Why AI in finance should be data-layer first
The model is the easy part. The reason finance AI projects stall is almost always the data beneath them. Here is how we approach it.
Most AI initiatives in finance begin at the wrong end. A team sees a compelling demo, picks a model, and starts wiring it into a workflow. Then they discover the data it depends on is fragmented, full of point-in-time errors, and impossible to trace back to a source.
In finance that is not a rough edge, it is a blocker. An answer you cannot trace is an answer you cannot put in front of a portfolio manager or an auditor. And a model standing on unreliable data does not just give wrong answers, it gives wrong answers confidently.
Ground the data before the model
We invert the usual order. Before any model selection, the data layer has to be query-ready and correct:
- Point-in-time correctness, so a backtest cannot quietly see prices that did not exist yet
- Ingestion from market, transactional, filing and alternative-data sources
- Lineage and validation, so every value can be traced to where it came from
- Entity resolution, so “the same company” really is the same company across feeds
- Retrieval setup: vector and warehouse stores designed for the questions you will actually ask
None of this is glamorous. It is also the difference between a system that works in the demo and one that works at quarter-end.
Then prove it with evaluation
Only once the data is solid do we prototype, and every prototype ships with an evaluation set. That turns “it looks good” into a number you can argue about. If the system cannot beat the process it is replacing on that number, far better to know in week three than after six months of build.
If your AI roadmap starts with choosing a model, it is starting in the wrong place. Start with the data.