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Best practices for improving month end close accuracy

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What's Inside?

  • Challenges while reviewing transactions during month end close
  • Best practices for finance teams to follow while reviewing transactions
  • How to leverage real-time data and AI for the process of reviewing all transactions
CONTENT

Chapter 1

Introduction

Chapter 2

Challenges that hinder reviewing transactions during month end close

Chapter 3

What can the finance team do

Chapter 4

Conclusion: The need for Real-time Data and AI
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Chapter 01

Introduction


The month end close is a time-consuming process as it goes through multiple stages & functions, which include recording financial transaction summaries from external systems, account analysis, accrual calculation, reviewing transactions for errors and account reconciliation. 

Reviewing all the transactions for the month, and ensuring that they are accurate and applied to the correct accounts is one of the most important functions that the finance teams perform every month.

Chapter 02

Challenges that hinder reviewing transactions during month end close


Reviewing all transactions is a laborious task. And every month after closing books, the finance teams are uncertain and left wondering whether they caught all the errors and omissions. The reason for this uncertainty is the manual nature of the task, which poses a great burden on the finance teams, month after month. Let’s take a look at some of the key challenges that finance teams face while reviewing transactions during month end close:

  • Too many transactions for review and too little time: The month end close process requires the analysis of all transactions of that particular month during the close period, as well as the upcoming month after the close period. This leaves the finance teams with too little time to review too many transactions. The process becomes even more challenging for organizations with lean finance teams, where they have to perform such humongous tasks with little manpower.
  • Increased scope for errors and omission: To limit the scope of reviewing all transactions, accountants will apply the concept of “materiality” to transactions.  They will analyze all the big transactions and some percentage of the midsize transactions and very few small transactions because an error in those won’t “materially” impact the fact-based decisions of those who rely on the month end financial reports. There are slight nuances to transactions that are easily overlooked that impact financial results and have to be considered and screened for:
    • An expense has been previously accrued but the expense is coded all in the current month.
    • Payment is for a prepaid expense that covers multiple time periods but is coded to the current period.
    • A purchase order covers a multiple-month purchase and it hasn’t been billed yet but some benefits have already been received.
  • Difficulty in identifying errors: Over a period of time, most of the transactions during the month end close period develop certain patterns. These patterns cut across different dimensions of the transaction depending on the customer/vendor and account segments they are applied. And it can be highly challenging for an accountant or the manager to identify and monitor the “outliers” in these pattern-based transactions, which can leave room for errors.

Challenges while identifying/monitoring the outliers


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Chapter 03

What can the finance team do


While the task of reviewing all transactions can be daunting, finance teams can make it less challenging by following the below-mentioned best practices.

Apply the concept of materiality: The most common approach is to apply the concept of materiality, validate the high-dollar transactions, and some of the mid-level transactions, and randomly review some low-dollar transactions.  

Ensure routine account analysis: Do routine analysis on all prepaid, purchase order, and accrual accounts to be sure the amounts are still valid. Accountants can do account analysis even during non-close times, this will give room to analyze more of the transactions rather than assuming all material items are timely identified and all smaller transactions will be caught eventually.

Keeping a constant vigilance: This is where technology and automation come in. Keeping a constant check to get 100% review can be an ordeal for the finance teams, but enabling automation through financial close software can reduce the stress as well as ensure increased efficiency.

Chapter 04

Conclusion: The need for Real-time Data and AI


The financial close process of reviewing all transactions can be automated by leveraging two technologies:

  • Financial close automation system with continuous data integration: Continuous data can help finance teams perform tasks like account reconciliation and account analysis by eliminating data cut-offs, duplication of transactions, and errors. It can ensure that the finance teams can do this analysis throughout the month rather than burning the midnight oil during the month end close process. Read our ebook, Real-Time Data is the Minimum Requirement for a Continuous Close, and learn more about how real-time data can help.

  • AI for detecting errors and omissions: Month after month the finance teams go through the same process  of looking for errors and applying a level of materiality. Applying AI technology can help in identifying any errors, unusual/out-of-sequence data or activity, and lower the level of materiality.

    Various pattern-based transactions during month end close


    Streamline the financial close process with improved data accuracy with AI-powered solution.

    Identifying anomalies in pattern-based transactions can’t be done by rule based automation. It needs Artificial Intelligence to dynamically identify the data patterns that are both normal and abnormal. AI technology uses algorithms for pattern detection and applies dynamic rule discovery across and combined with different dimensions, tolerances, and focus. It isn’t a magic algorithm that finds everything but a series of algorithms that focus on specific AI-derived dimensionality and tolerances. It learns as data sets roll forward and the data is enhanced with the actual corrections applied vs. anomalies detected. The good news is you don’t have to think up all of these relationships and rules. Pattern-matching algorithms use your past transactions as patterns for the future.

Read our eBook, “Demystifying AI for Anomaly Detection” to understand more about why AI is the right technology for detecting errors and omissions, eventually improving the data accuracy in the financial close process.


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HighRadius Autonomous Accounting Application consists of End-to-end Financial Close Automation, AI-powered Anomaly Detection and Account Reconciliation, and Connected Workspaces. Delivered as SaaS, our solutions seamlessly integrate bi-directionally with multiple systems including ERPs, HR, CRM, Payroll, and banks. Autonomous Accounting proactively identifies errors as they happen, provides the project management specifically designed for month end close to manage, monitor, and document the successful completion of tasks, including posting adjusting journal entries, and provides a document repository to support each month’s close process and support the financial audit.

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