Forecasting and risk models that feed operations
Time-series forecasting for retail checkout volumes and sales trends, plus credit scoring and risk models for banks and large e-commerce platforms — wired into real planning systems.
Challenge
Forecasting and risk models earn their keep only when they feed real decisions. A model that scores well offline but never reaches the planning database — or that drifts unnoticed — creates more risk than value. The work is in the pipeline and the deployment, not just the model.
Approach
We built time-series forecasting for retail checkout volumes and sales trends that flows directly into operational planning, alongside credit scoring and risk models for banks and large e-commerce platforms, and bond-default prediction for financial markets. Each model is evaluated against the operational decision it supports and monitored for drift after deployment.
System design
- Time-series forecasting feeding operational planning databases
- Credit scoring, risk, and bond-default models for finance and commerce
- Reproducible feature pipelines and training infrastructure
- Drift monitoring tied to the decisions the models inform
What we delivered
- Forecasts integrated into operational planning
- Risk and scoring models deployed into real workflows
- Monitored pipelines that surface drift before it bites
- Evaluation framed around business decisions, not just metrics
Why it mattered
Applied ML delivers value at the point of decision. By engineering the pipeline, deployment, and monitoring around the model, these systems inform operations every day — and keep informing them as conditions change.
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