Process reviews required to prevent financial crime

By Laurent Colombant, continuous monitoring solution manager, SAS

28 July 2020

Combatting financial crime has long been an arduous process. Conducting face to face investigations on the ground far and wide and rifling through masses of paperwork places a huge burden on the time and resources of financial organisations.
 
These traditional forensic techniques may produce results in the end, but not without considerable difficulty in establishing the facts of the case. As with any people-based investigation, the possibility exists that the truth may never be uncovered. That’s bad news when it comes to protecting the company from future threats.
 
Similarly, if data is not analysed quickly and effectively in organisations, it can be stored away, leaving the crime undetected for months. A better approach would be real-time analysis of all data passing through a system, to spot anomalies or suspicious data. In an ideal world, prevention and prediction would stop fraud before it even began. But that’s not possible… is it?
 
Keeping data under a watchful eye
 
Analytics offers that potential. Heavily regulated banks and insurance companies have already started to turn to data analytics to detect money laundering and terrorist financing. Anti-money laundering regulations provided the original impetus. However, organisations have now extended those techniques to fraud detection and due diligence for new customers under the KYC umbrella.
 
However, a recent SAS survey found that machine learning and artificial intelligence are very rarely used as fraud detection techniques by nonfinancial corporations or in the public sector. In fact, 43 percent of the 850 companies surveyed by SAS in 2019 still rely solely on manual controls to fight procurement fraud. And only nine percent use AI techniques to do so (How smart analytics can stop procurement fraud). This is unfortunate, because these approaches are very helpful in identifying risky third parties such as suppliers, business partners and intermediaries, and therefore preventing fraud.
 
These approaches are key to predicting fraud before it happens, and not just detecting problems after the event. They enable businesses to surface anomalies for review before they make any payments or sign contracts with suppliers. Machine learning enables systems to improve automatically over time and become more efficient. It uses both structured and unstructured modelling techniques, such as clustering and link analysis, to provide highly effective flagging of potential issues.
 
You don’t need luck on your side
 
As Alix Stuart points out in Compliance Week:
 
“Ferreting out fraud is never easy. For years, compliance professionals have relied on the bravery of whistleblowers, the missteps of perpetrators, and a good amount of luck to uncover wrongdoing both inside and outside their companies.
 
“That may all be changing with the advent of the big data era. These days, high powered analytic tools can crunch through enormous quantities of structured and unstructured data, producing an exponentially greater set of comparisons within the data on any given afternoon than a human could in a month. As companies refine their tools and techniques, catching fraudsters may soon become more a matter of learning how to properly interrogate a computer program rather than putting gumshoes on the case."
 
Analytics help organisations get to the bottom of it
 
It is, therefore, unsurprising to find that this data-based approach is now becoming more mainstream among regulatory agencies. The SEC and DOJ in the US, the Serious Fraud Office in the UK and Agence Française Anticorruption have moved on from relying solely on the knowledge and skills of experienced investigators to enforce their regulations, such as the Foreign Corrupt Practices Act (FCPA).
 
They now also apply algorithms to the data of audited companies to give them faster and better insights. Analytics also allows regulators to test the efficiency of the company’s internal controls, making fraud less likely in the future.
 
A hybrid approach offers long-term protection
 
Businesses must enrich  their rules-based with analytical and machine learning approaches to make sure they tighten the net on potential fraudsters. AI techniques enable you to identify unknown but emergent fraud modus operandi and significantly improve the efficiency of investigations. Solutions that can distinguish dodgy behaviour from errors act as good deterrents.
 
To learn more about how combining traditional controls and processes with automation, AI and other continuous monitoring analytics tools can help detect and combat financial crime, read SAS’ paper Procurement integrity powered by continuous monitoring.

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