Improving the business logic behind cash forecasting.
Finance-domain co-ownership of Group Cash Flow Forecasting with Data Science, supporting multi-country and multi-currency liquidity planning.
A forecast is only as useful as the business logic behind it.
Group Cash Flow Forecasting combines enterprise Finance data, model logic and Treasury use cases across countries and currencies. My role sits on the Finance side of the product: investigate variances, challenge assumptions and translate recurring business behaviour into changes the model can use.
One production rule, improved.
A payroll forecast used the earliest observed payment date. The code was deterministic; the business assumption was the problem. I tested whether recurrence was a better signal and changed the rule to use frequency-based date selection.
| Dimension | Before | After |
|---|---|---|
| Rule | Choose the earliest observed payroll date. | Choose the date that recurs most frequently. |
| Failure mode | A one-off early payment could pull the forecast forward. | The rule follows recurring payroll behaviour rather than the earliest exception. |
| Where it lives | Production business logic in Group Cash Flow Forecasting / Dataiku. | |
| Status | Shipped to production. No public quantified impact claim is made yet. | |
Finance-domain co-owner, not solo model owner.
I own / contribute
- Finance-domain investigations
- Business-logic analysis
- Targeted production enhancements
- Translation between Finance, Treasury and Data Science
Shared / specialist ownership
- Broader Data Science model
- Model architecture and specialist data-science decisions
- Enterprise platform engineering
Technical sophistication does not replace a good business rule.
The useful part of my role is the boundary: understand how Finance actually behaves, then make that logic explicit enough to test and improve.