$4 trillion is laundered every year.

About this white paper

Less than 1 percent of the laundered money is confiscated every year by the authorities – a clear indication that current AML (anti-money-laundering) systems are no longer fit for purpose. This poses a serious problem for banks, with their legal obligation to prevent money laundering, and for regulators whose credibility is at stake.

While better AML tools incorporating machine-learning algorithms exist, adoption has been slow due to concerns about explaining how they work. But forward-looking regulators in France, Germany, the UK, US and Singapore are beginning to recognize their potential. Given the scale of the criminal activity, it is probable more regulators will follow suit, accelerating the transition from current rules-based monitoring systems to real-time monitoring using machine-learning models.

Download this white paper to understand:

• What is driving the shift among regulators
• How explainable machine learning catches more financial crime
• The need for joined-up risk control
• How you can make the transition to machine-learning AML at your own pace