How do data scientists test the claimed accuracy of a model?
There will already be a historical dataset of Fraudulent transactions (E.g.: If we take Credit Card as an example, all transactions that were disputed and dispute was accepted). 

Sales team will include pre-sales developers that can help run a subset of such data through model (After various paperwork such as NDA, Legal review that a specific attribute like Gender/ Race can be used for this purpose). This will usually require :


Extracting data from source systems or warehouse or data lake 
Transforming data for the model's input/output format 
Quantitative + Subjective validation by Operations team that handles such transactions


Getting 90% accuracy is pretty straight-forward. Since most of the transactions are not fraudulent, any model that predicts all transactions as genuine will meet this bar. 

So step #3 (validation) will include agreement on appropriate measure for validation of the model (Such as F1 score)