If the running balance on your Arvest Bank statement does not add up row by row, that is exactly the kind of mismatch Convert·Into is built to catch rather than pass through silently. Every statement document goes through a running-balance check that compares the extracted transaction log against the closing figure printed on the original document. The engine reads the balance column line by line, reconstructing the arithmetic as it goes: each opening balance plus the transaction amount must equal the ending balance for that row, and the chain must arrive at the printed closing number without a gap. A single misread digit, a transposed amount, or a skipped row will break that chain, and the row is flagged for review rather than silently accepted. Because the check operates row by row, the rest of the file remains intact: the correctly read transactions are ready to export while the problem is still marked, so you can fix one value and carry on instead of starting over.
If a transaction description on your Arvest Bank statement is long enough to wrap across two or three lines, it still needs to land in a single row once converted. Long transaction descriptions that wrap across several lines in the original document are a common source of mangled data. The extraction engine recognises when a description continues on the following line by analysing the column structure of the table, not by guessing where the text ends. When a narrative runs three or four lines deep while the adjacent amount and date columns hold a single value, the engine stitches those lines into one clean description cell and aligns them with the correct row. The result is a transaction table where every amount sits beside its full description, without fragments pushed into the wrong column or trailing lines that look like separate entries.
When your Arvest Bank statement arrives as a scan or a photo of the page rather than a clean export, the numbers on it are just pixels until they are extracted. Scanned and photographed statements introduce a different set of challenges: uneven lighting, skewed pages, and faint print all push optical character recognition to its limits. The engine handles these files by running the same extraction and verification pipeline it applies to clean electronic statements. OCR reads the text, and the running-balance check catches anything the scan misread before it reaches the export. You get the same column mapping, the same reconciliation flagging, and the same export options. The only difference is that you uploaded an image instead of a file generated by the bank's own system, and the engine did the rest.