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Overcoming Investment Fraud with New Technology Solutions
After the Bernard Madoff Ponzi Scheme case, financial institutions are using new technology to discover investment fraud, and protect themselves.
In 2008, Bernard Madoff a once well-respected American financer who held the position of chairman at the prestigious NASDAQ exchange, was arrested for orchestrating the largest investor fraud and Ponzi scheme in history, coming in at $65 billion. This is by far the biggest financial crime ever committed, and certainly the biggest investment fraud case.
The monumental losses and widespread attention generated by the 2008 Madoff case may have contributed to a hastened level of enforcement and investigations into potential Ponzi schemes, following a long-term decline in cases between 2010 and 2018. But in 2019, investment fraud cases recently surged again – up 30% – to 60 cases in the United States alone.
Changes in the investment fraud and Ponzi scheme landscape
The specter of seemingly legitimate investment schemes targeting the public comes at a terrible cost to individuals and families, where life savings are stolen and used to fuel luxury lifestyles for the white-collar criminals selling snake oil in the form of high-yield investment fraud schemes.
Since Bernard Madoff was arrested, there have been significant changes in the fraud and Ponzi scheme landscape:
More Ponzi scheme cases are international in scope, and there is more collaboration by law enforcement agencies in multiple countries to investigate these cases
The growth of cryptocurrencies like Bitcoin have introduced new levels of complexity into recent investment fraud cases, and new opportunities for criminals to move money and to obscure their activities
Advancements and innovations in technology have given investigators better tools for discovering investment fraud, using advanced analytics, data mining, artificial intelligence and other techniques, and
Governments have stepped up their demands on financial institutions to do more to discover money laundering and fraud, and the level of fines levied against financial institutions has risen dramatically in an effort to drive more rigorous enforcement of the rules.
A growing need for fraud detection technology
Banks and other regulated financial institutions have strong incentives to implement the most advanced anti-fraud technology available. One reason is that courts in many countries are holding banks accountable for failing to detect fraud and other violations of banking regulations.
In 2013, one Bank paid over $50 million to the U.S. Securities & Exchange Commission and Office of Comptroller of the Currency for failing to file suspicious activity reports (SARs) regarding the huge money flows occurring in the accounts controlled by a fraudster. The suspect was convicted and sentenced to 50 years in this $1.4 billion Ponzi scheme.
The risk for banks is not just from fines levied by regulators. More and more class action lawsuits are being filed on behalf of consumers who suffer losses in fraud cases, seeking damages from banks serving the alleged fraudsters. So there’s more incentive than ever to avoid facing the consequences of a big investment fraud case.
More recently, a North American bank was sued for $4.5 billion by the liquidators of a high-profile investment fraud encompassing numerous companies involved in what was a notorious $7 billion Ponzi scheme using bogus certificates of deposit. In June 2021, an Ontario judge ruled that the Bank was not liable for the losses. The threat of huge losses created by being unknowingly linked to complex investment fraud schemes remains.
Tackling investment fraud with advanced analytics
Today’s advanced analytics technology uses a range of methods to detect fraud, including:
Data mining to classify, cluster, and segment data to automatically find associations, rules and patterns that may indicate fraud
Link analysis to find associations between people, entities, locations, companies, IP addresses, etc. and to enable investigators to quickly analyze high volumes of data to find those associations
Expert systems to encode expertise for detecting fraud in the form of rules
Pattern recognition to detect clusters or patterns of suspicious behavior either automatically (unsupervised) or to match defined inputs.
One critical benefit offered by today’s fraud detection technology is enabling investigators to discover relationships between people, companies, events, transactions, locations, and more. That is key to overcoming one of the most successful tactics used by sophisticated fraudsters; layering and creating separation between themselves and their fraudulent activities. They do this by creating multiple shell companies, by putting ownership of assets and companies in other people’s names (often family members), by changing corporate officers frequently, by using fake data in corporate filings, and taking other steps to obscure their roles and activities and create “noise.”
A good example of this was in the case of the company OneCoin, a cryptocurrency firm launched by Dr. Ruja Ignatora that generated losses estimated at hundreds of millions of USD for investors when Dr. Ignatora disappeared in October 2017. The BBC reported on its investigation of OneCoin, saying:
“OneCoin’s corporate structure [was] … incredibly complicated. Here’s an example: Ruja bought a very large property in central Sofia. Technically it was owned by a company called One Property. One Property was owned by another company called Risk Ltd. Risk Ltd was owned by Ruja, but was then transferred to some unnamed Panamanians, but it was still managed by another company called Peragon. And Peragon was owned by another company called Artefix, which was owned by Ruja’s mother, Veska. And then in 2017, the ownership of Artefix was sold to an unknown man in his 20s.”
Experienced fraud investigators know that this kind of dizzying arrangement is very common, which is why fraud detection technology solutions can identify these patterns quickly and efficiently.
