BlackLine matching: Pass rules and evaluation criteria

BlackLine transaction matching enables users to automatically match and reconcile transactions efficiently and effectively. That means less time spent on manual reconciliation, relying instead on matching logic to shorten the period-end close. 

Here are some of its other key features: 

  • Ability to match data sources based on pre-programmed rules—allowing team members to focus only on the exceptions 
  • Customization of match types 
  • Easily defined workflows that route exceptions to the appropriate individual 
  • Identifiable exceptions in any matching use case, regardless of file format, data quality, or volume 
  • Seamless integration with BlackLine’s reconciliation module, allowing for quick creation of items to be reconciled 

Let’s talk pass rules 

Pass rules are your key to automatically match transactions together using fields contained in the match sets’ data sources. Without them, you can only match manually—and once these rules are in place, preparers, approvers, and executives can view them, but not edit them. There are two types: 

  • Automatic rules: Think of these as hard and fast rules that the system will pass or fail; only to be used in situations where there is high confidence in the accuracy of the match (e.g., the check number, account, and amounts match). 
  • Suggested rules: Potential matches that are sent to the preparer for additional review/confirmation; to be used when there are multiple records on a data source that might fulfill the pass rule criteria and the odds of an incorrect match are higher (e.g., the amounts match, but dates differ, or the check number was read incorrectly by the bank). 

These rules can also be customized and manipulated to accommodate data prior to matching—for example, filters can be applied to both match sets and pass rules, and data can be grouped by data fields to allow for one-to-one, one-to-many, or many-to-many matching. 

Matching use case evaluation criteria 

If you’re trying to determine if matching is appropriate for a particular use case, here are the things to keep in mind: 

  1. Frequency of reconciliation: Does this reconciliation occur daily, weekly, monthly, quarterly, or annually? 
  2. Volume of transactions: Which reconciliations have the highest average # of transactions/month? Categorize as low (0-100), medium (101-1,000), high (1,001+). 
  3. Granularity of transactions: Are the reconciliations compared to summarized GL balances, to transactional bank balances, and/or supplemented by system detail (e.g. POS system reports are not used in the bank reconciliation)? 
  4. Number of data sources: What data sources are utilized in the reconciliation process? What format of files is available (BAI/T940)? What manipulation is required to revise raw data to perform the reconciliation? 
  5. Level of effort for current reconciliation process: How many FTE are typically used in the current reconciliation process? 
  6. Complexity of matching: Are matching processors one-to-one, one-to-many, or many-to-many? What are the key data fields used to perform the matching process?  

For a more detailed look at pass rules or matching use case evaluation criteria, reach out for a demo. And to learn more ways to improve efficiency in BlackLine, check out our time-to-value analysis of various improvement opportunities. 

Categories: Financial close, BlackLine
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