Hi @aqiff,
This problem falls within the field of computer science, linguistics and statistics and ML skill or NLP to start of with is overkill.
Specifically, you are looking to calculate an edit distance between two strings. Based on this distance you can infer if the given string matches your required string.
You can read this thread for some background and two more approaches: Compare Names
I have updated my azure function, you can use it for development but don’t use it on a production environment.
Either way, (using fuzzy matching as @Nithinkrishna suggested) or calculating edit distances or using the approach suggested by @kumar.varun2 is not sufficient in your case.
You will also need a mapping to the correct value string (correct Bank Name). So let’s say your match is higher than 80% then you will have to use a mapper which can return what DES Bank means in your required names list / array or / dictionary.
This way if you in the future have additional banks, just add to your banks list / array / dictionary.
Variability in your data will make this string comparison quite a challenge and do expect to have many exceptions.
On the contrary, if you use any ML skill this variability is good but then you need sufficient volume of cases and the velocity (how soon you get new data and retrain the Model) will dictate the performance of such matches using ML / NLP.