RiskFlow Engine v1.0

数桥风险引擎 v1.0

Data Bridge Consulting
Demo Portal

RiskFlow Engine v1.0 — Demo Portal

This sandbox showcases how RiskFlow generates forward-looking risk predictions across credit portfolios, healthcare patients and supplier networks, using semi-synthetic data.

For a full overview of our services, visit the main site or contact us for a private pilot.

Data Bridge

Try a Demo

Explore three interactive scenarios powered by the RiskFlow Engine. All data is semi-synthetic and anonymised, used solely for demonstration — it does not represent any real individuals, firms, or suppliers. You can review the model setup, variable definitions and class balance, and then run RiskFlow with your chosen prediction horizon.

📉 Credit Risk — Downgrade Risk Detection

Detect early signs of credit deterioration using market movements and firm fundamentals. Review risk drivers at different horizons and see how predicted downgrade risk translates into portfolio outcomes.

🩺 Healthcare — Early Tumour Risk (Synthetic)

Visualise how a patient’s risk evolves across clinical visits and identify early warning windows before diagnosis. Explore how key clinical indicators contribute to the individual risk trajectory.

🚚 Supply Chain — Supplier Default / Disruption Risk

Flag vulnerable suppliers early using delivery performance and financial stress signals. Examine short-horizon risk drivers, ROC performance, and dynamic high-risk supplier lists to support proactive planning.

Bring Your Own Data (BYOD)

Use your own data to explore how RiskFlow models real-world risk processes. The BYOD workflow focuses on validating event definitions, signal dynamics, and decision-relevant outputs before any full-scale PoC or deployment.

✔ Designed for small samples (e.g. ≤ 50k rows)
✔ No personal identifiers or sensitive fields
✔ Used solely for exploratory evaluation

Open BYOD

RiskFlow Technical Roadmap

v1.0 — Probabilistic Modelling
Statistical & probabilistic foundations; core event probability estimation.

v2.1 — ML Enhancement
Non-linear ML models improve accuracy, stability and adaptability.

v2.2 — Dynamic Risk Factors
Time-varying risk factors + uncertainty analysis for deeper structural insights.

v3.0 — Multi-Model Fusion
Fusion frameworks tailored to credit, supply chain and healthcare prediction tasks.

v4.0 — Adaptive Intelligence
Self-updating engine with real-time data feeds and automatic re-tuning.

Enterprise Collaboration

We help organisations adopt RiskFlow through lightweight trials, focused PoCs, and full-scale deployments.

① Guided Trial (2–3 weeks)

Explore RiskFlow with curated sample data and provide structured feedback.

② Focused PoC

Validate RiskFlow on your data with prediction outputs and concise insights.

③ Full Deployment

Custom models, private deployment and integrated data pipelines.