How Data Analytics Could Have Stopped a Fraudster Like Bernie Madoff?

It’s hard to believe that almost a decade has passed since Bernie Madoff was caught operating his now-infamous Ponzi scheme. But it’s a scandal that few of us will ever forget. Madoff conned his investors out of $65 billion over several years, making it the largest fraud scheme in U.S. history. He was eventually sentenced to 150 years in prison on 11 counts of fraud, money laundering, perjury and theft.

In retrospect, many wonder if Madoff could have been stopped far earlier if the technology was available. In theory, the answer is yes. The right data analytics tools and protocols could have exposed the red flags of Madoff’s fraud scheme, but of course, not in isolation.

The Switch From Documents To Data

Madoff may have simply been a product of outdated information storage. The Securities and Exchange Commission had some glaring issues in terms of management, but one of their biggest problems came from the way they stored information.

The agency has been described as using “1930s pen and paper technology” to record data that clearly needs a digital solution. For example, regulators at the firm relied heavily on PDF documents, which investigators would have a hard time to extract valuable information from. Even tech companies that attempt to scrape data from PDFs often come up empty-handed.

Since the SEC (and presumably other organizations) use such outdated methods, it’s quite likely that they were unable to spot any patterns or observe unusual numbers that would raise red flags.

Therefore, instead of using old-fashioned documents, experts have suggested that these companies rely on data and to do so for good reason.

Benefits Of Collecting Data Over Documents

  • Data is a living and breathing organism that changes over time (unlike static PDF documents)
  • Standardized data reporting is available to multiple investigators
  • Data reporting and analysis can be automated
  • Data can reveal unusual patterns that documents can’t
  • Collection of data over documents would enable tech companies to re-publish financial data so that a company could analyze hidden risks (ie. mismatches in vendor information)

The data could have been used to find discrepancies far before Madoff’s clients noticed a problem (which would have been ideal). For example, one of the tipping points for Madoff’s downfall was when his clients requested $7 billion back in returns – Madoff only had $200-$300 million to give back.

He had obviously lied to this clients about how much money he had generated for them, and they believed him because of his track record. But what if the SEC had been monitoring his accounts? Could they have uncovered mismatches in the amount of money he had generated in returns for his clients versus what was actually deposited? It’s highly possible.

With that said, the data would certainly have to be set in concordance with other information that serves as a red flag. That brings us to our next point.

Data Analytics Don’t Work In Isolation

Detecting a Ponzi scheme or any type of fraud is difficult and there usually needs to be some sort of accusation or “intel” to bring a scheme into the spotlight. In the Bernie Madoff case, as are many cases, there was a whistleblower – Harry Markopolos – who brought the scheme to the world’s attention.

That doesn’t mean a company should hire a designated whistleblower (that would be tricky anyway). However, they should establish an open corporate culture where everyone is subject to the same regulations and given the same amount of scrutiny when matters run afoul.

With that said, one of the reasons why Madoff was able to defraud so many people was due to his status as a trusted investment expert, and the fact that no one questioned his abilities.

Data Could Have Stopped Bernie In His Tracks

As we stated earlier, Madoff was a product of his reputation and environment – a trusted individual free of external surveillance and a system to monitor his trail. By nature, Ponzi schemes expose themselves to scrutiny because there is a mismatch between the returns being generated and what the fraudster claims to be generating.

A data analytics solution can find discrepancies such as a mismatch between stated revenues/funds versus what is actually being entered into a financial program. When combined with other investigative practices, business leaders will greatly increase their success in stopping corporate fraud.

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