Even more shocking is that despite this high level of spending, it does not appear to be working. Over the last decade, 90% of European banks have been fined for AML-related offences; globally, banks have been fined approximately $26 billion over the last 10 years.
In this backdrop where billions are being spent by rich countries to counter money laundering and it's too little success, one wonders how the not-so-rich - a country like Pakistan which is estimated to be losing $10 billion every year due to money laundering would be able to cope with the enormous challenge.
The good news is, to cut costs and enhance efficiency in efforts to detect human traffickers, narcotics and arms sales, terrorist payments, and the money laundering that fuels these activities financial institutions are being encouraged to take the leap and use Artificial Intelligence (AI) and machine learning for the purpose.
Taking such a leap should not an un-surmountable problem for Pakistan because we are already experimenting with AI and machine learning. The other day news reports said that Pakistan has introduced the first-ever business robot journalist, Dante, who writes and publishes a comprehensive report on stocks traded at the Pakistan Stock Exchange (PSX) within a few seconds after the market's closure.
Simultaneously, the AI based content writing software develops a video on share trading and gets it uploaded on YouTube.
The robot journalist is said to be capable of doing sports and weather reporting as well. Later, it is expected to be equipped to report on other sectors like health, education and culture. The financial sector, especially the securities brokerage houses and asset management firms, could also hire the robot to write comprehensive financial reports to seek guidance on taking investment and divestment decisions every morning.
It is claimed that it could also help experts and analysts take better decisions and reduce their workload significantly, at securities brokerage houses, asset management companies and in other sectors in the financial industry, as they had to visit a number of websites and read all the newspapers to take their investment and divestment decisions before PSX began trading at 9:30am on working days.
Those who are engaged in making use of AI in Pakistan would surely profit from an article (How AI is transforming the fight against money laundering) published on 17 Jan 2019 as part of World Economic Forum Annual Meeting in which authors Ellen Zimiles, Managing Director and Financial Services Advisory and Compliance Segment leader, Navigant Consulting Inc. and Tim Mueller, Managing Director and Financial Services Advisory and Compliance, Navigant Consulting Inc. have discussed in some details how to cut costs and improve efficiency of efforts to curb money laundering using AI.
According to these two gentlemen, regulators are now encouraging financial institutions to experiment, and to use the power of AI and machine learning to detect suspicious activities.
To implement these AI solutions successfully, subject matter experts are to be integral to the process to translate the problem, identify the challenges to solving it, and find the best solution to address the issues in this area.
The AI and machine learning are said to slash costs mainly by reducing false positives in monitoring systems and redirecting the efforts of human experts to other, more productive areas of suspicious activity.
Until recently, financial services have understandably not taken full advantage of AI solutions because of concerns with the so-called "black box" models, i.e., the model performs functions that are not transparent to the end user. If the bank does not understand how its technology is monitoring for financial crime, it cannot explain how it is complying with regulations to its regulators.
Still regulators globally are encouraging the use of AI. Indeed, the Monetary Authority of Singapore released a set of principles in November 2018 to promote fairness, ethics, accountability and transparency in the use of AI technologies in finance.
However, every bank transaction must be screened to see if the entities involved are on a list of known criminals or terrorists. Even the best screening systems produce a high-rate of false positives that must be dispositioned by a human reviewer, by either clearing the alert, or escalating it for further review.
Using supervised learning, humans can train the model to deal with new alerts using previously dispositioned sanctions alerts. Subject matter experts then test the model using new alerts to see how it performs. Based on those findings, humans optimize the model so that eventually it can perform the first level of review faster and with more accuracy than its human counterparts. The subject matter experts are involved in every step, allowing them to explain and justify the technology to the regulators.
Humans continue to be an integral part of the process after a model is put into operation. AI can perform the function of a Level 1 reviewer. The second, human, Level 1 reviewer checks the decisions of the model. After the model proves that it deals with alerts accurately, it will review all Level 1 alerts with only a sample tested by a human. This achieves maximum effectiveness (in terms of the accuracy of alert dispositioning) and efficiency (banks can deploy their human sanctions experts in other, more complex areas).
Already, AI systems are capable of performing link analyses, drawing inferences by identifying entities that are parties to suspect transactions. AI systems are also said to be gathering and analysing data from public sources, including from social networking sites, to help establish risk ratings for particular customers.
AI systems can also spot novel activities of terrorists and criminals. Indeed, criminals are constantly developing new methods of hiding their activity. AI can be used to identify new behaviours that would alert the financial institution to investigate.
With traditional monitoring systems, banks typically segment their customers by their industry, the type of business, size, as well as other factors. They apply rules that have worked historically for businesses in those segments. The problem with this approach is said to be that these segments do not consistently represent groups of entities with consistent transaction behaviour.
The AI system deals with transactions without regard for traditional categories. Instead, it analyses transactions, observed patterns, and creates new and more relevant segments, placing customers in them based on their behaviour. A segment, for instance, might include entities that engage in large wire credit transactions, have high-frequency counterparties, and a large number of unique originators.
If a customer executed transactions that were outside of the normal parameters for their segment, they would be subject to further analysis, including, potentially, investigations by humans.
Banks do not have to rip out and replace existing computer monitoring systems because the new technologies complement and enhance their legacy systems. At the same time, banks do not have to go to the trouble and expense of building massive teams of computer scientists specializing in AI. Powerful new machine learning technologies are said to be available today. The authors have urged banks to use subject matter experts to identify a pain point, apply AI technology, reap the rewards, then move on to the next problem.
There's said to be a downside to being too cautious. Today, regulators have given financial institutions the permission and encouragement to experiment, as long as it is done in a responsible manner. In the not too distant future, AI technologies are expected to be considered best practice.