How do data scientists test the claimed accuracy of a model? For example, if you are a data scientist at a company, and a salesperson offers you a pre-trained model that is claimed to have a 90% accuracy of detecting fraudulent transactions. How would you go about verifying this claim?

I would test it on the data that I possess and see what the acc. is for detecting frauds in my data-set, and run a t-test with the sample acc., to see if it is close enough to 90% to make the call that it would detect 90% of all frauds in the entire population.

Let me know if this is the correct way to think about this question.

Thanks
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)