The focus on anti-money laundering (AML) and counter-terrorist financing (CTF) heated up last week with the UK announcing tough new proposals to combat financial crime. Billions of pounds of international crime money is reported to be funnelled through the UK each year, and the most recent global figures raise the number to $1.6 trillion.
The proposed crackdown puts further pressure on financial institutions to report suspicious financial activity and introduces a criminal offence of ‘illicit enrichment’ — targeting public officials who exploit their power (hot on the heels of the Panama Papers). The move would force banks, law firms and accountants to use special measures when doing business with them.
It’s yet another wave of scrutiny for industries already facing tight financial regulations. But how can companies manage their AML risk effectively when they reportedly face ‘significant intelligence gaps’ in the data needed to protect their business?
More data, more problems for AML and CTF?
With heavy fines, it’s clear many AML and CTF programs aren’t satisfying the regulators or stopping financial crime. And for compliance professionals, there’s an endless amount of public and private data and reporting burdens to contend with. Sanctions, Watchlists, PEPs, transactional activity, social media – firms are faced with a ‘big data’ analytics challenge and legacy tech that can’t keep up. Meanwhile, transactions are happening digitally and faster than ever before.
But the ‘big data’ available – large and complex data sets that traditional processing applications can’t handle – also presents an opportunity to improve transaction monitoring and KYC processes and help fight money laundering and terrorist financing. To do this, firms need to invest in real time, high quality sources and combinations of data, and advanced analytics to help reveal the relationships between individuals, transactions, and events in real time. By enriching behavioural data (e.g. the transaction) with client data (e.g. name and address) and public lists (e.g. OFAC) banks will have more power to track and identify suspicious transactions.
Machine learning can close the big data gap
Machine learning technology has allowed companies to create sophisticated surveillance platforms that use intelligent algorithms to process and make sense of all this ‘big data’. These smarter systems can learn, remember and search for new patterns across enormous behavioural data sets or links that indicate AML or CTF.
For financial institutions this means machine learning technology can advance the way they monitor and identify suspicious transaction activity in real time and ultimately track illicit financial flows. For example, our transaction monitoring platform can learn the transaction behaviour of similar clients and discover transaction activity of clients with similar traits (business type, geographic, location, age, etc.) It can also continuously analyse false-positive alerts and learn common predictors – to reduce the vast amount of false positives banks and payments companies are frustrated with.
Next-gen compliance already here
Sophisticated data science and better technology can help companies rely less on personal judgement and more on facts to help their AML and CTF efforts. It also allows firms to take a ‘risk-based approach’ – where they can take action based on the level of risk for their business, and greatly lower compliance costs.
The crackdown on AML compliance will undoubtedly continue – so it’s a question of when, not if, companies need to invest in better data and technologies to keep pace with the regulators – and help fight financial crime.