A vector database is a type of database that stores information as vectors, numerical representations that capture the meaning of text, numbers, or documents rather than their exact characters. It lets software find items similar in meaning, not just identical in spelling, by measuring how close their vectors are.
In finance AI, this capability is what makes intelligent matching possible. Traditional reconciliation relies on exact rules: same amount, same reference. But real-world data is messy: a supplier name abbreviated, a reference omitted, an amount split across lines. Exact-match rules fail on exactly these cases, leaving humans to sort the leftovers.
Vector-based comparison handles them. By representing each transaction and document as a vector, the system can recognize that two differently-worded entries describe the same thing, the way a human would, but across thousands of lines at once.
This is the principle behind Phacet's AI Match. The agent that reconciles bank transactions and detects unmatched flows and the agent that reconciles payment gateway, bank, and ERP flows use semantic, vector-based matching to pair items that exact-amount rules miss, then expose the reasoning for each pairing through a native audit trail.
A vector database lets AI compare by meaning, not just by characters. In finance, that is what turns reconciliation from a rules-and-leftovers chore into a match that understands the data.
