CFOs and the finance departments have been hearing about how Artificial Intelligence is a game-changer for transforming FP&A operations. Many solutions providers talk of these advanced technologies as some magic blanket that can be thrown over to make all accounting challenges disappear. While AI and ML are essential technologies, they aren’t magic solutions. At the same time, they also aren’t to be feared for replacing humans.
Many software companies tout AI and Machine Learning technologies embedded into their software, but only a few companies are going beyond a rules-based approach to automating the analysis, decision-making, and solving company challenges.
This eBook highlights the right approach for applying AI in the financial close process for error detection. We will also uncover the differences between AI-based algorithms and rule-based automation and why error detection is the perfect use case for AI.
Every month organizations go through a stressful period during the financial close process, where they validate account balance and transactions for the month. The finance teams follow this process with limited resources and time available to perform an accurate analysis of the data. Hence, the finance teams during the monthly financial close period are overburdened as there is never enough time to validate everything, so rules of materiality are applied.
Research shows that 40% to 60% of the time accountants spend on the close process is looking for errors and omissions in transactions and account balances, which we also refer to as anomaly detection. Ideally, anomaly detection should be an everyday process, but usually many organizations do it after the month-end close starts.
Accounting transactions are very pattern-based, and the finance departments handle certain transactions every month or quarter. For example, vendor invoices are typically charged to the same account segment/account combination, and revenue and write-offs are typically charged to a similar account segment/account combination. An everyday process of looking at the patterns and identifying the outliers (anomalies) can enable them to get corrected before the month-end period starts, which will not only reduce the burden on finance teams during the close process but also improve the accuracy. Read our eBook, Best practices for improving month end close accuracy, and find out more about the challenges that finance teams face while reviewing large amounts of transactions every month.
There are several different AI solutions available for detecting fraudulent transactions, banks use them, retail companies use them on their cash register transactions, and many other companies use them as well. Identifying errors and omissions in accounting is just an extension of those methods. Identifying Errors and Omissions is a perfect use case for the Artificial Intelligence process because accounting is very pattern based. Here are some reasons why:
Algorithms interpret patterns: AI technology uses computer algorithms. The software programs aim to mimic the human ability to learn, interpret patterns and make predictions. The key to identifying errors and omissions is “interpret patterns”. For identifying errors and omissions, “Machine learning” is a form of AI that uses data to train algorithms to recognize patterns and identify outliers/Anomalies.
Flagging errors: AI technology looks at the sequences and patterns of invoice numbers. If an Invoice falls out of pattern or comes in at a different time it could be a manual invoice and a duplicate billing, which will be immediately identified and flagged as an error/anomaly.
One might wonder, what is unique about AI algorithms and how is it different from rules?
Algorithms apply different dimensionality, tolerances, and focus to identify outliers that translate into anomalies. 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.
Rules tend to be discreet “If/than/else” logic to find errors. Algorithms go beyond “If/than/else” logic to offer much border scope that is needed by Anomalies, replacing 100’s of rules logic.
|What can ‘Rules’ do||What can ‘Algorithms’ do|
|Flag anomaly if vendor A amount is greater than 5000.||Show all payments where the vendor is paid out of range of typical payments|
|Flag anomaly if vendor B and amount greater than 2000.||Show journal entry transactions that aren’t typical|
|Flag anomaly if manual journal entry to goodwill account||Show potential missed recurring payments|
|Flag anomaly if there’s a large customer write-off amount||Show invoices that seem to be coded to the right segment combination|
|Flag anomaly if large journal entry hitting the cost of goods sold making margin go negative for this commodity class||Identify transactions that don’t fit usual dimensional and amount patterns (Anomalies are outliers to normal transactions)|
|Flag anomaly if vendor C not paid this month||Identify any anomalies that are outliers to normal transactions|
|Flag anomaly if vendor D not coded to department 101||Identify transactions that are unusual in nature|
In comparison to AI-based algorithms, rules bring many challenges trying to identify anomalies:
Difficult to Scale: Rules are discrete for dimensional combinations, you could easily have +1000 rules. Management of those rules will become a problem and the system will stop reacting to newly added vendors/customers/accounts.
Scope: The task of identifying unusual transactions follows two different threads:
Rules don’t offer a broader identification of anomalies and a lot of individual rules need to be updated to cover the many different types of unusual transactions. While both rules and algorithms apply structured inputs and logic, rules tend to be discrete and challenging.
With rules, finance teams need to manually intervene and maintain 1000+ rules for each and every tasks, which include:
Vendor payments and invoices: Imagine reviewing all vendor payments and the typical invoice number pattern to understand if an invoice is a manual invoice and then cross referencing that against other vendor invoices and payments to check for a duplicate payment.
What if there was a Machine Learning process that does all the rule discovery based only on those different sets of dimensionality and then takes those patterns and lists and processes them through an algorithm that:
Classification algorithms can be broad, one algorithm focused on specific dimensionality such as time, amount, invoice number, vendor, etc., can be used to replace hundreds of discrete rules. One algorithm can be used to identify missing payments, duplicate payments, missing journal entries/duplicate journal entries, etc. The concept of Rule Discover is a very simplified example of machine learning. Think of learning algorithms as creating a list of usual transaction dimensional combinations. Those algorithms provide the input of a related algorithm that looks at the current day’s transactions and identifies the outliers.
Hence, AI and ML can be the perfect financial close management technologies for detecting errors and omissions; and overall improving the General Ledger entries and data accuracy. Watch this webinar by Ron Baden, VP, CFOTech, HighRadius, and learn more about how to apply AI in accounting with some real use cases.
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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.