An important element for advanced accounting automation solutions is the ability to add human intelligence to continually improving processes. Take, for example, automating high-volume reconciliations.
Transaction Matching is best used to replace the human endeavor of ticking and tying items in applications with high transaction volumes. These can range from physical inventory counts and comparisons to credit card-to-bank-to-GL applications, from intercompany AR and AP or income and expenses to the task of matching time cards to contractor invoices.
Putting humans to work ticking and tying these sorts of transactions is a waste of energy, not to mention labor costs. But worse is the waste of time, coming as it typically does, at the close of the accounting period.
A Diminishing Workload
At a high level, automated matching is integrated with the solution’s account reconciliation module to automatically check each reconciliation as it occurs.
In a Continuous Accounting scenario, where reconciliations are performed on a daily basis, the matching also happens on a daily basis. This greatly diminishes the human workload at the and of the period. What’s more, it gives the accounting staff plenty of time to follow up on exceptions, making corrections throughout the period.
The benefits are obvious. Not only does automated matching save time at period end, it adds significantly to the quality of the reporting by avoiding errors that might be made in haste or under duress.
But at the transaction level, where the matching takes place, it’s a telling story of how human wisdom can add value to help already-intelligent automated processes grow even smarter over time.
Take, for example, the combination of reason codes and a rules engine that’s extremely robust and eminently flexible.
The Matching Engine
Robustness and flexibility come from the engine’s ability to perform any type of matching: one-to-one, one-to-many, many-to-one or many-to-many.
The engine also permits dynamic matching that will process all combinations of transactions in a selected group, automatically hunting down any that match. Dynamic grouping is a powerful tool since a single group could include 100,000 transactions.
Any number of single or multiple rules can be applied, and rules can be automatic or adjusted to “suggest” matches for review or validation. Automatic rules can prioritize matching performance, with the most stringent (“amount and dates equal”) running before the less-precise rules, where dates or amounts may be allowed to vary.
Suggested rules run from least-stringent to most-stringent, with each suggested pass rule able to dissolve previously created suggested matches as the matches grow more refined.
The Reason Codes
Any unmatched or open items require exception handling. Each event generates a detailed description of the transaction that includes a field for creating a reason code to identify the reason for the mismatch. A reason code could be anything from “Amount variance: 10$ difference, while we only allow one” to “Reference number missing.”
This makes it easy for the accountant to categorize and classify mismatches, and creates an analytical tool that can be applied to improve the matching process over time.
Like other elements of BlackLine automation, it starts out as a reactive exercise, and over time, becomes a proactive contributor in an ever-improving Continuous Accounting landscape.
Read this blog to learn more about how modernizing your manual processes significantly increases productivity, boosts morale, and reduces risk.