A forensic accountant typically analyses accounting or financial data in a dispute or investigation context to investigate or quantify a financial issue. But as datasets grow exponentially larger and increasingly complex, the questions clients ask us are equally complex. This is where data analytics is useful — and, frankly, are now tools for your forensic accountants to get to the answer.
What is Data Analytics?
Data analytics is the process of collecting, analysing and drawing insights from large and disparate data sets to enable informed decision-making. In a forensic accounting context, applying data analytics to business and financial data leads to robust and defensible calculations that address complex questions that systems were often not designed to answer in a cost-saving and efficient manner.
When used correctly, data analytics can consolidate disparate datasets, be repeated on any dataset of the same data structure with minimal changes, saving time and cost and extract useful insights which otherwise would have been lost in the unorganised chaos of voluminous data.
Managing Disparate and Large Data
Typically, we see our clients' annual transaction data (also known as general ledger data) range from thousands of rows to tens of millions of rows of data on an Excel spreadsheet. Companies often use multiple systems that don't 'talk' to each other and were not designed to answer the type of questions often asked of forensic accountants.
In these instances, data analytic tools and programming skills provide a reliable and cost-saving solution to consolidate disparate financial information into a centralised database for analysis and identify what financial information is available or, more importantly, what should be available and is not.
In a recent matter, for example, we were given several years of financial data extracted from multiple accounting systems, which, due to the volume of the data, meant we were provided with more than 150 files. This mammoth task was complicated by the fact that data was extracted at different points in time from different systems and for overlapping periods. Given the size and volume of data, it was not possible to use Microsoft Excel to capture and analyse the relevant data. Instead, data analytics was used to efficiently consolidate the information into one database, identify gaps in the information provided, and inform the discovery process.
Repeatable, Economical and Efficient Models
Forensic accountants are often asked to address highly specific questions that require complex calculations or scenario modelling. As datasets get bigger and inputs increase, the calculations cannot be done effectively in traditional tools such as Excel (or, if done, regularly causes the computer to crash).
A model created through data analytics (typically called a script) provides a sequence of specific instructions to process the dataset, perform the analysis and produce an output in a specific format. The script is repeatable, meaning the same output will be given every time the same script is run on the same dataset. This reduces the chance of error and allows for the script to be repeated on any dataset of the same data structure with minimal changes, saving time and cost — for example, to change the review period or change the scope of entities included. For this reason, data analytics tools are critical for calculations involving extended time periods, multiple parties and voluminous transactions — such as payroll remediation, class actions and cases involving the analysis of bank statements.
For instance, in a typical payroll remediation calculation we are required to determine whether there is a difference between the amount paid by the company and the amount that should have been paid under a specific set of conditions. The flexibility of a script allows for the model to be first developed on a small and more manageable subset of the database to efficiently identify and address key issues before the calculation is pushed to a wider review period or additional entities. The same concept has also been applied to class action cases where a calculation is done for one applicant before extending it to all group members.
Communicating Complex Results and Highlighting Insights
The power of visualising data cannot be overstated. From simple charts showing financial trends over time to relationship maps showing the circularity of funds in a Ponzi scheme, data visualisation allows us to highlight trends buried in a mountain of data and communicate complex results in a compelling way.
One of the most dramatic uses of data visualisation is understanding and communicating the impact of assumptions on scenarios. As highlighted in How a 'Dirty' Expert Helps Clean up Your Accounting Evidencethere is real value for legal teams in identifying the sensitivity and materiality of certain inputs and assumptions on a calculation.
Imagine an interactive dashboard during a settlement negotiation or mediation that allows the user to toggle between different inputs and automatically update calculations and charts to visually demonstrate the impact to the opposing party in real time. This method of visualisation was a game changer in a recent engagement, allowing our defendant client to make a compelling argument to the plaintiff and their legal team, drastically reducing the quantum in dispute, and be confident in making a robust and defensible decision at a critical point in time.
Factual Observations Accepted in Court
Because of the increasing complexity of data, forensic accountants are often asked to answer questions that are more like factual observations than opinions. For example, we may be asked to present a summary of the transactions between a group of vendors or within specific supply chains. A common challenge to this type of analysis is whether any 'opinion' is actually provided by the expert, and therefore whether it constitutes 'expert evidence.'
We are pleased to report that it does. In a recent case, a Tribunal specifically addressed this issue and found that the ability to succinctly present observations of financial data, particularly from multiple sources, is a skill, and any opinions given after the forensic review and organisation of that data can be considered as an expert opinion.1
Accordingly, we see an increasing demand for experts to provide analysis of complex datasets, where the output is factual observations responding to questions about the nature of underlying transactions.
Conclusion
Data analytics has increasingly become an essential skill in legal disputes and investigations with vast volumes of data and a need for efficient, data-driven and repeatable analysis and calculations. When stakes are high and time is limited, knowing how and when to use data analytics as part of the solution is the key to robust outcomes for clients.
FootnoteS
1: CPG Group Pty Ltd v. FC of T, [2024] AATA 199 (Austl.), paragraph 114, Deputy President F D O'Loughlin KC, 5 January 2024
2: CPG Group Pty Ltd and Commissioner of Taxation [2024] AATA 199, paragraph 115, Deputy President F D O'Loughlin KC, 5 January 2024
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