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The The first module is the enrichment of the data. This can involve cleansing transaction identifiers, extracting important information from a text description field that you can use for the reconciliation, looking up and attaching other data that helps you reconcile, and so on. The other thing that should be done in this module is identifying the records that are likely to be impacted by timing differences and the records that are likely to require more complex matching rules (such as many-to-many matches)

Only after enriching the data can, you move on to the matching. The first thing to do is to use the information from the enrichment working enrichment to separate the data into relevant buckets based on the type of reconciliation you need to do. These buckets include the records that need to be persisted for timing differences and those with more complex matching rules. This step is very important because it separates the easy work from the harder work. For example, you may want to do the easier reconciliations first and get them out of the way to have less noise in the rest of the reconciliation. This will also allow for better reporting and more consistency, particularly if you develop a pattern, for instance, where 70% of records are simple and the remaining 30% are complex. Having that clear separation will make it easier to manage the process in the future.

The result of the matching is a list of variances that need to be investigated. Perhaps these investigations can be automated by bringing in new data sources, such as looking up a list of invoices extracted from the general ledger. However, often these investigations require some human analysis.

Investigation The key point here is to have a clear separation of the data. Make it as easy as possible to know what variances someone needs to investigate and how they can come back and clear that variance once they have found the cause of the issue. It is also important to ensure that variances can be closed off in a controlled and auditable way. Ideally, data should not be overwritten. Instead, there should be an easy way to adjust the data. People think they can’t automate their reconciliation because the work needed to investigate the variances is manual. For example, they are looking through scanned invoices to determine why an error might have occurred. Isolating the investigation component as one reconciliation module offers away to keep it manual whilst still automating the enrichment and matching of the data to reap the benefits of automation, such as time savings. The investigation results can also be fed back into the automated solution for the last module, which is reporting.

Having enriched the data and completed the mapping and investigation, you can focus on reporting, which comes in two forms. The first is the data that needs to be produced, such as a journal that needs to be posted to the General Ledger. The second is the visibility and status reporting for management and audit purposes. This involves providing summaries of the information, such as what has reconciled to date or the status of their conciliations. Reporting is not dependent on the first three modules being completed. Particularly for status reporting, you should be able to provide a picture of each reconciliation as it moves through each of the three stages. By doing your reconciliation work modularly, you permit yourself to automate. In the absence of modularity, it is all too easy to focus on the most complex, messy part of the process and reach the conclusion that you cannot automate. The modular approach allows you to split the process up into manageable chunks that you can tackle one piece at a time.