Financial data you can reconcile.
In lending and payments the reporting has to reconcile, and the models have to be defensible on historical cases. iLeaf builds the warehouse and semantic layer so portfolio, collections and risk figures agree across teams, then adds forecasting evaluated against held-out outcomes rather than backfitted to look good.
What this looks like in financial services
- Portfolio reporting
- A semantic layer so arrears, exposure and yield mean the same thing in every report.
- Risk & churn models
- Forecasting trained on your history and validated on outcomes the model never saw.
- Collections analytics
- Prioritisation by likelihood of resolution rather than simply by balance outstanding.
- Regulatory reporting pipelines
- Reproducible extracts with lineage, so a figure can be traced back to source records.
Questions we get asked
How do you validate a risk model?
On held-out historical data with known outcomes, replaying decisions as they would have been made at the time. Backtesting against data the model was trained on tells you nothing useful, so we separate those strictly and report both.
Can you work with our existing warehouse?
Yes, and usually that is the faster path. We assess what you have, fix the trust problems first — reconciliation and quality tests — and add modelling on top rather than proposing a migration you did not ask for.
Data & Analytics for financial services— let’s talk.
Tell us what you are running and what it needs to do next. We will tell you honestly whether we are the right team for it.