Money laundering presents a thorny problem for any investigative team. Your target is hidden by design, and funds flow across international and legal boundaries to hide from sight- and prosecution. For organized crime, drug dealers and even terrorist organizations, money laundering is the lifeblood of their illegal activity.
Fortunately, big data and its associated technologies represent a powerful tool for those trying to track down money launderers and their ill-gotten gains.
According to figures from PricewaterhouseCoopers, money laundering transactions comprise 2 to 5 percent of global GDP - the equivalent of $1 trillion to $2 trillion dollars each year. Given the vast scale of the challenge, it shouldn’t be surprising that only 1 percent of these transactions are caught and seized by enforcement agencies around the world. Even with such low success rates, global spending on compliance measures targeted at money laundering are projected by WealthInsight to rise to over $8 billion this year, PwC reported.
Large gaps remain in the financial system. The report showed that internal data on possible transactions remains flawed - 33 percent of financial institutions claimed they had issues with their overall data quality, while just 50 percent of tracked incidents of money laundering were flagged by internal systemic measures.
“With the introduction of significantly increased fines, penalties and sanctions across the world, it is costing much more not to comply, and this spells out a potentially disastrous impact on corporate bottom lines,” said Imran Farooqi, financial services partner at PwC. “Technology was reported as being one of the top concerns related to anti-money laundering, preceded only by regulatory change and access to appropriately skilled staff. Many companies feel hampered by old technology that predates the digital age.”
It’s not surprising that the data needed to track money laundering incidents is often messy and blurred. After all, criminals target the weak spots and opaque subsections of financial institutions and the global financial network by design. Increasingly, money laundering has moved beyond the traditional realm of financial institutions into other industries such as digital payment services or even social media.
“We are also seeing greater use of big data technology to link together the different silos of customer information within a bank,” said Chrisol Correia, Director of Global Anti-Money Laundering at LexisNexis Risk Solutions in an interview with Fintech Innovation about the challenges of tackling AML in Singapore. He cites the benefits of combining all the information on a single party in one place rather than fracturing it across silos or different business lines.
For instance, a single individual might have a personal account in one nationality or jurisdiction, a corporate account in another and accounts behind shell companies or alternative identities in a third. Traditionally, those accounts might be fractured across different internal teams, but a data-driven approach can unify them into a single view that enables superior risk assessment.
Criminals don’t wait for you to catch up, so you need solutions that can run analytics in real time. With Visallo, you get the online streaming analytic capabilities necessary to spot red flags as they arise, rather than weeks or months later. Visallo combines multiple data streams, including public records and proprietary sources, to give a cross-section view of each actor and transaction.
This is a lot of information to process, so Visallo’s UI helps you parse, sort and combine data into coherent visualizations. Where criminals might seek to slip through the gaps between financial institutions and technology, Visallo’s analytics engine tightens the holes in that net and catches incidents as they happen.
Contact us to schedule a demo and see why our system is right for you.