Every fraud product on the market now claims artificial intelligence. For a bank or mobile money operator evaluating vendors, the label is useless; what matters is whether the system learns from your transaction patterns or simply ships someone else's rules.
The questions that cut through
Ask a vendor four things. What data does the model train on, yours or a global pool, and how often does it retrain? What is the measured false-positive rate at the detection threshold they are quoting, on traffic like yours? Can an analyst see why a transaction scored high, in terms a regulator would accept? And what happens in the first ninety days, before the model has seen your normal?
A vendor who answers all four plainly is selling a model. A vendor who answers with the word "proprietary" is usually selling rules.
When a model earns its keep
Machine learning pays for itself where rules degrade: velocity attacks that adapt to your thresholds, mule networks that look like ordinary customers individually, and social-engineering fraud where each transaction is technically legitimate. If your loss profile is dominated by simple, well-understood patterns, a disciplined rules engine with good case tooling may outperform a model at a fraction of the cost, and you should not be embarrassed to buy one.
The integration reality
The model is a quarter of the project. The rest is data plumbing from your core systems, case management your fraud team will actually use, and the governance to explain scores to auditors. Budget accordingly, and pilot on three months of your own historical data before signing anything multi-year: a vendor confident in their model will accept the test, and the ones who refuse have answered your question.