QuantaVerse

LEVERAGING GLOBAL TRENDS FOR A FINTECH COMBATTING MONEY LAUNDERING MEDIA COVERAGE HIGHLIGHTS FLIP BOOK Client: QuantaVerse

TABLE OF CONTENTS: WIRED: Banks Deploy AI to Cut Off Terrorist’s Funding American Banker: Miami Vice: This Bank Using AI to Thwart Financial Crime ACAMS Today: How Artificial Intelligence can Help Overcome Challenges in Correspondent Banking Relationships Barron’s: How Wells Fargo Could Have Avoided a Scandal Forbes: Using AI to Fight Crime: The Terrorism, Trafficking And Money Laundering Links Fox News: How Artificial Intelligence is Transforming the Global Battle Against Human Trafficking ABA Bank Compliance: Using Data Science to Mitigate Bank Employee Fraud Bank Automation News: AI Tech Company QuantaVerse New Service to Fight Financial Crime Benzinga: How QuantaVerse Saves Time, Money In Fight Against Financial Crime Philadelphia Business Journal: Tech Disruptors: David McLaughlin, QuantaVerse Fintech Futures: Legacy technology “not up to the challenge” to tackle AML The FCPA Blog: How AI can Nail a Bribe Paying Scrap Metal Dealer Forbes: Software Fills In For India-Based Compliance Teams In COVID Lockdown Global Investigations Review: How the Pandemic has Affected Corporate Compliance Teams Celent Analyst Report: QuantaVerse Alert Investigator: Compliance Automation to Overcome Resrouce Shortage During Covid-19 and Beyond

Broadpath

Banks Deploy AI to Cut Off Terrorists’ Funding Computers are better than humans at recognizing small patterns.

By: Issie Lapowsky | July 9, 2017 ONE THING THAT makes ISIS so hard to fight is that the terrorist network is diffuse and scattered, with small cells of operatives all over the world. Not only does this make it hard for law enforcement to predict where the group might strike next; it makes it incredibly complicated to track activity on the network—activity like banking transactions. Small sums of money flow from foreign fighter to foreign fighter, yet banks struggle to identify it within their systems.

Banks have long used anti-money laundering systems to flag suspicious activity, and in the aftermath of September 11th, they have turned to those same legacy tools to catch terror-related transactions, too. But these legacy tools are not up to the job.

They rely upon hard-coded “if-then” rules about predictably suspicious behavior. If the software spots a seven-figure transfer of funds from Miami to Bogota, for example, it knows to flag it. But as terrorist groups like ISIS recruit people internationally for smaller, targeted attacks, those tools become far less effective. There are just too many rules and possibilities to consider.

“It doesn’t take much to survive in a hostel in Belgium while waiting to be moved to another location,” says Dan Stitt, who’s spent two decades in the financial crimes industry, with stints at the Drug Enforcement Agency and the Export-Import Bank of the United States. The pattern of small transactions a terrorist in hiding makes might not raise red flags for the usual anti-money-laundering systems.

4

QUANTAVERSE ISSUU FLIP BOOK

Broadpath

Unless those systems use artificial intelligence.

Banks are increasingly turning to machine learning to mine vast quantities of bank data and find anomalies in accounts and transactions that might otherwise have gone unnoticed. “It’s a surgical approach to finding a needle in a haystack,” says Stitt, who now serves as director of financial crime analysis for the Wayne, Pennsylvania-based firm QuantaVerse, which developed the AI technology some of the world’s biggest banks use to identify money laundering, terrorist funding, and other financial crimes. The technology has already helped identify a Panamanian man the DEA called “one of the world’s most significant drug money launderers.”

The use of machine learning in this industry is still in its earliest days, and even QuantaVerse is unsure how many of its leads have actually turned out to be verifiable threats.

But financial regulatory experts have high hopes for the potential of such tools. “Machines are able to take in multiple additional data points and analyze those data points in a way that may not seem obvious to human beings,” says Kevin Petrasic, a partner at the law firm White & Case, who specializes in financial regulation.

Banks Must Help Find Criminals Ever since the Bank Secrecy Act of 1970, banks have been required to assist government agencies in detecting money laundering. Software has helped automate that process somewhat. Yet, the process is beset by false positives, in which the system flags behavior that is not actually criminal. A recent Dow Jones survey of more than 800 anti-money laundering professionals found that nearly half of them said false positive alerts hurt their confidence in the accuracy of the screening process.

Still, to comply with governments, banks invest billions of dollars in these systems every year. “That’s billions invested—a lot of humans investigating the flags a legacy system will generate, and a large ma jority of those turn out not to be financial crimes,” says David McLaughlin, who founded QuantaVerse in 2014. “Meanwhile, the real financial crimes are going unnoticed.”

The challenge, particularly for banks looking to stop the flow of money to foreign fighters, is that there are infinite possible permutations of transactions to hand code into a rules-based system. A person looking to join ISIS might take $80 out of an ATM in Brussels, receive a wire transfer in Algeria, and use a credit card in Lebanon. He might take out a payday loan or transfer money to family. On their own, these incremental activities might not trigger suspi-

QUANTAVERSE ISSUU FLIP BOOK

5

Broadpath

cion, but taken together, they create a pattern that a machine might identify as fishy.

“Any investigator is going to go for the shiny object in front of them,” Stitt says. “If I have an alert for $1 million for a wire transfer to Mexico or a series of transactions for $80 in Belgium, what am I going to look at? That’s where the system has failed on an investigative level.”

Pattern Recognition Unlike these traditional system’s QuantaVerse software learns these predictors on its own. The company’s team of data scientists trained its algorithms on several years’ worth of data from one of the top five biggest banks in the world, whose name the company is contractually prohibited from sharing publicly. With Stitt’s input, the team trained the system in what good and bad behavior looks like so that the system could begin learning and identifying that behavior without human oversight.

These judgment calls, Stitt says, are based on a combination of factors, including how quickly money moves around, where it’s moving, and how much is being transferred. But they also look for clues like anomalies in invoicing number sequences. If a criminal group is looking to launder money, it might falsify invoices to make it appear a legitimate transaction occurred, when, in fact, the money came from a drug deal or the sale of counterfeit goods. Those invoices come with their own identification numbers, and often, Stitt says, “People forget which numbers they used.” QuantaVerse’s technology can spot duplications and mistakes in the system.

QuantaVerse’s tool also looks at an account’s history to analyze pre-existing relationships it has with other accounts. The system, Stitt explains, might question a sudden transaction between a fertilizer company and the fire department if it hasn’t seen many such transactions in the past. Traditional anti-money laundering systems look at about 90-days’ worth of data. QuantaVerse’s system can analyze two to three years.

‘That’s Not Normal’ All of this was key to identifying the alleged drug trafficking ring in Panama called Grupo Wisa, a holding company which runs duty free stores in Latin American airports. QuantaVerse identified a series of invoices for large, round dollar amounts being passed back and forth between businesses that had the same owner. “When you have entities owned by the same person sending money back and forth in the amount of millions of dollars that’s not normal,” Stitt says. It looked like a straightforward money-laundering case, but

6

QUANTAVERSE ISSUU FLIP BOOK

Broadpath

Stitt says based on his experience tracking terrorist funding, it bore many of the markings of Hezbollah’s money laundering operation.

QuantaVerse reported the issue to its client. A year later, the United States DEA announced that Nidal Waked, one of the proprietors of Grupo Wisa, had been arrested at the Bogota airport on charges of money laundering. (The company, for its part, rejects the charges).

Just how big a role QuantaVerse’s tip played in catching Grupo Wisa is unclear. But even a small lead is a win for this nascent industry, which Petrasic says is growing thanks to increasing regulatory pressure in the US and abroad in the wake of the 2008 financial crisis. Of course, as with any computer system that can learn on its own, the results are only as good as the data fed into them and the human oversight and controls put on them.

As human beings slowly adapt to the sneakily ubiquitous threat of terror in our own lives, machines will need to adapt even faster to help choke it off.olution.”

QUANTAVERSE ISSUU FLIP BOOK

7

Broadpath

Miami Vice: This Bank Using AI to Thwart Financial Crime

By Penny Crosman | August 9, 2018 One of the largest banks in Florida and the largest Chilean-owned bank in the U.S. said this week it has begun using artificial intelligence software to detect financial crime.

Bci Miami is one of the first banks in the U.S. to publicly acknowledge using AI this way, when many still consider the technology to be new, risky and unsanctioned by regulators.

ous types of fraud,” said David Schwartz, president and CEO of the Florida International Bankers Association. “The things that make South Florida an attractive location for investors and people looking for a better place to live — our geography, weather, cultural diversity — attract good people and unfortunately, they also attract bad people.”

But the bank is forging ahead after previewing the tech with its U.S. and Chilean regulators.

As a result, banks in Miami are under special pressure to try to detect money laundering, human trafficking and other wrongdoing.

“Bci Miami is committed to serving our customers while working with regulators to continue to maintain a strong, compliant anti money laundering program,” said Michele Fernandez, head of compliance for Bci Miami. “We now have a more efficient risk management strategy in place.”

The bank is using three software modules from QuantaVerse. Other vendors of AI software that can be used to detect money laundering and other financial crime include Thetaray, Merlon Intelligence, ZestFinance, Ayasdi, Quantexa, and IBM Watson.

If you wanted to test AI for catching financial crime, you could find few places richer with opportunity than Miami, which has been dubbed “the organized crime capital of America.”

There were 31,167 reports of identity theft in Florida in 2017, the second highest state in the nation, according to statistics by the Federal Trade Commission. Those stolen identities are used in many different types of fraud.

“It seems that we are a hotbed for vari8

QUANTAVERSE ISSUU FLIP BOOK

One module by QuantaVerse is called PreTMS Entity Resolution & Risk Scoring, which is intended to reduce false positive alerts by classifying the risk of each transacting party. (TMS stands for Transaction Monitoring System.) This software works to find missing information about people and companies and clean up information.

“It’s an analytics tool to understand the risks the entities are presenting to the bank,” said David McLaughlin, CEO and founder of QuantaVerse.

Broadpath

Handling a transaction for General Electric, for instance, would be considered low risk, especially for money laundering. A transaction conducted on behalf of a company whose owner was indicted for financial crimes in a previous job would be labeled high risk.

The second piece of software is called Alert Investigator. It helps human compliance officers investigate financial crime alerts.

“When a human gets an alert from a transaction monitoring system, they have to pull data from different files, search open source data, and put together an investigative case that allows someone to determine whether this needs to be reported as suspicious activity,” McLaughlin said. “We can automate 70% of that investigation. We can provide the investigator an automated financial crime report with natural language generation that includes a recommendation of whether to file a suspicious activity report or not or if further investigation is recommended.”

The third component is a false negative identifier. This looks at all the transactions that were not alerted and analyzing them to make sure nothing nefarious was missed.

The bank’s top goal is to improve efficiency in its investigative process as it grows. Its second goal is to catch every instance of financial crime in its organization and prevent money launderers, drug traffickers and human traffickers from using its rails.

tecting suspicious behavior in South Florida.

FINRA has targeted certain types of businesses in specific Miami zip codes, looking for signs of illegal electronics exports and other trade-based money laundering schemes.

Such orders force local banks to comb their databases to see if they serve any of the affected businesses.

Another complication for South Florida banks when it comes to detecting money laundering is that in the Latin American countries with which they tend to business, there are a lot of people with similar names.

Distinguishing bad actors from innocent people is that much harder.

“You can get an incredible volumes of false positives you have to analyze, so being able to put all that data into a machine and have that machine be able to compile it all, review it all, analyze it, pull it from various sources can definitely go a long way to enhancing banks’ programs,” Schwartz said.

Miami banks spend a lot of resources, time, personnel, and money on compliance, he said.

“If you talk to the local regulators, they will tell you the banks in Miami have done a good job, not just in terms of their compliance program, but in cooperation with regulators locally,” Schwartz said.

Schwartz knows of several banks locally and nationwide that are testing AI for anti-money laundering purposes.

These tools come in to play together in deQUANTAVERSE ISSUU FLIP BOOK

9

Broadpath

“There’s such a large volume of activity and the incidence of false positives is so high that you’re throwing a lot of resources at trying to analyze all these false positives,” he said. “And banks are spending a lot of time and money just analyzing and eliminating them. An AI engine can analyze in seconds a client’s history, profile, and the types of activities he’s had going through that account. It would be easy and quick for the machine to understand that client’s profile and match it against that activity.

If there is an alert at the end of the day, it’s still coming into human hands to review it, but at least the whole process of getting there will help reduce the number of false positives.”

10

QUANTAVERSE ISSUU FLIP BOOK

Broadpath

PRACTICAL SOLUTIONS

How Artificial Intelligence can Help Overcome How artificialChallenges intelligence in canCorrespondent help overcome Banking Relationships challenges in correspondent banking relationships By: David McLaughlin | March - May, 2018

T

he capacity to send and receive inter-

regulatory scrutiny, investigation and exor-

foreign correspondent banking relation-

Thenational capacity to send and receivebitant internationIn some cases, organizations no longer desire payments through corresponpenalties. New solutions powered by ships rather than deal with the inherent dent bankingthrough is critical tocorrespondent the global al payments banking toand manage banking relation-correartificial intelligence (AI) advanced correspondent data risk and challenges of maintaining economy as financial institutions rely on analytics technologies offer much promise to spondent banking accounts. is critical to the global economy as financial ships linked to money services businesses these relationships to move their customers’ financial institutions to help assist with verThe correspondent regulatory institutions rely on these relationships (MSBs) or banks inbanking high-risk money.

As a result of increased regulatory ificationto of business relationships forcorrespondent correlandscape changed when the Financial requirements andcustomers’ risk related to shell com- As spondent move their money. a resultbanking of customers. jurisdictions over fears of potentialregulatory Crimes Enforcement Network (FinCEN) panies, correspondent banking relationship increased regulatory requirements and risk scrutiny, investigation and exorbitant penalAccording to estimates in a 2015 report by enacted “Special Due Diligence for Corremanagement has become a more challengthe World Bank, global remittances are spondent Accounts and Private Banking” related to shell companies, correspondent ties.

New solutions powered by artificial ining undertaking for financial institutions. expected to grow, albeit at a slow pace.1 interim regulations under Section 312 of the banking relationship has telligence (AI) andUSA advanced data analytics In some cases, organizationsmanagement no longer High remittance volume brings increased PATRIOT Act. FinCEN’s actions were desire to manage correspondent bankingundertaking regulatory pressures,technologies risk and compliance become a more challenging offerinmuch promise financial response to the 9/11to terrorist attacks and relationships linked to money services busicosts that bite at the heels of financial instimandated that financial institutions for financial institutions. institutions to help assist with verification enact nesses (MSBs) or correspondent banks in tutions. The unfortunate outcome is that specific know your customer (KYC), cusof business relationships for correspondent financial institutions often choose to de-risk high-risk jurisdictions over fears of potential tomer due diligence (CDD) and, in some banking customers. cases, enhanced due diligence (EDD) procedures for their foreign correspondent bank-

ing relationships.

According to estimates in a 2015 report by the World Bank, AML tools and processes are failing: global remittances are New technologies to theexpected rescue to grow, albeit at a slow pace.

Effective correspondent banking relationHighship remittance volume brings risk management boils down to having the adequate tools to efficiently resolve increased regulatory pressures, entities that use correspondent banks. risk Recurring and compliance costs that and challenges for compliance bite investigative at the heels ofinclude financial teams establishing an economic purpose and verifying institutions. The unfortunate complementary lines of business for correspondent outcome is that financial instibank customers. tutions often choose to derisk Legacy technologies including rules-based foreign correspondent banking transaction monitoring systems (TMS) attempt to detect and report on transaction relationships rather than deal details that indicate suspicious activity, but withare thelargely inherent risk Financial and chalineffective. institulenges maintaining tionsof often find themselvescorrefiling conservative suspicious activity reports (SARs) spondent banking accounts. because the necessary data is unavailable.

1

“Remittances Growth to Slow Sharply in 2015, as Europe and Russia Stay Weak; Pick Up Expected Next Year,” The World Bank, April 13, 2015, QUANTAVERSE ISSUU FLIP BOOK http://www.worldbank.org/en/news/press-release/2015/04/13/remittances-growth-to-slow-sharply-in-2015-as-europe-and-russia-stay-weak-pick-upexpected-next-year

11

Broadpath

The correspondent banking regulatory landscape changed when the Financial Crimes Enforcement Network (FinCEN) enacted “Special Due Diligence for Correspondent Accounts and Private Banking” interim regulations under Section 312 of the USA PATRIOT Act. FinCEN’s actions were in response to the 9/11 terrorist attacks and mandated that financial institutions enact specific know your customer (KYC), customer due diligence (CDD) and, in some cases, enhanced due diligence (EDD) procedures for their foreign correspondent banking relationships.

AML Tools and Processes are Failing: New Technologies to the Rescue Effective correspondent banking relation-ship risk management boils down to having the adequate tools to efficiently resolve entities that use correspondent banks. Recurring challenges for compliance and investigative teams include establishing an economic purpose and verifying complementary lines of business for correspondent bank customers.

Legacy technologies including rules-based transaction monitoring systems (TMS) attempt to detect and report on transaction details that indicate suspicious activity, but are largely ineffective. Financial institutions often find themselves filing conservative suspicious activity reports (SARs) because the necessary data is unavailable.

12

QUANTAVERSE ISSUU FLIP BOOK

The reliance on TMS is presenting significant risk to financial institutions. An unacceptable number of illicit transactions, or false negatives, are not flagged by today’s TMS. It is estimated that 50 percent of financial crimes in the banking system pass through TMS unnoticed. In addition, it is understood by anti-money laundering (AML) compliance practitioners that approximately 95 percent of the alerts generated by TMS are false positives.

Unlike TMS, artificial intelligence-based systems can detect patterns of behavior, analyze the intent of those patterns and expose anomalous activities. For example, transactions that do not follow the usual frequency and directional patterns expected for a given type of account may not be flagged by a TMS, but would be identified with an effective AI solution. An AI solution can learn the baseline of normal reported payroll account activity and thus identify any irregularities in payroll transactions as potentially fictitious and worthy of further investigation.

AI can Radically Improve Correspondent Banking Advanced data analytics and AI technologies can help financial institutions manage correspondent banking more effectively. An important function of an AI solution is its ability to monitor customers’ relationships to other customers and

Broadpath

PRACTICAL SOLUTIONS

entities and learn from their associated behavior. An AI-based AML solution can automate the transactional analysis of correspondent banking relationships to find anomalous behaviors and The reliance on TMS is presenting significant risk to financial institutions.

An identify the end clients unacceptable number of illicit transactions, or causing those anomalies. false negatives, are not flagged by today’s TMS. It is estimated thatsolution 50 percent of An AI-enhanced financial crimes in the banking system pass can also account for seathrough TMS unnoticed. In addition, it is sonality,by mergers and acquiunderstood anti-money laundering (AML) compliance practitioners sitions, randomness andthat other approximately 95 percent of the alerts genlegitimate erated

by TMS arevariances false positives. to find the illegitimate that Unlike TMS, artificialanomalies intelligence-based sys- are pretems can detect patterns of behavior, anasenting significant risks to financial lyze the intent of those patterns and expose institutions. anomalous activities. For example, transactions that do not follow the usual frequency Entity resolution Entity resolution andforthe investigation of and the investigation of and directional patterns expected a given entity relationships are integral to curbing type of account may not be flagged by a entity relationships are integral curbing theto de-risking cycle. The ability to estabTMS, but would be identified with an effeclish behavioral histories related to the the derisking cycle.

The ability to establish tive AI solution. An AI solution can learn the volume of transactions and their amounts, behavioral related baseline of normal histories reported payroll account to the volume expected volume of transactions and activity and thus identify any irregularities of transactions and their amounts, expectamounts, current and ongoing ultimate Human decision-making is still in payroll transactions as potentially fictibeneficial owner data, andtution. any/all adverse With data AI, time-coned and volume transactions a keyanalytics element to and AML compliance tious worthy ofof further investigation. and amounts, media related to the entity in question, is suming and costly correspondent bank and current and ongoing ultimate the beneficial On the front line against money laundering key to ensuring accurate corresponAI can radically improve are AML compliance professionals tasked pseudo-customer investigations can become dentmedia banking risk management and know owner data, and any/all adverse correspondent banking with identifying suspicious activities. The your customer’s customer.automated, providing investigators with the related to the entity in question, is the average compliance professional works Advanced data analytics and AI technoloExpanding the same data most points toessential an entieight to 10 alerts each day. To make a decidata. gies canto help financial institutions manage key ensuring accurate correspondent ty’s related parties (customers) provides a sion on each case, the professional is correspondent banking more effectively. An banking risk management andholistic know your risk picture for compliance and required to review multiple and disparate Advancements in data analytics and AI can important function of an AI solution is its investigative teams within a bank or covbank systems, including KYC and customer customer’s customer. ability to monitor customers’ relationships enable compliance teams and AML asinvestiinformation program databases, well as ered financial institution. With data analytto other customers and entities and learn cross-reference TMS for related flags and ics and AI, time-consuming and costly gators to fulfill their regulatory obligations from their associated behavior.

AI-based Expanding the sameAndata points to an entity’s turning to external sources of data. The correspondent bank and pseudo-customer AML solution can automate the transacwithautomated, precision,application improve SAR reporting and of AI can significantly enhance investigations can become related parties (customers) provides a holistic tional analysis of correspondent banking the capabilities of human compliance pro- of providing investigators ultimately with the mostprevent unnecessary derisking risk picture foranomalous compliance relationships to find behaviorsand investigative fessionals by eliminating tedious, time-conessential data. and identify the end a clients causing those banking customers. tasks involving massive amounts of teams within bank or covered financial insti- correspondentsuming Advancements in data analytics and AI can anomalies. An AI-enhanced solution can data. enable compliance teams and AML investialso account for seasonality, mergers and gators to fulfill their regulatory obligations acquisitions, randomness and other legitiwith precision, improve SAR reporting and mate variances to find the illegitimate David McLaughlin, CEO, QuantaVerse, QUANTAVERSE ISSUU FLIP BOOK 13 ultimately prevent unnecessary de-risking anomalies that are presenting significant Wayne, PA, USA, dmclaughlin@ of correspondent banking customers. risks to financial institutions. quantaverse.net

Broadpath

Human Decision-making is Still a Key Element to AML Compliance On the front line against money laundering are AML compliance professionals tasked with identifying suspicious activities. The average compliance professional works eight to 10 alerts each day. To make a decision on each case, the professional is required to review multiple and disparate bank systems, including KYC and customer information program databases, as well as crossreference TMS for related flags and turning to external sources of data. The application of AI can significantly enhance the capabilities of human compliance pro-fessionals by eliminating tedious, time-consuming tasks involving massive amounts of data.

14

QUANTAVERSE ISSUU FLIP BOOK

Broadpath

How Wells Fargo Could Have Avoided a Scandal By: David McLaughlin | October 7, 2016 The firing of 5,300 Wells Fargo (ticker: WFC) employees for purportedly opening two million unauthorized accounts suggests that management wasn’t comfortable with this unethical activity. Many are speculating that such activities happening over a period of several years, means that, overtly or covertly, some managers turned a blind eye to a situation of ongoing, and systemic, fraudulent activity. Whether looked over or simply overlooked, the mere fact that these practices continued as long as they did has many asking, “Are today’s megabanks just too big to manage?”

In a digital, knowledge-based economy, virtually everything we do produces data which can be used for multiple purposes. The data detritus of emails, texts, customer relationship management inputs, client activity, and much more, can be harnessed to produce valuable insights regarding the behaviors and actions of people — both legitimate and illegitimate. If the right questions are being asked, and the right data is being analyzed, too big to manage is no longer the reality, nor should it be allowed to be an excuse for fraud, crime, or unethical behavior to exist on a systemic basis.

Data science, including big data, machine learning, and the emerging technologies of artificial intelligence, are enabling institutions of all industries and sizes to better manage risks, to find fraud and criminality, and to therefore support the needs of compliance, management, and governing boards. The reality is that in today’s environment, the senior managers of large organizations can be alerted, directly on their desktops (i.e., they don’t have to hope one of their reports will tell them in a timely manner) when the data indicates some form of malfeasance.

Another reality is that a situation involving the systemic opening of unauthorized client accounts is a relatively simple problem. From a data science perspective, this is not a

QUANTAVERSE ISSUU FLIP BOOK

15

Broadpath

complex issue, and can be quickly identified with access to a relatively small number of data sets, all of which are in a bank’s possession.

For example, here are a few of the potential data sets and data science techniques which could have been utilized to ensure this type of unethical behavior was identified: • Email account names. Fuzzy logic and anomaly detection would have highlighted an inordinate amount of accounts with invalid email addresses.

• Account dormancy. Time series analysis could be used to identify an abnormal amount of unused accounts that had been opened in the previous 12 months, as well as a heavy pattern of account escheatment. Statistically significant amounts of both would indicate that customers had accounts of which they weren’t aware.

• Customer complaints. Natural language processing would pull out phrases from emails and recorded customer lines that would have indicated customers didn’t know or understand which accounts they had with the bank.

• Employee termination for fraud or ethical violations. This one is so obvious it’s difficult to label “data science.” Pure and simple correlation analysis would have shown a problem with front line employees.

As compared to identifying the complexity of money laundering, this would have been easy. Money was not being moved between multiple accounts that obscure ownership and source of funds, and there was no need to find and ingest data from outside the bank — unindexed, unstructured, or deep web data.

Knowing how the new technologies of data science could identify this particular unethical activity at Wells Fargo, the question becomes what was management’s true desire to find and put a stop to it, and the independence of a board which is required to demand the appropriate governance controls. Many in the industry continue to debate that megabanks are too big to manage and regulate. However, with an effective data science capability in place, this simply isn’t true. By identifying and asking the right questions, there’s no challenge too big that data science can’t manage.

16

QUANTAVERSE ISSUU FLIP BOOK

Broadpath

Your Money Helps Fight Crime: Using AI to Fight Terrorism, Trafficking and Money Laundering

By Rebecca Sadwick | January 9, 2018

As a rule, the private sector tends to bemoan greater governmental regulation. Most of us hold connotations of bureaucratic processes slowing down the pace of innovation, and several high-publicity cases of the public sector trying to force private companies to aid its efforts at the expense of the business’ preferences. Our very expectations of the effectiveness of increased regulations tend to carry some skepticism, at best. Think of the EPA’s clean air and water regulations, which were viewed as an undue burden on private industry for years before their value (and

surmountable impact to short-term business objectives) could be demonstrably proven.

Which is why I was intrigued to learn that heightened regulations of big banks after 9/11 have actually accelerated the pace of technological advancement—including the widespread introduction of artificial intelligence (AI)—changing the internal ethics and motivations of the industry itself. The relatively recent regulations have elicited proactive responses from banks, which typically now look to help solve the problem

QUANTAVERSE ISSUU FLIP BOOK

17

Broadpath

in ways that regulatory oversight does not directly mandate or incent.

“Historically, the banking industry treated identifying and reporting money laundering as an arbitrage game,” said David McLaughlin, founder and CEO of QuantaVerse, the first company with AI solutions purpose-built for identifying money laundering and other financial crimes. The financial sector’s primary tactic had once been to assess the cost of “throwing teams of human investigators and a variety of databases at identifying suspicious transactions and hoping not to get fined” for failing to report the 50% of financial crimes this approach is estimated to be missing.

Not so today. There has been a complete paradigm shift among financial institutions, which seems particularly notable.

Banks are for-profit institutions, which need to create returns for their shareholders.

Their core business is not in anti-money laundering (AML), and these AML efforts involve real expenses in terms of personnel, tools, and the potential for false-positives to negatively impact clients with legitimate transactions. From a pure bottom-line analysis, many banks had previously assessed the risk of non-compliance (and the corresponding fines) to be less than the cost of developing complex tools and processes to capture suspicious transactions.

“Since 9/11 especially, the fines and the headlines [about banks failing to identify criminal transactions] have become bigger, so the 18

QUANTAVERSE ISSUU FLIP BOOK

banks themselves have had to change their approach to solving the problem,” McLaughlin said.

Instead of being an unwitting tool used by terrorists and criminals, banks have become a weapon in the fight against financial crime and the tragedies those crimes pay to support.

Shifting Roles and Regulation The motivation for banks to monitor the transactions passing through their systems are largely explained as responses to increased legislative and media pressures.

But where is the breakdown and overlap between regulatory obligation, business ethics, and normative ethics?

“Traditionally, most of the conversations about compliance centered around costs, but as AI has improved the effectiveness and the efficiency of anti-money laundering efforts, we have seen the discussion evolve,” McLaughlin said. “Companies like QuantaVerse, regulatory institutions, and our clients (financial institutions) are really aligned now.” He explains that the banks “genuinely feel responsible for being part of the solution now...[and are] not looking to walk back compliance measures.”

The 2001 PATRIOT ACT, passed in response to 9/11, expands permissions to monitor financial accounts and transactions that are likely to indicate a tie to terrorist organizations. Banks have the burden of proof in demonstrating that their institutions are

Broadpath

not aiding criminal organizations, and government agencies now have the jurisdiction to seize assets, freeze accounts, and make arrests for suspected financial crimes.

While law enforcement and anti-money laundering teams are working to thwart global crimes, outdated legislation and limited technological resources have presented costly and time-consuming challenges to the banks themselves.

The AI-powered tools that big banks are now using to trigger alerts on suspicious activity that human investigators may not detect help to increase compliance with new reporting standards. Third-party detection platforms like QuantaVerse, IBM’s Watson financial services, Palantir, and Thomson Reuters and Bloomberg’s financial regulatory compliance solutions are now being used to alert their clients—the banks and financial institutions— who investigate and submit Suspicious Activity Reports to the government.

The Victims Money laundering is not a victimless crime.

The strong ties between money laundering and significant threats like terrorism, the drug trade, human trafficking, and corruption is at the crux of the increased attention that AML efforts have elicited in the last decade.

The money generated from these crimes is regularly laundered through global banks with “clean” funds, supporting lavish lifestyles and future crimes of those responsible.

Consider that: • Drug traffickers create $100 billion of illicit cash flows each year.

• Human traffickers made $150 billion in 2016 from the estimated 20 million humans trafficked globally.

• Small money movements utilized by terrorist organizations, including ISIS, for their operational use of “foreign fighters” often go undetected by banks’ transaction monitoring systems (TMS).

• Numerous reports have docu mented that many recent terrorist attacks in

The Benefit of AI Every time you make a transaction at a bank, you are providing training data that helps fight these crimes. By exposing AI algorithms to transactions that do and do not constitute criminal activity, they become better at predicting and identifying money laundering and other financial crimes.

Algorithms are adept at identifying patterns in transactions and accounts that often go undetected by human investigators. “Banks have been accumulating vast amounts of data with the promise that, someday, the data will be used to provide information,” according to QuantaVerse. McLaughlin said, “We recognized that we could build QUANTAVERSE ISSUU FLIP BOOK

19

Broadpath

something more effective in solving this problem—a scalable, effective solution that could help restrict financial crimes and the money laundered around the globe.”

Questions naturally emerge as to the efficacy of these AI models and whether there are unintended consequences of

20

QUANTAVERSE ISSUU FLIP BOOK

heightened automated screening of financial transactions. Implications for financial inclusion, anti-trafficking efforts, and the risk of driving criminal activities underground will be discussed in Part II of this series—published on Human Trafficking Awareness Day, Thursday, January 11th.

Broadpath

How Artificial Intelligence is Transforming the Global Battle Against Human Trafficking

By Hollie McKay | September 27, 2019 It’s one of the world’s deadliest and most barbaric global ventures.

Each year, human trafficking generates more than $150 billion in profits – at the expense of human life – with children accounting for around a third of its victims.

It’s a practice that operates underground in almost every country on the planet, and despite the resources thrown at it — by law enforcement, non-governmental organizations, social media campaigns – it only seems to grow.

Inside law enforcement’s efforts to end human trafficking - ‘Fox Report’ provides some insight into the horrors of human trafficking and the steps that law enforcement is taking to put a stop to it.

Experts on the topic say the tools used in even in the most developed societies fall far short of what is needed to put a dent in this grim and growing enterprise.

But artificial intelligence (AI) and machine learning (ML)? Those could change the game.

“Anytime you have to ingest large amounts of data and information, you try to identify trends and patterns, and it can be very difficult to do well,” Alma Angotti, managing director at Navigant Consulting and a former U.S. regulation official for the Securities and Exchange Commission, and the Financial Industry Regulatory Authority told Fox News. “Typically, it has been a rules-based system – like flagging transactions over a certain amount such or with a certain amount of frequency. The problem with that is you can’t identify patterns and problems.”

The U.S. Department of Homeland Security defines trafficking, also referred to as modern-day slavery, as a crime that “involves the use of force, fraud,or coercion to obtain some type of labor or commercial sex act.”

AI and ML, Angotti said, have the power to analyze more than just financial activity.

QUANTAVERSE ISSUU FLIP BOOK

21

Broadpath

“It can highlight social, economic and even political conditions from hundreds of thousands of sources,” she said. “For example, law enforcement can look at young women of a certain age entering the country from certain high-risk jurisdictions. Marry that up with social media and young people missing from home, or people associated with a false employment agency or who think they are getting a nanny job, and you start to develop a complete picture. And the information can be brought up all at once, rather than an analyst having to go through the Dark Web.” The current anti-money laundering (AML) environment, Angotti pointed out, relies on “simple transaction-monitoring rules to detect human trafficking,” and it “simply does not have the capacity to consider, weight and examine the necessary number of inputs.”

“The problem with it now is that it produces a lot of false positives, so the real In this Oct. 27, 2015, photo, Dawn Stenberg, from the Junior League issues are lost in the weeds. of Sioux Falls, stands near the group’s anti-human trafficking billboard in Sioux Falls, S.D. (AP) If you use more machine learning, you can program more variables and machines can use the information it has and then teach itself how to better identify patterns,” she said.

“You get better alerts, and alerts that are more likely to recognize real risks.”

Despite the potential, for now those tools remain underused, Angotti said.

“The government could increase the leveraging of technology to support trained law enforcement in tackling this complex issue. It would be helpful to match dataholders – government and financial institutions – with computational science and AI partners to provide deployable tools to identify human trafficking activity and the money flows associated with it,” she said.

“The same networks that traffickers use to recruit their victims can also be used as the source of data to detect criminal activity.”

22

QUANTAVERSE ISSUU FLIP BOOK

Broadpath

QuantaVerse CEO and founder David McLaughlin said that while small steps are having a significant impact, there is much more that can be done. Artificial intelligence can follow the money created by human trafficking operations. That means that instead of identifying only lower-level human traffickers, the “beneficial owners” or kingpins of these operations can be discovered, he said.

“AI is better at finding suspicious financial activity than legacy technology alone. Today, banks are required by regulators to report unusual banking activity by filing Suspicious Activity Reports (SARs) to FinCEN. To do this, banks’ anti-money laundering (AML) teams currently rely on antiquated, rules-based Transaction Monitoring Systems (TMS),” McLaughlin explained. “Suspicious transactions are ‘flagged’ by the TMS and handed over to human investigators to determine if a flag should be reported to the authorities.

“Unfortunately, 95 percent of the flags produced by the TMS systems are benign and waste vital investigative resources. Even worse, TMS systems are missing crimes that are going through the banking system undiscovered.”

McLaughlin said AI automates the investigative digging and then produces reports of its findings. AML investigators can now spend their valuable time/expertise analyzing AI findings and making determinations on what may be human trafficking activity faster than ever.

Technology is also playing a prominent role in the private sector, with a community rather than a top-down approach. The revolution of apps has also revolutionized the fight against modern-day slavery, with an added focus on the next step – converting data into something actionable.

U.S. researchers developed an AI engine earlier this year, entitled Hotels-50K, which recognizes a hotel from an image of a hotel room -- a critical development for human trafficking investigations.

“Images directly link victims to places and can help verify where victims have been trafficked and where their traffickers might move them or others in the future. Recognizing the hotel from images is challenging because of low image quality, uncommon camera perspectives, large occlusions (often the victim), and the similarity of objects (e.g., furniture, art, bedding) across different hotel rooms,” the developers wrote. “To support efforts towards this hotel recognition task, we have curated a dataset of over 1 million annotated hotel room images from 50,000 hotels.”

Those images include professionally captured photographs from travel websites and QUANTAVERSE ISSUU FLIP BOOK

23

Broadpath

crowd-sourced images from a mobile application, the developers continued, which are more similar to the types of images analyzed in real-world investigations.

The analysts then endeavor to present “a baseline approach based on standard network architecture and a collection of data-augmentation approaches tuned to this problem domain.”

FILE- In this Tuesday, Oct. 9, 2018, file photo New Google Pixel 3

Meanwhile, the “STOP APP” smartphones are displayed in New York. Google’s new Pixel 3 phone plays catch-up with Apple and Samsung on hardware. It’s really is designed to “enable designed to showcase Google’s advances in software, particularly in people anywhere in the artificial intelligence. (AP Photo/Richard Drew, File) world to report suspicious incidents of human trafficking anonymously and securely.” The information submitted is then uploaded directly into a secure database for the app’s parent organization, Stop the Traffik, where it is analyzed alongside multiple different datasets on human trafficking and modern slavery activity. The raw data is used to develop trend reports and alert authorities and communities where necessary.

The likes of IBM have partnered with international money exchange authorities such as Western Union to develop a cloud-centric database to detect questionable payments, identified through established patterns and concerns proven to have highlighted trafficking cases in times past. And the team at HumanSlavery.com is currently developing a “unique solution” involving a reporting mechanism with location tracking that utilizes both existing sensors such as cameras and heat sensors in addition to law enforcement databases to create a map – using artificial intelligence – on which authorities can then operate.

However, experts caution that technology can only ever be a slice of the solution. “The first thing to keep in mind is that human trafficking is a fundamentally human problem, to which AI can only be a small part of the solution. Sex trafficking, for instance, arises as part of a dysfunctional ecosystem created by a host of economic, sociological, and regulatory problems,” added Notre Dame Adjunct Professor of Law, Alexandra Yelderman. “At best, AI will be as good as humans at untangling those complex issues, but probably won’t be better.”

24

QUANTAVERSE ISSUU FLIP BOOK

Broadpath

Using Data Science to Mitigate Bank Employee Fraud By: David McLaughlin | March - April 2017 Banks today hold vast and ever-multiplying stores of data, including emails, text messages, Customer Relationship Management (CRM) records, client activity information, and much, much more. This data, if analyzed correctly, could reveal both enormous value and uncover unidentified risk. In efforts to meet the requirements and expectations of regulators, as well as legislators, shareholders and customers, banks have spent billions on scores of data management, data mining, data analytics, and reporting solutions and tools. Despite these investments, banks continue to face information challenges and significant operational, legal, and reputational risk.

While much focus has been dedicated to improving the identification and thwarting of external bad actors, banks must better monitor for and prevent unethical and illegal activities from close-in business partners and even its own employee base.

Consider the recent case of a large bank where employees fraudulently opened millions of bank and credit card accounts unbeknownst to clients. The bank employees have been accused of creating accounts customers did not request, funding those accounts by transferring money

from other accounts without notifying customers, creating PIN numbers for unknown and unwanted debit cards, and setting up fictitious email addresses to obfuscate their fraud. The firing of those employees involved should suggest that management would not tolerate this unethical activity. However, many are speculating that with such activities happening over a period of several years, means that some managers turned a blind eye to a situation of ongoing fraudulent activity or were potentially a part of the problem.

Data can and should be harnessed by banks to produce valuable insights regarding the behaviors and actions of people and entities—both legitimate and illegitimate. If the right questions are being asked, there is no excuse for this type of fraud or unethical behavior to exist on a systemic basis. Data science, including big data, artificial intelligence and machine learning, can enable institutions of all industries and sizes to better manage risks, to find fraud and criminality, and to therefore support the needs of compliance, management and governing boards.

QUANTAVERSE ISSUU FLIP BOOK

25

Broadpath

Applying Data Science to the Banking Industry Data science methodologies can alleviate the costly dependency on human bank investigators and their manual, limited ability to more efficiently and directly discover internal and external bad actors and suspicious activities. The insights revealed by a data science-powered solution can then be conveyed through APIs, reports, dashboards and rich visualizations, enabling banks to take the required actions to reduce their risk and associated costs. In today’s environment with the proper data science solution in place, investigators could report findings to senior management in a timely manner after a data science resolved alert.

Identifying Employee Fraud with Data Science Techniques The identification of unauthorized client accounts is a relatively simple problem to address. From a data science perspective, it is not a complex issue, and can be quickly identified with access to a relatively small number of data sets, all of which are in any bank’s possession. Here are four key potential data sets and data science techniques that can be utilized to ensure unethical internal behavior is identified: • Email account names. Fuzzy logic and anomaly detection can highlight an inordinate amount of accounts

26

QUANTAVERSE ISSUU FLIP BOOK

with fraudulent or invalid email addresses. For example, if a checking account application, a mortgage application, and a credit card application have very different data or email addresses, this could raise an alert. Banks can set alerts that are triggered with, for instance, three or more data disparities, and software can also alert if an email address is changed just before a new account is created. Of course, many people maintain different email addresses, but analysis in aggregate may lead to a problem.

• Account dormancy. Banks can utilize time series analysis, a model used to predict future results based on previously observed values, to identify an abnormal amount of unused accounts. This tool can be adapted to flag accounts that have had no activity for long periods of time, after being open, say in the previous 12 months, as well as a heavy pattern of account escheatment. This could be a potential sign that the customer did not ask for a product and does not know what he has.

Statistically significant amounts of both would indicate that customers had accounts of which they were not aware. Of course, you might not want to wait 12 months–you can also review for inactivity for accounts

Broadpath

opened for 30 or 60 days in order to take immediate action.

• Customer complaints. Natural language processing can pull out phrases from emails and rec-orded customer lines which would indicate that customers didn’t know or understand which accounts they had with a bank. For example, semantic analysis performed on calls, emails, and other employee-to-customer interaction, will identify consistent themes within complaints and conversations. If customers complain that they are being charged for accounts they did not open, or they are finding accounts opened in their name, this would determine a more widespread issue.

• Employee termination for fraud or ethical violations. While this isn’t necessarily a “data sci-ence” technique, correlation analysis can identify a problem with front line employees.

For example, if management saw employees being fired for ethical violations in numbers that are significantly higher than the past, or higher than industry averages, this action should be a warning flag that something inappropriate is underway.

The tried and true data science techniques of finding anomalous patterns of behavior would be perfectly applicable to a case of

employee fraud. Organizations need to be proactive and play the offense. While data analytics helps organizations know what has happened and respond, it can also thwart malfeasance. Sending an automatic email to customers when an account has been opened tends to be standard practice in most industries nowadays, however, some banks are still behind the times. If a customer receives an email about an account that has been opened, that customer can immediately respond and the situation is promptly resolved. It’s a small fix, but when implemented, it can ensure that accounts are not opened fraudulently or in error, as employees are more likely to be more scrupulous where there are checks and balances.

Banks should also use data science to know what their baseline—their normal— looks like. Banks should do analysis and consistently review characteristics for their account holders to learn where there are anomalies. How diverse are email addresses for single account holders with multiple products? How many active and inactive accounts does the bank typically have? Where are accounts typically opened, and how many are typically opened and closed? What IP addresses are being used for banking and where are the IPs? Are the same IPs being used to access different accounts? Knowing what business your bank has, beyond just revenues, will alert you to deviations and

QUANTAVERSE ISSUU FLIP BOOK

27

Broadpath

in turn, executive management can take action in a timely manner.

Considering how the technologies of data science are well-suited to identify unethical employee activity, the question will become management’s will to find and put a stop to it, and the Board’s independence to demand the appropriate governance controls. With an effective data science-powered solution in place and by asking the right questions, banks can be able to identify and prevent internal employee fraud and avoid serious damage to its brand identity, as well as millions of dollars in fines.

About the Author David McLaughlin is CEO of QuantaVerse, a data science company founded specifically to help the financial services industry.

He can be reached at dmclaughlin@quantaverse.net.

QuantaVerse, Inc. is the emerging leader in data science-powered risk reduction and revenue growth solutions, purpose-built for the global banking industry. Founded by financial services industry veterans and innovators, QuantaVerse solutions employ proprietary data science algorithms, integrate and filter internal bank data and related external data – including public Internet data, unindexed deep web data and government and commercial datasets – to help the global banking industry to significantly improve their compliance with AML, KYC and BSA regulations and requirements.

For more information, visit www.QuantaVerse.net.

28

QUANTAVERSE ISSUU FLIP BOOK

Broadpath

AI Tech Company QuantaVerse’s New Service to Fight Financial Crime

By Tatjana Kulkarni | December 14, 2017 XCLUSIVE – Financial crime detecting platform QuantaVerse has added a new AI-based service to allow its financial institutions and banking clients better detect fraud and other crimes through audit investigations, as these crimes continue to be a ma jor concern for banks and non-banks alike.

The new CAE (Chief Audit Executive) Checkup service unveiled today uses the startup’s AI Financial Crime Platform to analyze data and detect insider threats, bribery, corruption, money laundering, fraud, terrorism financing, and third-party risks.

This can be done without having the audit team manual create models and study samples for anomalies to catch financial misbehavior.

“With this new technology, audit departments can look at the entire transaction history of the year to spot anomalies,” David McLaughlin, CEO and founder of QuantaVerse, told Bank Innovation. “That not only reduces risks, but also saves these internal audit teams resources and time.”

The AI service analyzes data it gathers from core accounting, core banking, travel and expense reporting and vendor servicing, trade/export to pinpoint irregularities and “anomalous data patterns related to both known and not yet identified financial crime typologies,” McLaughlin explained.

Given that security issues don’t seem to be going anywhere in 2018, QuantaVerse is working on other features and products in this space. One technology that it is paying close attention to is blockchain, and its applications particularly in the KYC space. There might even be a deal in the pipeline, but McLaughlin could not disclose more.

“Cybercrime was certainly a big concern this year, and concern around it will continue in 2018,” McLaughlin said. “Fraud is a hit to a company’s bottom-line, and when it comes to AML (anti-money-laundering), we are seeing more and more regulators holding C-level personally accountable for these crimes. We have seen that in the case of MoneyGram.

No one wants to be in that position and to reduce these risks and crimes is where AI and ML technologies is really going to make a big difference.”

Based in Wayne, Pennsylvania, QuantaVerse was founded three years ago to help banks and other financial institutions combat are reduce fraud and money laundering as well as comply with KYC (Know Your Customer) and FCPA (Foreign Corrupt Practices Act).

Over the past few years, the company has extended its services to non-FIs. The company uses Artificial Intelligence and Machine Learning.

QUANTAVERSE ISSUU FLIP BOOK

29

Broadpath

How QuantaVerse Saves Time, Money In Fight Against Financial Crime

By Renato Capelj | December 17, 2020

Legacy anti-money laundering (AML) technologies — transaction monitoring systems — historically generate about a 95% false-positive rate.

That’s according to David McLaughlin, who founded QuantaVerse in 2014 to help institutions identify, stop and prevent financial crimes.

“There’s a huge false-positive rate, and every alert with a traditional TMS has to be investigated by a human,” he said. “It’s a very manual process, with lots of unfulfilling work.”

QuantaVerse filters through the noise of institutional, government, public internet and unstructured deep web data to significantly improve compliance.

“We’re the only company that is focused on the problems; we lower the number of false-positives, automate the investigations that are happening around the alerts, and we do so in a way that actually lowers the risk of the organization, by finding instances 30

QUANTAVERSE ISSUU FLIP BOOK

of risk that the TMS may be missing.”

Core Product Portfolio: To reduce the number of false positives and negatives, as well as streamlining the financial crime investigation process, QuantaVerse offers three core solutions.

“Our customers can consume just one, if that’s their preference, or they can consume all three — consume the platform — and get all the benefits,” McLaughlin said.

QuantaVerse’s multifaceted financial crime monitoring, investigations and enforcement product portfolio allows organizations to efficiently comply with AML, know-your-customer and Foreign Corrupt Practices Act regulations.

“The primary preference of our clients in the market today is to consume that automated investigation capability, not mess with false-negatives or -positives, not change the rules-based engine, but just take all of those alerts coming out of a TMS, and enable the workforce to work

Broadpath

more efficiently using our technology,” McLaughlin said.

Use Case, Recent Developments: In light of the COVID-19 coronavirus pandemic, the digital transformation in financial crime prevention accelerated as companies looked to streamline the investigation capabilities of their remote workforces.

Firms came under pressure quickly as profits sank, McLaughlin said.

“They got this huge pressure to say: ‘OK, well, where are we? Where can we help the profitability of our organization?’”

The profitability of compliance was top of mind for organizations that, in droves, sought to replace their antiquated methodologies, opting for automated, digital solutions like QuantaVerse, he said.

QuantaVerse has become more finite around automation, allowing lower levels of investigation more power in validating and deciding whether to push cases through further scrutiny, McLaughlin said.

“We have created capabilities that allow level one analysts to do their jobs in less than 10 minutes, down from 30 minutes.

They get a one-page report, and they just now have to read, validate and make that decision, or send cases up for further scrutiny to a more experienced analyst.”

QuantaVerse is looking to offer new markets a personalized, out-of-the-box solution, the company’s founder said.

“I think this approach that we have to reduce and automate the remaining workload, and lower the risks, is something that is applicable across multiple projects and markets.”

“I think the biggest impact on our company from COVID was this ramp-up in momentum since the summertime.”

The Innovation Outlook: The digital transformation in financial crime prevention will continue accelerating.

QUANTAVERSE ISSUU FLIP BOOK

31

Broadpath

Tech Disruptors: David McLaughlin, QuantaVerse

By Michelle Caffrey | May 3, 2018 David McLaughlin, founder and CEO, QuantaVerse, Wayne The AI-powered technology QuantaVerse has developed enables banks, corporations and other financial institutions to track down bad actors, from terrorists laundering money to human traffickers to corrupt executives. Its tech could have large ripple effects throughout the world’s largest organizations, empowering them to proactively identify and stop criminal activity.

What is it about your business that makes you want to get up in the morning and get to work?

At QuantaVerse, we’re engaged in creating a brand-new technology that is having a ma jor impact on huge societal problems.

Money laundering is not a victimless crime.

The World Bank estimates that up to five percent of global GDP is laundered every year, equaling trillions of dollars derived from really terrible crimes such as human trafficking, drug trafficking and terrorist financing.

Finding that money has proven to be a fairly intractable problem to solve.

Regardless of the large amounts of money being spent by banks and other financial

32

QUANTAVERSE ISSUU FLIP BOOK

David McLaughlin of QuantaVerse.

services companies to try to identify it, the vast ma jority of financial crimes have historically gone undetected.

The money being moved through those financial crimes are being used to finance future crimes and support opulent lifestyles. Our products and solutions make it dramatically harder, and riskier, for trans-national criminal organizations to move their money around the globe. It’s easy to go to work knowing you’re having that kind of impact on the world.

Broadpath

What are the challenges and advantages of building an organization in Greater Philadelphia?

The Philadelphia area is a fantastic place to start a business. There’s great talent, an affordable living standard, a strong service industry that knows how to work with tech startups, the PACT organization, and a great angel community, we really have it all. I can’t think of an area that has all the advantages of Philadelphia.

Plus, we have great sports teams!

Do you see yourself as a disrupter, why or why not?

We knew the application of artificial intelligence and machine learning could drive transformational improvement in the efforts to combat money laundering. We set out to build that platform and have proven that we can deliver on that promise. We walk around all day thinking about how we can continue to help our clients lower risks and costs, while finding more bad guys than they did yesterday.

If we happen to be disrupting an ineffective legacy technology industry while we’re doing that — so be it.

We didn’t start QuantaVerse with the goal to disrupt something, and we don’t walk around all day thinking about disruption.

QUANTAVERSE ISSUU FLIP BOOK

33

Broadpath

FinCEN Files: Legacy Technology “not up to the challenge” to Tackle AML

By Alex Hamilton | September 20, 2020 Legacy technology is just “not up to the challenge” when it comes to dealing with anti-money laundering (AML) requirements, and overreporting from banks playing it safe is putting regulators under undue pressure, say market participants.

A ma jor leak this week showed ma jor banks allowing oligarchs, mobsters, and criminals to launder money more than $2 trillion.

under resourced, need to investigate. This inevitably leads to inefficiencies and further compounds the resourcing issues.

Buzzfeed News obtained more than 2,100 suspicious activity reports (SARs) filed by banks and financial institutions. These were originally submitted to the US Treasury’s Financial Crimes Enforcement Network (FinCEN).

“Currently the process is hugely manual in many countries. It’s also important to note that when FIs report a suspicion, it often relates to a transaction or series of transactions that have already taken place.”

Analysis by the International Consortium of Investigative Journalists (ICIJ) found that between 1999 and 2017 banks flagged transactions worth trillions in SARs submitted to FinCEN.

David McLaughlin, CEO of QuantaVerse, says that finding financial crime among the millions of SAR reports is like “finding a needle in a stack of needles”. He says that legacy technology relief upon by incumbents is “not up to the challenge”.

SARs in Their Eyes “Whilst a large number of SAR filings may indicate effective controls in the financial institutions, it may also suggest they are overreporting so as to avoid future regulatory issues as a ‘just in case measure’,” says Rachel Woolley, global director of financial crime at Fenergo.

“This results in increased numbers of reports that [financial intelligence units], which are 34

QUANTAVERSE ISSUU FLIP BOOK

Banks submit millions of SARs to regulators each year

Broadpath

“Although two million SARs are filed annually, its estimated that financial institutions never even identify between 50% and 70% of the money laundered through global systems. Those are transactions that are never alerted and never investigated with no chance of SARs being filed.

“It’s clear that improved systems need to be deployed throughout the entire chain of financial crime identification and investigation.”

For James Heinzman, executive vice president for financial services at ThetaRay, the leak itself is of greater importance.

“The bigger problem at hand that needs to be addressed immediately is the fact that there was a breach in security that led to these leaks, and that leak needs to be identified.

“The privacy of the people involved — the investigators that determine whether there is something that can be considered criminal or suspicious activity — is paramount to preserve the integrity of the program, and this breach really compromises that.”

Support for Regulators FinCEN’s SAR database is available to more than 450 law enforcement and regulatory agencies in the US. More than 13,000 users use the system millions of times every year.

According to US Treasury figures, the number of people working at FinCEN has shrunk by more than 10% since 2010.

Jamal Al-Hindi, acting director of FinCEN in 2017, testified that year to congress that the department faced hiring issues.

Heinzman says regulators are always going to receive an unfair share of the blame when stories like these break. “In reality, the blame should be placed on the funding of these agencies.”

He adds: “Regulators are doing good work with what little funding they have, and they’re working well with banks and the industry as a whole to identify issues.”

Jane Jee, CEO of Kompli-Global, says that there are “disparate rules which are not applied consistently”, especially among the 25 AML supervisors in the UK. “The files show failures by banks and by implication law firms and accountants.

Therefore, the regulators need more expertise and need to create a panel of experts to assess where technology can help reduce abuse.”

Fenergo’s Woolley says that issues arise as the SAR process is looked at in silos. “Many of the issues discussed in recent days refer to the inconsistencies in the SAR reports themselves. These require manual review to make sense of what is being reported.

“Stretched resources are now having to first identify relevant information, extract it, assess it and then make a decision. This increases the time to determine if a SAR was warranted in the first place.”

QUANTAVERSE ISSUU FLIP BOOK

35

Broadpath

never going to completely solve this issue as it’s only one piece of the puzzle.”

Kompli-Global’s Jee says there is “no doubt” that some issues have been resolved. She believes that company registered are remain a point of contention.

“Companies House [in the UK] is still the weak link as there are practically no checks carried out on the information filed. Now, there are proposals to reform but when will they come into force?”

The EU’s 4AMLD and 5AMLD have brought in new KYC measures

The 4AMLD/5AMLD Solution The FinCEN leaks concern suspicious transactions flagged between 1999 and 2017. The European Union’s fourth and fifth anti-money laundering directives (4AMLD & 5AMLD) have come into force since. Have these historical issues been plugged by newer legislation?

“I think knowing your customers better is a part of the equation, but it’s not the complete answer,” says Heinzman.

“In order to get a full picture of what’s suspicious, of course you need to know who your customers are and who they’re transacting with, but even more importantly banks need to know what their activities are as well.

“This includes both what they’re actually doing, and what their business is in the context of their relationships. [Regulation is] 36

QUANTAVERSE ISSUU FLIP BOOK

Looking Forward Woolley believes that the leaks are going to trigger some definite activity among regulators and banks. “Ticking boxes to show you met the minimum standard isn’t working. We need a more joined-up approach that focuses on effective outcomes.”

Jee says the current controls on AML “are not working well enough” but there are steps being taken to combat it.

“These cases should not be dismissed as old cases because many can still occur today.

More should be done to stop dirty money getting into the financial system and moving round the world.”

Heinzman says that it remains important to keep in mind that the leaked SARs were banks investigating and reporting.

“What happened after they reported it to FinCEN? We don’t know. Also, bear in mind

Broadpath

that these aren’t definitively criminal findings; they’re just reports of suspicion. They should certainly be investigated, but whether or not they have been is another story.

“The biggest ramification isn’t the SAR itself.

It’s the fact that a lot of information about sources and the means to identify this suspicious activity will give the bad guys insight into how things work at the banks.

“There’s going to be scrutiny around the security of these reports, and how information like this could be leaked. This breach has jeopardised the integrity of the whole financial crime investigation and reporting system.”

QUANTAVERSE ISSUU FLIP BOOK

37

Broadpath

David McLaughlin: How AI can Nail a Bribe Paying Scrap Metal Dealer By: David McLaughlin | October 25, 2017 Last year 27 companies paid nearly $2.5 billion to resolve FCPA-related offenses. So far this year, 9 companies have paid more than $1.2 billion for FCPA-related settlements.

Yet despite these staggering outcomes, existing tools deployed today by corporations are not capable of effectively mitigating FCPA risk.

Many solutions are extremely outdated, time-consuming, or lack the functionality to detect potential threats.

Traditional surveillance solutions have been found to be deficient in: • Detecting accounting misappropriations • Matching receipts, invoices, expense payouts against travel and self-reported databases, and • Processing natural language or unstructured data sets such as emails or text messages The next evolution in protecting against FCPA risk is to leverage advancements in artificial intelligence (AI), machine learning, and data analytics.

These new technologies can enable anti-bribery and anti-corruption teams, 38

QUANTAVERSE ISSUU FLIP BOOK

compliance staff, audit teams, internal investigators, and consultants to detect potential FCPA misconduct.

By employing a set of AI agents to query a firm’s core accounting/finance system, travel/expense reporting system, trade finance data, third-party vendor lists, and internal e-mail systems, an AI solution can extract actionable evidence from the data to detect and report instances of anomalous activity related to potential FCPA risks.

Some of the AI techniques used to identify FCPA risks include advanced entity resolution and verification, Ultimate Beneficial Owner analysis, deep Web analytics, NLP (Natural Language Processing) Web scraping, network analysis, and volumes and values analysis.

Here’s how AI can work. A U.S.-based scrap metal recycling company transacts business with a steel manufacturer based in India. Analysis of the scrap metal recycling company’s employee travel and expense reports found anomalies associated with one employee as compared to the activity of similar employees.

Natural Language Processing analysis then identified a number of keywords in the employee’s emails and texts that indicated guarantees and suspicious foreign payments.

Broadpath

It was established that the employee was making improper payments and promises to the India-based steel company to induce them to purchase scrap metal from the U.S. company.

For responsible corporations serious about detecting potential FCPA issues, advanced AI and data analytics can assist the leadership in conducting root cause analysis to determine how the suspected corruption or bribery occurred, assess the effectiveness of its anti-bribery and anti-corruption compliance program, and identify internal controls that work and those that don’t work.

Through the power of an AI-based risk mitigation solution, companies can easily analyze massive amounts of corporate financial data, discern patterns, and quickly identify where exceptions or anomalies exist that can unveil FCPA risks.

QUANTAVERSE ISSUU FLIP BOOK

39

Broadpath

Software Fills In For India-Based Compliance Teams In COVID Lockdown By: Tom Groenfeldt | April 27, 2020 The COVID-19 crisis has exposed a lot of unfamiliar reliance on globalization and outsourcing, but regulatory compliance? With India on a severe lockdown, banks that had outsourced a lot of their compliance work to India find it can’t be done — the staff can’t go into the office and often the internet is too weak to support work from home. QuantaVerse, in the business of preventing fraud and money laundering, offers cloud-based tools that can be turned on to supplement U.S.-based compliance staff until the Indian team is back at work.

Financial firms are required to look at all the transactions they process to avoid violating U.S. laws on sanctions and illegal activities such as drug trafficking, said David McLaughlin, the CEO of QuantaVerse.

“As the economy has become globalized, FI compliance has often off-shored compliance investigation to India. When technology identifies potential money laundering or a financial crime, a human has to look at the account, parties to the transaction, do web searches for adverse media — it’s a very expensive task on-shore, so some organizations have taken part or all of it to India. But with the lockdown in India, people can’t go the the office and connectivity’s not great, so some firms are working hard to figure 40

QUANTAVERSE ISSUU FLIP BOOK

out how to cover the work. They can’t just decide not to do it.”

And it’s not just off-shore compliance teams that have been affected. Domestic compliance staff are working from home or remote locations and they are seeing some sharp spikes in alerts.

The company has heard from in-house teams in New York that are having trouble keeping up. They see increases in alerts coming from transaction monitoring systems that interpret the new patterns of the COVID-related economy as suspicious and requiring investigation.

“We’ve seen a 50% increase in the number of alerts we have to investigate since we entered the crisis.,” said a compliance officer at a New York bank. “Of course, most of them are false positives, but we aren’t prepared to handle the volumes. The backlog is starting to become significant.”

The potential violations have different reporting rules. Suspected money laundering has to be reported in 30 days; large banks might be filing reports every day. Sanctions screening, such as a payment to Iran, has to be completed before the transaction is done.

Broadpath

In mid March, FinCEN requested that financial institutions affected by the COVID-19 pandemic contact FinCEN and their functional regulator as soon as practical if they have concerns about potential delays in their ability to file required Bank Secrecy Act (BSA) reports.

things people are good at,” said McLaughlin. “Computer systems can pull lots of data from a myriad of resources and compile it in a way to see patterns. Those are things computers are good at. Then we enable the investigative staff to do the things they are good at.”

On its web site, QuantaVerse says that by replicating much of the human investigative process that analyzes entities, transactions, and their intention (or economic purpose), most of an AML case investigation can be completed by the software.

Financial firms are ready for some types of disasters, added McLaughlin, who has worked in large financial organizations, but not this.

Before the COVID crisis, said McLaughlin, the company worked with financial firms to help them make their compliance programs as efficient as possible. Unlike robotic process automation (RPA) and AI-enabled AML tools that accelerate individual steps within an AML or BSA investigative process, the QuantaVerse end-to-end financial crime platform has been proven to automate more than 80 percent of the investigative effort, he said.

“We haven’t worked with an organization that has completely eliminated an investigative staff, but we help them use staff in

“They are ready for infrastructure disaster, like a data center. But many companies aren’t prepared for the infrastructure staying up and humans being unable to perform their jobs in mass scale. We have taken automation of the investigative process and enabled companies to add a component to their business continuity and disaster recovery in the event human investigators are no longer able to complete the requirement.

We can get an environment up very quickly and easily and put it on the shelf for a customer, and then when their investigators come back online they can turn us off.”

QUANTAVERSE ISSUU FLIP BOOK

41

Broadpath

How the Pandemic has Affected Corporate Compliance Teams By: Clara Hudson | May 20, 2020 While most compliance professionals have adjusted to a new reality with minimal disruption, for a few the changes have been stark.

Who’s affected, and how, varies from company to company and industry to industry.

GIR spoke to 15 industry insiders – including defence lawyers, compliance professionals and a recruiter – to understand how the coronavirus pandemic has affected compliance departments. The worst-hit teams are “just down to skeleton crews”, a recruiter said.

The Energy Sector is “very ugly” Before the pandemic, the corporate compliance field was booming. But with the jobs market hurting badly on the whole, unsurprisingly the compliance field has also taken a hit.

One lawyer who requested anonymity to speak openly said they know a chief compliance officer at a large company who had to lay off three individuals working for them as a result of the pandemic. Another lawyer said they know a temporary compliance employee at a large company was going to be made permanent until the pandemic hit and the company changed its mind.

42

QUANTAVERSE ISSUU FLIP BOOK

A top lawyer for Salary.com, Colin Levy, spoke out in a recent article about losing his position as the company’s general counsel, which he described as “brutal.” And ride-share giant Uber listed several compliance personnel it laid off who are now looking for work.

The energy industry in particular has been ravaged by the pandemic, with oil trading at a negative price for the first time in April.

John Gilmore, a managing partner at legal and compliance recruiting firm BarkerGilmore, said he’s heard from a lot of compliance professionals in the energy sector who want to move elsewhere because those companies are “cutting way back” on their staff. Some compliance teams in the energy industry are “just down to skeleton crews”, Gilmore said.

“It’s getting killed,” he continued. “It’s very ugly.”

While emphasising that it’s probably too early to tell what will happen down the line, demand for compliance staff in other industries remains steady, Gilmore said.

“We filled three positions in the last three months where candidates never stepped foot on the property,” Gilmore said. “We’ve

Broadpath

had a pretty good time of it” given the circumstances, said Gilmore.

Matt Frank, a former senior compliance executive at several public companies, is on the job market himself because his former employer’s capital raise was interrupted by the pandemic. He said that, considering the coronavirus, “it’s been oddly busy” from a recruiting standpoint.

“From what I’ve seen, the last few months have been busier than any other time,” he said.

Speaking on a recent podcast, Steve Harrison, a partner at compliance recruiting firm Conselium, said that while compliance has been “a bullet-proof market” for over a decade, the outlook is now “not so rosy.”

officer for Alexion, said he’s been able to do most of his work remotely. “I wouldn’t say it’s slowing down, but it’s perhaps delaying the launch of certain compliance initiatives, such as training modules,” Sharma said.

But there are still hurdles for those in compliance who now find themselves working remotely in a job that often relies on face-to-face meetings.

“It’s daunting,” said Frank, explaining that compliance personnel might worry that teleworking could hobble their ability to “kick the tires” so they can spot and be clued in on certain risks.

“Are you being looped in? Do you know what’s going on? Do you know the risks that are being taken?”

Harrison was somewhat hopeful, however, noting that many compliance consulting practices are looking to hire, and are “sort of gobbling up the talent where they can.”

Technology Takes Over

In the healthcare, pharmaceutical or technology industries, companies may be scrambling to get new controls in place to meet new risks, such as companies who produce essential equipment and are beginning to trade with more countries.

He explained, for example, that transaction monitoring and screening activities that many banks carry out in offshore jurisdictions including India have been severely impacted because it’s more difficult for those employees to do their jobs from home.

Piyush Sharma, who works in the pharmaceutical industry as the deputy-chief compliance

Some compliance processes could run slower as a result, Gilroy said. Another consequence

Some banks and corporations are already having “to do the same work with less”, said Terry Gilroy, a Baker McKenzie partner The healthcare and life sciences industries are who previously worked in-house at UK particularly robust right now, Harrison said. bank Barclays.

QUANTAVERSE ISSUU FLIP BOOK

43

Broadpath

is that some financial institutions could decide they can get by just fine without those additional resources, Gilroy said.

In response to such shortages during the pandemic, some companies are moving towards the use of technology. Lawyers have asserted, however, that while technology continues to play an important role, “it cannot replace” on-the-ground supervision and compliance reviews.

Most companies still have very basic compliance functions, which really only get upgraded during an enforcement action.

In recent years there has been more chatter around introducing data analytics in a compliance programme, a fancy phrase for collecting and drawing new insights from large swathes of information, to speed up and improve decision-making.

But since the pandemic hit, “there’s a necessity and thus permission to do

44

QUANTAVERSE ISSUU FLIP BOOK

things differently,” said Amanda Raad, a partner at Ropes & Gray. “Some that were stuck with the old ways of established infrastructure all of a sudden have permission to try new things out now.”

Some financial institutions have hired compliance technology consultancies because the pandemic has exposed vulnerabilities in their old compliance methods, said David McLaughlin, who runs artificial intelligence compliance firm QuantaVerse. “It’s been put right in front of their face the vulnerability of the human workforce,” he said.

Banks have been asking his company to build contingency plans to use technology to step in and make up for a staff shortfall, if needed, he said.

“Once we are out of the crisis, it will become a part of their compliance,” McLaughlin said, when discussing the long-term implications.

Broadpath

Quantaverse Alert Investigator: Compliance Automation to Overcome Resrouce Shortage During Covid-19 and Beyond By: Carin Ray | June 2, 2020

Abstract COVID-19 is prompting dramatic shifts in customer behavior as they exponentially increase the use of digital and remote channels for regular purchases and financial transactions. Rapidly evolving crisis situations like this provide fertile ground for financial criminals who take advantage of the uncertainties and vulnerabilities in the system to commit frauds for personal financial gains, and funnel the illicit proceeds into money laundering, terrorism financing, and other nefarious acts. Financial institutions (FIs) must remain vigilant and guard against the elevated financial crime risks at a time when their internal operations are disrupted due to the need for social distancing. Compliance processes at many FIs are not designed to support remote work at a large scale; worse, many parts of compliance operations are not automated, needing significant manual efforts that slow down client onboarding, transaction processing, and cause huge backlogs of cases.

QuantaVerse, a provider of artificial intelligence (AI) powered anti-money laundering (AML) technology solutions, has launched a new adaptation of its QuantaVerse Alert

Investigator solution that is intended to automate alert investigation and business continuity for banks impacted by COVID-19.

There are two main ways this solution can aid staff-constrained compliance divisions.

First, the use of AI automates many steps that compliance professionals would otherwise need to conduct manually to investigate and resolve alerts. This can reduce time and effort needed in the investigation process and enable efficient management of case queues and backlogs. Second, cloud deployment accelerates adoption time and the cloud-based solution facilitates remote work and collaboration among investigation teams. Additionally, cloud deployment optimizes capital and contractual commitment that can be advantageous for banks that are currently operating with a reduced security staff and budget.

Third party vendor due diligence can be challenging for FIs while onboarding new vendors, especially younger fintech companies. Another challenge while adopting AI-driven solutions is testing and validating AI models because they can sometimes be perceived as black boxes with limited explainability. To overcome these challengQUANTAVERSE ISSUU FLIP BOOK

45

Broadpath

es and expedite the vendor onboarding process, QuantaVerse provides independent third party model validation documentation and standardized information gathering, as well as an addendum documenting capability and execution steps for business continuity and disaster recovery plans.

The uncertainties and vulnerabilities created by COVID-19 will likely persist for some time, as will the strain due to

46

QUANTAVERSE ISSUU FLIP BOOK

workforce shortages at FIs. This situation is prompting technology revaluation, modernization, and upgrade in several areas including in financial crime compliance and favoring the adoption of automation and efficiency-enhancing tools. A cloudbased and AI-powered solution such as the QuantaVerse Alert Investigator should therefore be viewed favorably by FIs struggling to meet the challenges created by the pandemic.