BERT finetuning - call center transcripts - named entity recognition of credit card / account number

I am trying to finetune BERT for named entity recognition based on annotated data of call center transcript.

This is a dummy call between 2 agents -

Prabhat - Hello Neeraj, How are you Neeraj - I am good, Thanks, How are you Prabhat - I am good as well Neeraj - For HiHi Phones, we would like to offer 10% discount Prabhat- I am interested Neeraj - Could you share your credit card number Prabhat - Sure one two four Neeraj - Yes Prabhat - five three.. no two.. five and nine Neeraj - Okay, what's next Prabhat - six two Neeraj - six and then two Prabhat - twenty five Neeraj - So it is five, two, five Prabhat - Yes Neeraj - nine, six, two, two and five. Prabhat - Yes Neeraj - Thanks for sharing your credit card Prabhat - Do you need anything else Neeraj - Yes, what's your customer Id Prabhat - It is five, two Neeraj - Okay Prabhat - three, nine Neeraj - sure, Thanks. I will place the order for you

we want to detect the numbers after the word 'credit' as B-CREDIT/I-CREDIT. but the numbers after the word customer id should go under B-CUSTOMER/I-CUSTOMER.

after training a bert uncases model, getting a very random output of numbers.

needed a clarity on 1 detail - named entity recognition happens on entity level, but does BERT keeps context with it (for example - word credit appeared before the number so assign the number to B-CREDIT/I-CREDIT. and if number is occuring after ACCOUNT then number should be detected as B-ACCOUNT/I-ACCOUNT?