First, give conclusions that can be used for decision-making
The smart reconciliations contain at least data acquisition, standardization, candidate matching, rule judgement, differential classification, manual review and result writing back. Each layer has to be independently supported. Automatic matching is high, but the error matches are hidden, and the risk is greater, so serious errors should be set at a very low tolerance. Test data should cover one-on-one, one-on-one, one-one, partial amount, over-period, repetition, refund, missing and abnormal currency. Manual correction should go into a subsequent return sample, but the model cannot be subjected to an unrecognized automatic learning error.
What conditions need to be identified before judgement is made?
The same question may have different answers under different business, data and project phases. It is suggested that the following conditions be checked and that the common findings on the web be incorporated into their own projects.
Suggested order of advance
First, we'll be clear about the target and the border.
Defines the calibration, difference and serious error.
Validation Key Dependence
The freeze contains a collection of independents of normal anomalies.
Development of assessable outcomes
Check item by item for matching rule permissions to write back and log.
Make sure you decide the next step with the real results.
Exercise interface failed, repeated, suspended and restored.
How do you understand it in the actual business?
A bank refund corresponds to three receivables, and the system generates candidate allocations by client, amount and time, but is manually confirmed because of discount differences. The financial confirmation is then returned to the ERP and the candidate, rule, change and final certificate is maintained.
The easiest pit to step on.
Only publish automation rates without statistical error matching
All samples seen during development are accepted and accepted
Reruns generate duplicate vouchers or write-offs
How should we end up receiving and confirming?
Delivery should include receipt and inspection of data versions, matching and discrepancies, serious errors, manual corrections, authority, performance, interfaces, reporting of recovery, etc., and certification of re-operation and review by enterprise personnel.
When preparing to communicate with suppliers or internal teams, it is recommended that current processes, representative samples, existing systems, planning time and budget levels be brought. First, the unknown items are clearly marked, and then the decision is made to use diagnostics, PoC, fixed-range projects or ongoing research and development, which is usually more reliable than a direct demand for a price and duration without borders.