What are the methods of account reconciliation?

5 mins

Account reconciliation is defined as the process of comparing two related sets of financial records to find out if they are in agreement. It is an important internal control measure aiding entities for a smooth financial close and is mandated by various regulations such as the Sarbanes-Oxley Act in the US.

Businesses are required to reconcile all their balance sheet accounts to find out possible account misstatement. Timely reconciliation of balance sheet accounts allows entities to identify discrepancies and make necessary adjustments in the general ledger in a timely manner, ensuring the completeness and accuracy of financial statements.

Reconciliation of accounts can be done in different ways including manual reconciliation and the use of account reconciliation software. In this article, we will discuss various methods of account reconciliation along with their pros and cons.

Stages of Account Reconciliation

Account reconciliation is normally carried out after the close of a financial period. Here are the steps involved in a general ledger reconciliation process:

  • Accounting professionals check each account in the general ledger and scrutinise that the balance of each account is accurate.
  • In the process, they compare account balances with outside sources such as bank statements and credit card statements.
  • If mismatches/discrepancies are found, accountants investigate them further and take necessary corrective actions, including adding adjustment amounts, correcting balance errors and making additional journal entries.
  • All these actions are stored for audit purposes.


Different Reconciliation Methods

Unlike earlier times, reconciliation cycle times in financial institutions have increased and processes have become inefficient, and costly today. It is difficult to manage and reconcile the growing size and complexity of data and adhere to ever-growing regulatory requirements. Regulations such as Basel III, Dodd-Frank and MiFID II mandate increased depth and breadth in the reconciliation process.

Given the situation, age-old reconciliation methods and rules-based solutions are poised to become ineffective and prone to numerous errors. Here, we will list out various reconciliation methods and find out their advantages and disadvantages.

Manual reconciliation

This is the traditional way of account reconciliation with written accounts and dedicated staff. Here, accountants manually check all the written accounts to see if all entries are matching. While smaller firms with a limited amount of transactions can still employ this strategy. For large businesses, this method is no longer feasible in today’s scenario due to the ever-increasing data volumes.

Spreadsheet reconciliation

This is done by using spreadsheet software solutions that have basic data arrangement and calculation features. A great tool for data analysis and streamlined calculations, spreadsheets are still used by a large number of organisations. In fact, recent studies suggest that almost 70% of the world’s financial institutions still use Microsoft Excel for reconciliation processes.

Spreadsheets, on the other hand, are not sustainable in the long run for the following reasons.

  • They cannot manage the rapid handling of data as demanded by the regulations today.
  • Spreadsheet reconciliation can consume up to four hours of an accountant’s time every day as he/she has to manually sum up the numbers and spend additional time in the mechanics of reconciliation.
  • They cannot handle reconciliation tasks that become increasingly complex
  • Change in document owners and users can lead to confusions and process disruptions
  • They tend to fail in case of excessive data loads
  • They lack proper audit trail options
  • They lack analytics needed for today’s businesses


Rule-based and Hosted Reconciliation Solutions

Through partially automating reconciliation processes, these software solutions could greatly reduce errors that came via manual processing. They could address matching of transactions more effectively with pre-set business matching rules and create cases around exceptions/breaks which need human intelligence to reconcile. These solutions can remove almost half of the costly and time-intensive manual intervention required in reconciliation.

Rule-based solutions generally lack in the following aspects:

  • Onboarding of these solutions is complex and lengthy
  • It will take time to change rules to adapt to changes in regulations and accounting practices.
  • Pre-defined rules need to be built in and tuned on an ongoing basis in order to address the changing data patterns.
  • It is humanly not possible to design n-factorial rules required by RPA solutions to completely automate processes.
  • They cannot handle new asset types and with more complex technicalities and calculations involved.
  • Break resolution is still manual and time-intensive
  • The basic problem with RPA solutions is they have no or limited learning capability.


Artificial Intelligence/Machine Learning-based Solutions

There are also reconciliation software solutions powered by modern technologies such as artificial intelligence (AI) and machine learning (ML). These modern reconciliation software solutions came into play to address the drawbacks of rules-based solutions. Mixing and matching certain attributes of data across multiple files will help match records. It is not manually possible to figure out attribute-mix and create that many rules.

AI/Machine Learning can automatically identify attribute-mix/pattern and create rules for matching. They can also do exception handling, a key reconciliation process, which is completely manual today. The main advantages of these solutions are:

  • They can analyse historical data and create optimised, re-playable match rule configuration with minimal user interaction.
  • They take care of data quality and ambiguity issues and can ingest data from multiple sources and file formats.
  • They use machine learning techniques to accurately detect break types and predict unknown patterns thereby auto-detecting new break cases.
  • They provide a complete audit trail of machine learning predictions so that every recommendation is justified through a trail. They can also accurately predict adjustment amounts.
  • They can be onboarded faster by any business with minimal customisation.

Thus, these new-age solutions can address the problems of rules-based solutions and make the reconciliation process more efficient. Solutions such as Tookitaki Reconciliation Suite are horizontally scalable to move hand-in-hand with ever-growing data sets and support flexible deployment options to minimise the cost of deployment.

By and large, they provide centralised control, better monitoring, operational cost savings, increased effectiveness and efficiency, better accessibility, improved data security and reduced audit risks.

In comparison to rules-based solutions, our solution goes a step ahead and enables completely automated reconciliation, while providing superior accuracy in matching and effective exceptions management.

Speak to a member of our team today to learn more about our market-leading reconciliation solution.