Our Cash Matching Reached Ninety Two Percent by Guessing Which Invoice Customers Meant

Описание и применение

Автоматизация сопоставления банковских переводов с счетами достигла 92 % благодаря правилу «fallback», но привела к ошибочным начислениям. Удалив правило и введя ручную проверку, компания восстановила точность до 79 % автоматических совпадений и менее 1 % ошибок.

Задача

Automating the matching of bank transfer payments to invoices was difficult because payment references were inconsistent, leading to a low initial match rate of about seventy percent.

Результат

The fallback rule temporarily boosted the automatic match rate to 92% but introduced a high error rate. Removing the rule and adding manual review restored accuracy, achieving a 79% automatic match rate with less than 1% wrong matches, improving credit control efficiency.

Рабочий процесс

• 1. Read bank statement each morning. 2. Search payment reference for invoice numbers. 3. Allocate payment to matching invoice. 4. If no reference matches, apply fallback: assign to oldest open invoice with same amount. 5. Audit fallback matches; identify incorrect allocations. 6. Remove fallback rule. 7. For unmatched payments, present candidate invoices to a person for manual review. 8. Report three metrics: automatic matches, manual matches, and wrong matches (sampled).

Доказательства

At first the rate was about seventy percent, because references are messy.

Источник

The most effective rule was a fallback. If no reference matched, take the oldest open invoice for that customer with exactly the same amount.

Источник

When we audited a sample of fallback matches, about one in five had gone to the wrong invoice.

Источник

The automatic rate is now seventy nine percent, and the wrong rate is below one percent.

Источник