Transaction reconciliation, particularly in the context of financial transactions, is a complex problem that is well-suited for AI. The task involves matching business payments to bank statement transactions, which can result in numerous possible solutions. This problem can be viewed as a variant of the Knapsack problem in Computer Science. While most reconciliation processes opt for equal totals rather than 1:1 matching due to the sheer number of possibilities, reconciliation engines like the one offered by Modern Treasury are capable of handling this task with 100% confidence and repeatability. AI plays a crucial role in solving such complex reconciliation challenges by combining deterministic methods with probabilistic models trained on real-world observations. The responsible use of AI involves integrating it into workflows overseen by humans. As the era of real-time payments unfolds, the reconciliation challenge becomes more complex due to the need for speed and the changing nature of transaction data. The implementation of software that can leverage additional remittance information provided by new payment rails is essential to avoid overwhelming numbers of possible solutions. Financial workflows provide an ideal environment to responsibly implement AI solutions due to the structured nature of the data and the vast solution space.
https://www.moderntreasury.com/journal/reconciliation-is-a-knapsack-problem