In the bank reconciliation process, the transactions recorded in the company’s electronic bank statements (EBS) or electronic cash book are compared with its e-passbook or digital passbook cash book are compared with the bank’s passbook to identify any inconsistencies in the day-to-day transactions. In this simple process of tallying the cash book and bank statement, there could be multiple errors. These errors or bank reconciliation problems might differ based on the size of the organization.
In this blog, we will introduce you to some real-life bank reconciliation examples as well as the major roadblocks faced by organizations while reconciling their bank statements.
Table of Contents
Introduction
4 Common Examples of Bank Reconciliation Statement
Challenges Faced While Preparing Bank Reconciliation Statements
Leverage AI to Reduce Errors in Bank Reconciliation
4 Common Examples of Bank Reconciliation Statement
A bank reconciliation statement is a summary document that shows the recorded bank account balance of the company matches the balance recorded by the bank. The statement covers all transactions of the company, including deposits and withdrawals.
Before deep diving into the practical examples of bank reconciliation statements, let’s go through a few terminologies which are used in a recurring way while explaining the examples.
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Challenges Faced While Preparing Bank Reconciliation Statements
Businesses can gain a variety of advantages from effective reconciliation processes. Without good reconciliation, it is difficult determining which expected payments haven’t been made. In addition to detecting fraud, cash book and bank reconciliation statements allow you to quickly identify any potential disruptions in your cash flow.
Effective bank reconciliation process offers various advantages to businesses. It allows businesses to identify any expected payments that haven’t been made, and detect fraud. Bank reconciliation can also help businesses quickly identify any disruptions in their cash flow.
However, even today, the bank reconciliation process is highly manual in nature. The accountants are responsible for manually comparing the digital passbook and e-cash book to prepare bank reconciliation statements. Additionally, sometimes due to the delay in cash being processed in the bank, there is a difference between the passbook and the cash book. This might lead to multiple errors or inconsistencies in the bank reconciliation statement. Let us explore the various problems in bank reconciliation process and real-life examples of errors in bank reconciliation:
Cash-In Transit Not Being Reflected on Passbook In case of electronic fund transfers such as wire transfers, ACH, and credit card payments, the cash is not immediately reflected in the bank, which leads to a difference in the passbook as compaACH, wire transferred to the cash book.
Outstanding Checks Not Being Reflected on Passbook This is a predominant issue which leads to multiple errors in bank reconciliation statements. If there is a delay in checks getting deposited or being processed, the balance on the passbook would not match the cash book balance.
Manual Errors Related to Bank Reconciliation As discussed earlier, bank reconciliation is a highly manual process. The accountants might enter incorrect transaction details or not add the bank fees or interest details mistakenly. These human errors might lead to problems in the bank reconciliation process and eventually the statement.
Ebooks
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Leverage AI to Reduce Errors in Bank Reconciliation
Powered by technologies, such as AI/ML, advanced bank reconciliation software make anomaly detection, variance analysis, and financial close task management easier for analysts.
HighRadius’ AI-Powered Account Reconciliation Software accelerates the reconciliation process to achieve up to 90% of auto-certification of accounts every month. It also enables the review of 100% balance sheet reconciliations before ledger close. Driven by artificial intelligence, the software transforms reconciliations from a reactive to proactive process by detecting anomalies, making it faster and accurate.
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