Time and time again, we trumpet the incredible value of advanced data analytics in forensic investigations – often, it is the key to finding the needle in the haystack. Fortunately, our firm remains at the forefront of utilizing data to identify unexpected patterns when investigating financial fraud – that red flag that tells us something isn’t as it should be – whether for a qui tam case involving kickback schemes or a case of underreported revenue. While we’ve discussed the importance of data analytics in a past blog, it’s also important to understand the variety of data that’s available and why you should enlist an expert to help translate the immense amount of information.
There are two general categories of data: structured data and unstructured data. While there are critical differences between the two, it’s crucial to understand how their distinctions work in concert to help us put the pieces of the puzzle together.
Structured data includes information that is highly organized and transactional in nature, like accounting system data and bank account data. By its nature, structured data is readily searchable.
Without data analytics, anomalies in structured financial data can be difficult to detect – but with the right tools and a little knowledge, implementing analytical techniques can bring forward the evidence needed. Here’s a key example: FSS worked with a school district that had a control in place that limited funds that could be transferred without board approval to $50,000. Knowing this, we searched for transactions of exactly $49,999. We found that one of our persons of interest had transferred this exact amount 48 times within 60 days.
Thanks to the structured data and an understanding of the organization’s policies and procedures, we easily honed in on this anomaly among millions of transactions – something that couldn’t have been accomplished without the use of data analytics.
But what if there is more to the story — what if more questions need answers?
While structured data is, well, structured – unstructured data is essentially the opposite. Structured data plays a key role in data analytics, however, we continue to see the rise of unstructured data to bring context to our cases. The analysis of this “nontraditional” data can help investigators follow the electronic trail often left behind by wrongdoers. Unstructured data includes:
- Text messages
- Social media
- Public records
- Word documents
As time goes on, forensic investigators will be forced to branch out beyond traditional analytics. The use of advanced data analytics with the combination of structured and unstructured data can give investigators a broader understanding of their subject and can help uncover a wealth of evidence that may go undetected when only considering traditional data sets. Advanced data analytics can be particularly useful when faced with incongruent information systems or spotty evidence.
Validating & Comparing Varied Data
Recently, FSS was hired to investigate whistleblower allegations that a company violated the Anti-Kickback Statue and submitted false claims to the government.
This qui tam case involved a company that worked alongside a third-party organization that provided goods to customers in exchange for referrals. Allegedly, the company paid the third-party organization a percentage of the compensation they received from the federal government as a result of the referrals. As part of this investigation, FSS requested and analyzed the following data:
- Transaction details used to submit claims to the government from two disparate billing systems (structured data)
- Word documents and PDF records of purchases from vendors of goods provided to their customers (unstructured data)
- Emails between the company and their customers requesting and acknowledging receipt of the goods (unstructured data)
Through the testing and analysis of this disparate data, FSS was able to identify evidence that customers were incentivized to do business – and that the third-party company was paid kickbacks. The structured data was used to establish that the claims were submitted to the government. The unstructured data was then used to compare against the structured data – allowing us to draw the connection among the incentivized referral (goods provided to the customer), claim submission to and payment received from the government, and commission payment to the third-party organization.
Without access to the varied types of data and the use of advanced data analytics, it would have been much more difficult to develop the evidence and reach our conclusion.
The Power of Advanced Data Analytics
The combination of structured and unstructured data in forensic investigations is a game-changer, particularly when used in conjunction with other techniques, such as interviews. Data analytics allows investigations to move faster and cost less, all while allowing our examiners to synthesize unlimited amounts of quantitative and qualitative data to identify trends, relationships, unexpected patterns, inconsistencies and irregularities – and helping them find the proverbial needle in the haystack.