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Hitting the Books: How Southeast Asia’s largest bank uses AI to fight financial fraud
Yes, robots are coming to take our jobs. That’s a good thing, we should be happy they are because those jobs they’re taking kinda suck. Do you really want to go back to the days of manually monitoring, flagging and investigating the world’s daily bank transfers in search of financial fraud and money laundering schemes? DBS Bank, Singapore’s largest financial institution, certainly doesn’t. The company has spent years developing a cutting-edge machine learning system that heavily automates the minutia-stricken process of “transaction surveillance,” freeing up human analysts to perform higher level work while operating in delicate balance with the antique financial regulations that bound the industry. It’s fascinating stuff. Working with AI by Thomas H. Davenport and Steven M. Miller is filled with similar case studies from myriad tech industries, looking at commonplace human-AI collaboration and providing insight into the potential implications of these interactions.
Excerpted from Working with AI: Real Stories of Human-Machine Collaboration by Thomas H. Davenport and Steven M. Miller. Reprinted with permission from The MIT Press. Copyright 2022.
DBS Bank: AI-Driven Transaction Surveillance
Since the passage of the Bank Secrecy Act, also known as the Currency and Foreign Transactions Reporting Act, in the US in 1970, banks around the world have been held accountable by governments for preventing money laundering, suspicious cross-border flows of large amounts of money, and other types of financial crime. DBS Bank, the largest bank in Singapore and in Southeast Asia, has long had a focus on anti-money laundering (AML) and financial crime detection and prevention. According to a DBS executive for compliance, “We want to make sure that we have tight internal controls within the bank so the perpetrators, money launderers, and sanctions evaders do not penetrate into the financial system, either through our bank, through our national system, or internationally.”
The Limitations of Rule-Based Systems for Surveillance Monitoring
As at other large banks, the area of DBS that focuses on these issues, called “transaction surveillance,” has taken advantage of AI for many years to do this type of work. The people in this function evaluate alerts raised by a rule-based system. The rules assess transaction data from many different systems across the bank, including those for consumers, wealth management, institutional banking, and their payments. These transactions all flow through the rule-based system for screening, and the rules flag transactions that match conditions associated with an individual or entity doing suspicious transactions with the bank—those involving a potential money laundering event, or another type of financial fraud. Rule-based systems—in the past known as “expert systems” — are one of the oldest forms of AI, but they are still widely used in banking and insurance, as well as in other industries.
At DBS and most other banks across the world, rule-based financial transaction surveillance systems of this sort generate a large number of alerts every day. The primary shortcoming of rule-based surveillance systems is that most — up to 98 percent — of the alerts generated are false positives. Some aspect of the transaction triggers a rule that leads the transaction to be flagged on the alert list. However, after follow-up investigation by a human analyst, it turns out that the alerted transaction is actually not suspicious.
The transaction surveillance analysts have to follow up on every alert, looking at all the relevant transaction information. They must also consider the profiles of the individuals involved in the transaction, their past financial behaviors, whatever they have declared in “know your customer” and customer due diligence documents, and anything else the bank might know about them. Following up on alerts is a time-intensive process.
If the analyst confirms that a transaction is justifiably suspicious or verified as fraud, the bank has a legal obligation to issue a Suspicious Activity Report (SAR) to the appropriate authorities. This is a high-stakes decision, so it is important for the analyst to get it right: if incorrect, law-abiding bank customers could be incorrectly notified that they are being investigated for financial crimes. On the other side, if a “bad actor” is not detected and reported, it could lead to problems related to money laundering and other financial crimes.
For now at least, rule-based systems can’t be eliminated because the national regulatory authorities in most countries still require them. But DBS executives realized there are many additional sources of internal and external information available to them that, if used correctly, could be applied to automatically evaluate each alert from the rule-based system. This could be done using ML, which can deal with more complex patterns and make more accurate predictions than rule-based systems.
Using the New Generation of AI Capabilities to Enhance Surveillance
A few years ago, DBS started a project to apply the new generation of AI/ML capabilities in combination with the existing rule-based screening system. The combination would enable the bank to prioritize all the alerts generated by the rule-based system according to a numerically calculated probability score indicating the level of suspicion. The ML system was trained to recognize suspicious and fraudulent situations from recent and historical data and outcomes. At the time of our interviews, the new ML-based filtering system had been in use for just over one year. The system reviews all the alerts generated by the rule-based system, assigns each alert a risk score, and categorizes each alert into higher-, medium-, and lower-risk categories. This type of “post-processing” of the rule-based alerts enables the analyst to decipher which ones to prioritize immediately (those in the higher- and medium-risk categories) and which ones can wait (those in the lowest-risk category). An important capability of this ML system is that it has an explainer that shows the analyst the evidence used in making the automated assessment of the probability that the transaction is suspicious. The explanation and guided navigation given by the AI/ML model helps the analyst make the right risk decision.
DBS also developed other new capabilities to support the investigation of alerted transactions, including a Network Link Analytics system for detecting suspicious relationships and transactions across multiple parties. Financial transactions can be represented as a network graph showing the people or accounts involved as nodes in the network and any interactions as the links between the nodes. This network graph of relationships can be used to identify and further assess suspicious patterns of financial inflows and outflows.
In parallel, DBS has also replaced a labor-intensive approach to investigation workflow with a new platform that automates for the analyst much of the support for surveillance-related investigation and case management. Called CRUISE, it integrates the outputs of the rule-based engine, the ML filter model, and the Network Link Analytics system.
Additionally, the CRUISE system provides the analyst with easy and integrated access to the relevant data from across the bank needed to follow up on the transactions the analyst is investigating. Within this CRUISE environment, the bank also captures all the feedback related to the analyst’s work on the case, and this feedback helps to further improve DBS’s systems and processes.
Impact on the Analyst
Of course, these developments make analysts much more efficient in reviewing alerts. A few years ago, it was not uncommon for a DBS transaction surveillance analyst to spend two or more hours looking into an alert. This time included the front-end preparation time to fetch data from multiple systems and to manually collate relevant past transactions, and the actual analysis time to evaluate the evidence, look for patterns, and make the final judgment as to whether or not the alert appeared to be a bona fide suspicious transaction.
After the implementation of multiple tools, including CRUISE, Network Link Analytics, and the ML-based filter model, analysts are able to resolve about one-third more cases in the same amount of time. Also, for the high-risk cases that are identified using these tools, DBS is able to catch the “bad actors” faster than before.
Commenting on how this differs from traditional surveillance approaches, the DBS head of transaction surveillance shared the following:
Today at DBS, our machines are able to gather the necessary support data from various sources across the bank and present it on the screen of our analyst. Now the analyst can easily see the relevant supporting information for each alert and make the right decision without searching through sixty different systems to get the supporting data. The machines now do this for the analyst much faster than a human can. It makes the life of the analysts easier and their decisions a lot sharper.
In the past, due to practical limitations, transaction surveillance analysts were able to collect and use only a small fraction of the data within the bank that was relevant to reviewing the alert. Today at DBS, with our new tools and processes, the analyst is able to make decisions based on instant, automatic access to nearly all the relevant data within the bank about the transaction. They see this data, nicely organized in a condensed manner on their screen, with a risk score and with the help of an explainer that guides them through the evidence that led to the output of the model.
DBS invested in a skill set “uplift” across the staff who were involved in creating and using these new surveillance systems. Among the staff benefiting from the upskilling were the transaction surveillance analysts, who had expertise in detecting financial crimes and were trained in using the new technology platform and in relevant data analytics skills. The teams helped design the new systems, beginning with the front-end work to identify risk typologies. They also provided inputs to identify the data that made most sense to use, and where automated data analytics and ML capabilities could be most helpful to them.
When asked how the systems would affect human transaction analysts in the future, the DBS compliance executive said:
Efficiency is always important, and we must always strive for higher levels of it. We want to handle the transaction-based aspects of our current and future surveillance workload with fewer people, and then reinvest the freed- up capacity into new areas of surveillance and fraud prevention. There will always be unknown and new dimensions of bad financial behavior and bad actors, and we need to invest more time and more people into these types of areas. To the extent that we can, we will do this through reinvesting the efficiency gains we achieve within our more standard transaction surveillance efforts.
The Next Phase of Transaction Surveillance
The bank’s overall aspiration is for transaction surveillance to become more integrated and more proactive. Rather than just relying on alerts generated from the rule-based engine, executives want to make use of multiple levels of integrated risk surveillance to monitor holistically from “transaction to account to customer to network to macro” levels. This combination would help the bank find more bad actors, and to do so more effectively and efficiently. The compliance executive elaborated:
It is important to note that money launderers and sanctions evaders are always finding new ways of doing things. Our people need to work with our technology and data analytics capabilities to stay ahead of these emerging threats. We want to free up the time our people have been spending on the tedious, manual aspects of reviewing alerts, and use that time to keep pace with the emerging threats.
Human analysts will continue to play an important role in AML transaction surveillance, though the way they use their time and their human expertise will continue to evolve.
The compliance executive also shared a perspective on AI: “It’s really augmented intelligence, rather than automated AI in risk surveillance. We do not think we can remove human judgment from the final decisions because there will always be a subjective element to evaluations of what is and is not suspicious in the context of money laundering and other financial crimes. We cannot eliminate this subjective element, but we can minimize the manual work that the human analyst does as part of reviewing and evaluating the alerts.”
Lessons We Learned from This Case
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An automated system that generates large numbers of alerts most of which turn out to be false positives does not save human labor.
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Multiple types of AI technology (in this case, rules, ML, and Network Link Analytics) can be combined to improve the capabilities of the system.
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Companies may not reduce the number of people doing a job even when the AI system substantially improves the efficiency of doing it. Rather, employees can use the freed-up time to work on new and higher-valued tasks in their jobs.
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Because there will always be subjective elements in the evaluation of complex business transactions, human judgment may not be eliminated from the evaluation process.
9 Books Featuring Queer Disaster Magicians
Are you tired of your magicians being wise old sages and beatific fonts of knowledge? Let me fix that. Here are nine books where the main characters are both magical and absolute disasters. The fact that we have such an incredible selection of these sorts of dumbasses that also happen to be queer is just the best…
Must-Read Parapsychology Books For Paranormal Investigators
Hitting the Books: What if ‘Up’ but pigeons?
We all have those thoughts, the ones that come to us in the small hours of the night. Who am I? Why are we here? What if my cellphone ran on vacuum tubes instead? Randall Munroe has the answer to, well, only one of those questions, but also the answers to a whole bunch of others collected together into What If? 2: Additional Serious Scientific Answers to Absurd Hypothetical Questions. Yes, that is a T-Rex eating an airplane. In the excerpt below Munroe examines what it would take to haul an average sized human in a chair over Australia’s tallest skyscraper, using only the power of pigeons. Lots and lots of pigeons.
Excerpted from What If? 2 by Randall Munroe. Copyright © 2022 by Randall Munroe. Excerpted by permission of Riverhead, an imprint and division of Penguin Random House LLC, New York. All rights reserved. No part of this excerpt may be reproduced or reprinted without permission in writing from the publisher.
How many pigeons would it require in order to lift the average person and a launch chair to the height of Australia’s Q1 skyscraper?
In a 2013 study, researchers at the Nanjing University of Aeronautics and Astronautics led by Ting Ting Liu trained pigeons to fly up to a perch while wearing a weighted harness. They found that the average pigeon in their study could take off and fly upward while carrying 124 grams, about 25 percent of its body weight.
The researchers determined that the pigeons could fly better if the weights were slung below their bodies, rather than on their backs, so you would probably want pigeons to lift your chair from above rather than support it from below.
Let’s suppose your chair and harnesses weigh 5 kilograms and you weigh 65 kilograms. If you used the pigeons from the 2013 study, it would take a flock of about 600 of them to lift your chair and fly upward with it.
Unfortunately, flying with a load is a lot of work. The pigeons in the 2013 study were able to carry a load 1.4 meters upward to a perch, but they probably wouldn’t have been able to fly too much higher than that. Even unencumbered pigeons can only maintain strenuous vertical flight for a few seconds. One 1965 study measured a climb rate of 2.5 m/s for unencumbered pigeons,* so even if we’re being optimistic, it seems unlikely that pigeons could lift your chair more than 5 meters.†
No problem, you might think. If 600 pigeons can lift you the first 5 meters, then you just need to bring another 600 along with you, like the second stage of a rocket, to carry you the next 5 meters when the first flock gets tired. You can bring another 600 for the 5 meters after that and so on. The Q1 is 322 meters high, so about 40,000 pigeons should be able to get you to the top, right?
No. There’s a problem with this idea.
Since a pigeon can carry only a quarter of its body weight, it takes four flying pigeons to carry one resting pigeon. That means each “stage” will need at least four times as many pigeons as the one above it. Lifting one person may only take 600 pigeons, but lifting one person and 600 resting pigeons would take another 3,000 pigeons.
This exponential growth means that a 9-stage vehicle, able to lift you 45 meters, would need almost 300 million pigeons, roughly equal to the entire global population. Reaching the halfway point would require 1.6 × 1025 pigeons, which would weigh about 8 × 1024 kilograms—more than the Earth itself. At that point, the pigeons wouldn’t be pulled down by the Earth’s gravity—the Earth would be pulled up by the pigeons’ gravity.
The full 65-stage craft to reach the top of the Q1 would weigh 3.5 × 1046 kilograms. That’s not just more pigeons than there are on Earth, it’s more mass than there is in the galaxy.
You could make things more efficient by reusing pigeons. In the 2013 study, the researchers gave the pigeons 30 seconds to rest on the perch before bringing them down for another trial. If each “stage” is two seconds, and pigeons are refreshed after 30 seconds, you could fly arbitrarily high with a 15-stage craft—but that would still require trillions of pigeons.
A better approach might be to avoid carrying the pigeons with you. After all, pigeons can get up to the top of the skyscraper themselves, so you might as well send them ahead to wait for you there instead of having their friends carry them up with you. If you could train them well enough, you could have them glide along at the appropriate height, then grab you and tug you upward for a few seconds when you reach their altitude. Keep in mind that pigeons can’t grab and carry things with their feet, so they’d need little harnesses with aircraft-carrier-style hooks to intercept you.
With this arrangement, it’s possible you could fly yourself to the top of the tower with just a few tens of thousands of well-trained pigeons. You should probably make sure you have some kind of safety system that will keep you from plunging to your demise every time a falcon flies by and spooks the pigeons.
The craft wouldn’t just be more dangerous than an elevator, it would also be a lot harder to pick your destination. You might plan to go to the top of the Q1, but once you take off… you’ll be completely under the control of anyone with a bag of seeds.
Hitting the Books: How to uncover the true nature of the multiverse
It’s difficult to describe the state of the universe’s affairs back when the whole of everything was compressed to a size slightly smaller than the period at the end of this sentence — on account that the concepts of time and space literally didn’t yet apply. But that challenge hasn’t stopped pioneering theoretical astrophysicist, Dr. Laura Mersini-Houghton, from seeking knowledge at the edge of the known universe and beyond. In her new book, Before the Big Bang, Mersini-Houghton recounts her early life in communist Albania, her career as she rose to prominence in the male-dominated field of astrophysics and discusses her research into the multiverse which could fundamentally rewrite our understanding of reality.
Excerpted from Before The Big Bang: The Origin of the Universe and What Lies Beyond by Laura Mersini-Houghton. Published by Mariner Books. Copyright © 2022 by Laura Mersini-Houghton. All rights reserved.
Scientific investigations of problems like the creation of the universe, which we can neither observe nor reproduce and test in a lab, are similar to detective work in that they rely on intuition as well as evidence. Like a detective, as pieces of the puzzle start falling into place, researchers can intuitively sense the answer is close. This was the feeling I had as Rich and I tried to figure out how we could test our theory about the multiverse. Rationally, it seemed like a long shot, but intuitively, it seemed achievable.
Finally, a potential solution hit me. I realized that the key to testing and validating this theory was hidden in quantum entanglement — because decoherence and entanglement were two sides of the same coin! I could rewind the creation story all the way back to its quantum-landscape roots, when our wave-universe was entangled with others.
I already knew that the separation — the decoherence — of the branches of the wave function of the universe (which then become individual universes) was triggered by their entanglement with the environmental bath of fluctuations. Now I wondered if we could calculate and find any traces of this early entanglement imprinted on our sky today.
This might sound like a contradiction. How could our universe possibly still be entangled with all the other universes all these eons after the Big Bang? Our universe must have separated from them in its quantum infancy. But as I wrestled with these issues, I realized that it was possible to have a universe that had long since decohered but that also retained its infantile “dents” — minor changes in shape caused by the interaction with other surviving universes that had been entangled with ours during the earliest moments — as identifiable birthmarks. The scars of its initial entanglement should still be observable in our universe today.
The key was in the timing. Our wave-universe was decohering around the same time as the next stage, the particle universe, was going through its own cosmic inflation and coming into existence. Everything we observe in our sky today was seeded from the primordial fluctuations produced in those first moments, which take place at the smallest of units of measurable time, far less than a second. In principle, during those moments, as entanglement was being wiped out, its signatures could have been stamped on the inflaton and its fluctuations. There was a chance that the sort of scars that I was imagining had formed during this brief period. And if they had, they should be visible in the skies.
Understanding how scars formed from entanglement is less complicated than you might imagine. I started by trying to create a mental picture of the entanglement’s scarring of our sky. I visualized all the surviving universes from the branches of the wave function of the universe, including ours, as a bunch of particles spread around the quantum multiverse. Because they all contain mass and energy, they interact with (pull on) one another gravitationally, just as Newton’s apple had its path of motion curved by interacting with the Earth’s mass, thus guiding it to the ground. However, the apple was also being pulled on by the moon, the sun, all the other planets in our solar system, and all the stars in the universe. The Earth’s mass has the strongest force, but that does not mean these other forces do not exist. The net effect that entanglement left on our sky is captured by the combined pulling on our universe by other infant universes. Similar to the weak pulling from stars on the famous apple, at present, the signs of entanglement in our universe are incredibly small relative to the signs from cosmic inflation. But they are still there!
I will admit it… I was excited by the mere thought that I potentially had a way to glimpse beyond our horizon and before the Big Bang! Through my proposal of calculating and tracking entanglement in our sky, I may very well have pinned down, for the very first time, a way of testing the multiverse. What thrilled me most about this idea was its potential for making possible what for centuries we thought was impossible — an observational window to glimpse in space and in time beyond our universe into the multiverse. Our expanding universe provides the best cosmic laboratory for hunting down information about its infancy because everything we observe at large scales in our universe today was also present at its beginning. The basic elements of our universe do not vanish over time; they simply rescale their size with the expansion of the universe.
And here is why I thought of using quantum entanglement as the litmus test for our theory: Quantum theory contains a near-sacred principle known as “unitarity,” which states that no information about a system can ever be lost. Unitarity is a law of information conservation. It means that signs of the earlier quantum entanglement of our universe with the other surviving universes must still exist today. Thus, despite decoherence, entanglement can never be wiped from our universe’s memory; it is stored in its original DNA. Moreover, these signs have been encoded in our sky since its infancy, since the time the universe started as a wave on the landscape. Traces of this earlier entanglement would simply stretch out with the expansion of the universe as the universe became a much larger version of its infant self.
I was concerned that these signatures, which have been stretched by inflation and the expansion of the universe, would be quite weak. But on the basis of unitarity, I believed that however weak they were, they were preserved somewhere in our sky in the form of local violations or deviations from uniformity and homogeneity predicted by cosmic inflation.
Rich and I decided to calculate the effect of quantum entanglement on our universe to find out if any traces were left behind, then fast-forward them from infancy to the present and derive predictions for what kind of scars we should be looking for in our sky. If we could identify where we needed to look for them, we could test them by comparing them with actual observations.
Rich and I started on this investigation with help from a physicist in Tokyo, Tomo Takahashi. I first got to know Tomo at UNC Chapel Hill in 2004 when we overlapped by one year. He was a postdoc about to take a faculty position in Japan, and I had just arrived at UNC. We enjoyed interacting, and I saw the high standards Tomo maintained for his work and his incredible attention to detail. I knew he was familiar with the computer simulation program that we needed in order to compare the predictions based on our theory with actual data about matter and radiation signatures in the universe. In 2005, I called Tomo, and he agreed to collaborate with us.
Rich, Tomo, and I decided that the best place to begin our search was in the CMB — cosmic microwave background, the afterglow from the Big Bang. CMB is the oldest light in the universe, a universal “ether” permeating the entire cosmos throughout its history. As such, it contains a sort of exclusive record of the first millisecond in the life of the universe. And this silent witness of creation is still all around us today, making it an invaluable cosmic lab.
The energy of the CMB photons in our present universe is quite low; their frequencies peak around the microwave range (160 gigahertz), much like the photons in your kitchen microwave when you warm your food. Three major international scientific experiments — the COBE, WMAP, and Planck satellites (with a fourth one on the way), dating from the 1990s to the present — have measured the CMB and its much weaker fluctuations to exquisite precision. We even encounter CMB photons here on Earth. Indeed, seeing and hearing CMB used to be an everyday experience in the era of old TV sets: when changing channels, the viewer would experience the CMB signal in the form of static — the blurry, buzzing gray and white specks that appeared on the TV screen.
But if our universe started purely from energy, what can we see in the CMB photons that gives us a nascent image of the universe? Here, quantum theory, specifically Heisenberg’s uncertainty principle, provides the answer. According to the uncertainly principle, quantum uncertainty, displayed as fluctuations in the initial energy of inflation, is unavoidable. When the universe stops inflating, it is suddenly filled with waves of quantum fluctuations of the inflaton energy. The whole range of fluctuations, some with mass and some without, are known as density perturbations. The shorter waves in this spectrum, those that fit inside the universe, become photons or particles, depending on their mass (reflecting the phenomenon of wave-particle duality).
The tiny tremors in the fabric of the universe that induce weak ripples or vibrations in the gravitational field, what are known as primordial gravitational waves, hold information on what particular model of inflation took place. They are incredibly small, at one part in about ten billion of the strength of the CMB spectrum, and therefore are much harder to observe. But they are preserved in the CMB.
It’s Britney B*tch! Spears Joined Madonna, Mariah Carey, & Whitney Houston in Hot 100 History Books This Week Thanks to ‘Hold Me Closer’
With one of the most illustrious careers in Pop music history, Britney Spears is no stranger to seeing her name attached to some jaw-dropping Billboard records over the years.
But, thanks to her latest hit – the Elton John-led ‘Hold Me Closer’ – the ‘Baby One More Time’ beauty has been penciled in the publication’s record logs one more time.
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