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Analytics and AI needed to stem UBS-style fraud, claims expert

Laurent Colombant, Continuous Compliance and Fraud solutions manager, SAS

The recent tax evasion scandal that has beset Swiss banking giant UBS shines a light on how deep rooted and institutionalised fraud can become, an anti-fraud software expert has claimed.
Last week UBS was fined €3.7bn (£3.2bn) after a court in Paris found that the bank had illegally helped French clients conceal billions of euros from the country’s tax authorities between 2004 and 2012, a verdict that the bank has challenged and is appealing against.
The combination of supplier fraud, conflicts of interests and bribery is the second-largest financial crime worldwide, said Laurent Colombant, Continuous Compliance and Fraud solutions manager, SAS. The difficulty, he said, is that fraud-related crime is often carried out by employees as they know how to get around processes and safeguards.
“The recent UBS case in France just goes to show how institutionalised fraud can become,” said Colombant. “It’s easier than you might think to hide actions like wining and dining potential customers to encourage fiscal misconduct, masking the expenditure in expenses or costs of sales. UBS has appealed the court’s decision to levy a fine. Despite its protestations of guilt, however, the bank nevertheless fired its Chief Internal Auditor, whose responsibility it is to detect this type of unethical behaviour.”
He commented that fraud-related crime is often perpetrated by employees, with or without external assistance: “They know how to bypass checks and procedures and often have a certain level of trust within the company,” he added. “That means it can be very hard to spot when your organisation is the victim – and the perpetrator – of fraud. It’s even harder to know how much fraud goes undetected. To address this crime, organisations need to equip themselves with more accurate, in-depth anti-fraud and corruption measures. The key is to be able to spot anomalies and deviations from normal behaviour quickly and catch fraudsters and their networks in the act.”
Advanced analytics and AI, he said, can help organisations achieve this by rapidly sifting through huge volumes of data to pick out potentially fraudulent activity and prevent it occurring. Machine learning capabilities can also help them to reduce false positives using self-learning algorithms that spot fraud with increasing accuracy the more they are trained.
“In 2018 the combination of supplier fraud, conflicts of interests and bribery constituted the second-largest financial crime worldwide,” said Colombant. “As anti-fraud tools become more effective, organisations need to invest in effective advanced analytics and AI to ensure they can cut fraud off at the root, before it takes hold and damages both company bottom line and reputation. Denial isn’t an option anymore.”

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