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J. Patrick Quillian
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Uncovering fraud with algorithms and machine learning

For years, it was hard to untangle financial scams because going through the documents required lots of concentration and focus. Sometimes, the sheer volume of information was an impediment to uncovering what was actually going on. Today, law enforcement authorities in Oklahoma and around the country know that there are new ways to uncover fraud. Artificial intelligence is making it easier to spot the discrepancies that reveal the commission of certain white-collar crimes.

Changes in detection techniques

In the past, people would search for fraud in time-consuming and laborious ways. They involved things like examining ratios and percentages in the numbers found in the documents. Often, only larger-than-average transactions were examined more closely. However, computer science has changed everything.

Algorithms follow simple rules to sift through huge amounts of information and look for indicators of fraud. Machine learning makes it possible to include many more variables in these analyses. This means they can spot much more subtle patterns of possibly fraudulent actions.

The role of history

About 20 years ago, there were huge several financial scandals in the news. White-collar crimes at companies like Enron and WorldCom caused billions of dollars in losses to investors. This created demand for more robust fraud detection measures.

Regulators and compliance professionals understand that fraud happens when opportunity meets financial incentives and a lack of conscience. All people who have access and ability to create and authorize transactions need to be monitored closely. Otherwise, bad things can happen. Sometimes, people who did nothing wrong find themselves accused of wrongdoing.

If you are suspected of financial crimes you didn’t commit, you need representation that understands the kind of analysis prosecutors use today. A an experienced attorney and retained experts may be able to demonstrate that you are not the person responsible for irregularities.

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