How Benford’s Law and AI Categorization Accelerate Financial Fraud Investigations

Financial fraud is a major global issue. Undetected financial fraud can bankrupt businesses and even damage entire economies, making it absolutely imperative that accountants and attorneys have the right tools to detect and prosecute fraud at every level. 

One useful tool in the fraud investigation arsenal is Benford’s Law, which can help detect the anomalies that are often the key indicators of fraud. Though not a foolproof fraud detector, using this statistical phenomenon can be a vital part of a comprehensive professional toolkit in resolving matters where financial fraud is suspected. When supported by the right technology, it can lead to more efficient and effective resolutions. 

Understanding Benford’s Law

Benford’s Law is an indicator of probability in certain datasets. It refers to the relative frequency distribution of the first digit of numbers in a dataset. The principle states that about 30% of numbers in any financial dataset will start with 1, while numbers 2 through 9 steadily diminish in frequency, with numbers beginning with 9 only occurring 5% of the time. 

While the law doesn’t apply to all datasets, it does tend to be true for stock prices and financial data—which is why it can be useful for detecting fraud. 

Using Benford’s Law in Fraud Analysis

Though Benford’s Law can be applied to fraud analysis in a number of ways, the most common application generally follows this process: 

1. Calculate Expected Distribution: Because Benford’s Law provides an expected distribution of the leading digits in a dataset, forensic accountants can use it to calculate the expected frequency of first digits for the particular set they are analyzing. 

2. Compare with Actual Data: Next, the actual financial data can be compared side-by-side with the calculated distribution to see how closely they match up.

3. Focus on Anomalies: Large discrepancies between the expected distribution and the observed distribution may point to areas that require closer scrutiny. Closer analysis of the transactions associated with these discrepancies can help to identify potential errors, fraud, or manipulation. 

This application makes Benford’s Law a useful way to identify red flags in financial data. Though the identified anomalies don’t necessarily prove wrongdoing, they highlight where further scrutiny may be warranted. This can help to direct the focus of a financial investigation toward areas with the highest likelihood of fraudulent activity. 

Benford’s Law can also be applied to a wide range of financial data, including income, expenses, invoice amounts, and other relevant financial information. This comes in especially handy with large datasets where it would be impractical to manually examine each individual transaction. This sort of analysis has the potential to save professionals many hours of extra effort. 

Enhanced Application of Benford’s Law Using Technology

Benford’s Law is a powerful analysis tool because it highlights potential red flags more quickly than manual review. However, prior to analysis, you still need to organize, extract, reconcile, categorize, and group the data—which is an incredibly time-consuming process.

Here’s how advanced software, like Valid8, can help expedite time-to-analysis while ensuring accuracy: 

Load and Extract Data

After all financial documents have been gathered, scanned, and organized, the data can be loaded (or extracted) into a database. Depending on the type of document, this can be done directly or by using Optical Character Recognition (OCR) to pull the data from document scans. AI-driven SaaS solutions can also recognize and categorize text strings like statement dates and period balances, checking to ensure the beginning and ending balances match up for each accounting period. 

Reconcile Data

Data reconciliation is an essential process for identifying discrepancies, errors, or anomalies in the extracted data before proceeding with the investigation. The process involves several distinct steps that can each be aided by technology:

• Find and fix data errors: Errors happen no matter the extraction process. It’s important to check for duplicate statements and accounting periods, which can easily be done with the right software.
• Load or integrate data with master copy: After checking for accuracy and removing duplicates, the data can be loaded into a master database. Specialized AI software can then match check and deposit slip images to relevant bank statement transactions. 
• Identify missing data (and repeat): Using software capable of creating a map of all the gathered data provides a visual timeline that updates as data is added. This makes it much easier to identify gaps in the data set and determine what is missing so it can be acquired or investigated. 

Primary Analysis 

With a clean transaction database as a foundation, the data can now be checked to prepare it for proper analysis. Technology can also help here in a few different ways:

• Categorize and group transactions: Technology like Valid8’s automated AI categorization can classify banking transactions by transaction type, location, category, subcategory, and counterparty to allow for more streamlined analysis. 
• Identify transfers: Software can identify and match transfers, allowing the user to confirm or reject matched pairs—which is a far more efficient alternative to manually working with a spreadsheet.
• Identify undisclosed accounts (and repeat): Data visualizations provided by data mapping software can easily show which accounts don’t match up with the categorized data. The power team of Benford’s Law plus technology makes it much easier to effectively visualize anomalies—for both the investigator and the members of the court. 

Tech and Humans in Harmony: Greater Than the Sum of Its Parts

Statistical observations like Benford’s Law are canonized as mathematical principles because they serve a useful purpose—in this case, making it easier to detect suspicious anomalies in financial data. Similarly, technology like Valid8’s financial AI solutions are widely used because they save time and effort in untangling webs of data in fraud investigations. Both tools are useful on their own, but a savvy professional will know that using them in combination can improve the efficiency of both, exponentially improving results. 

Valid8 is a verified financial intelligence (VFI) platform specifically developed for financial analysis. It is well equipped to be used alongside manual statistical tools like Benford’s Law to streamline data preparation and improve the accuracy of fraud analysis. 

If you’d like to see for yourself how Valid8 can help you generate a clean and accurate data map for your financial fraud investigations, schedule your free 30-minute demo and let our team show you just how simple it can be.