following capability stack into a single solution:
- Federated learning —— Wired into the critical business path across ingestion, inference and human-in-the-loop collaboration.
- Predictive modeling —— Wired into the critical business path across ingestion, inference and human-in-the-loop collaboration.
- Deployment: Private deployment, balancing data compliance, performance SLA and operability.
4. Quantified Outcomes (Before vs After)
| Metric | Before | After | Delta |
|---|---|---|---|
| suspicious-transaction precision | 100% | 118% | +18pp |
Source metric: +18pp suspicious-transaction precision.
5. Specs & Fit
| Federation protocol | Horizontal / vertical / transfer FL |
| Privacy tech | MPC / DP / HE (optional) |
| Chips | Ascend / Cambricon / NVIDIA |
| OS | Kylin / UOS / Ubuntu |
| Certifications | MLPS-3 / cryptographic eval / ISO 27001 |