Originally posted to Twitter[1] after seeing @ghuntley's post - below is a method of building a database of SVB account holders using a cleverly crafted Google query:
(121140399 OR 121145145 OR SVBKUS6S) AND 33**** filetype:xls | pdf | doc | txt -site:svb.com
The query[2] pulls back documents containing an SVB routing or swift number + SVB account number.
The name of the business tied to the SVB account number is usually within 100 characters of the account number and typically labeled "beneficiary" or "for credit of" (or some close variation on those themes).
In edge cases where a company name is not found near the account number, the account holder is often the source/author of the document[3].
Google only returns 1000 results for any given query - so retrieving the entire results set would require narrowing each query's scope and then iterating through the targets at a finer granularity.
For example, the current query looks for account numbers matching 33****. Replacing that with 33*NN**, where NN is 01-99 would let you iterate through ranges of account numbers (33*01**, 33*02**, and so on...).
Other approaches include geographically scoping the queries by adding "AND NJ" to queries (and iterating through each two-byte state code) or tacking on partial zip codes, etc...
It takes tweaking - but the goal is to build a query with elements that you can loop over to get your results into buckets of < 1000. Another option would be to use the Bing Web Search API and paging through the json response.
These methods might complement the work you've already done on affectedbysvbornot.com - and help provide a more exhaustive list of companies impacted by the SVB collapse.
1: https://twitter.com/thegrif/status/1634841498876272641