The series follows a lawyer, with ties to the underworld, who becomes a mole for a group of journalists. Using a hidden camera, she unveils a network of criminal activities, only to later have second thoughts about the TV production. The revelations are both chilling and enlightening, shedding light on the sophisticated methods criminals use to exploit authorities, as well as legal and financial systems.

Government-appointed Lawyers: The Unexpected Facilitators

One of the most startling revelations is the role of court-appointed lawyers. Tasked with assisting in the closure of companies in foreclosure, some of these lawyers have been found to aid criminals in shutting down companies with massive debts, including those owed to tax authorities. 

They also advise clients on how to commit social crimes, such as cheating the public sector on wage guarantees during bankruptcy. This betrayal of public trust underscores the need for stricter oversight and accountability within legal professions.

The Menace of Invoicing Factories

The documentary highlights the pervasive issue of invoicing factories. Criminals launder money by creating fictitious invoices, which are then funneled into legitimate businesses. This practice not only facilitates money laundering but also leads to significant fraud within the VAT system. 

The involvement of respectable businesses in these schemes underscores the complexity of financial crime and the necessity for robust detection mechanisms.

Gang Involvement in Environmental Crimes

Another disturbing aspect revealed is the involvement of gang members in environmental crimes. Officially retired due to illness or disability, these individuals, through straw men and fictitious directors, run contracting companies that dispose of polluted building materials. 

These materials are mentioned to be dumped on farmland and in fragile natural habitats, including areas close to kindergartens it has shown. The environmental impact is devastating, highlighting the far-reaching consequences of financial crimes.

The Unholy Alliance: Businessmen, Lawyers, and Gang Members

“Den sorte svane” paints a picture of an unholy alliance where esteemed businessmen, lawyers, and gang members regularly meet to discuss and orchestrate criminal activities. These meetings illustrate the brazenness with which these crimes are planned and executed. 

The ease with which new acquaintances are brought into the fold, based solely on endorsements from known associates, is particularly alarming.

Identity Theft and Exploitation

The documentary also exposes the rampant theft of identities. Vulnerable individuals, often drug addicts or those indebted to gangs, are coerced into handing over their identities. These stolen identities are then used to set up corporations, through which large sums of money are laundered. The victims are left with nothing, while criminals continue to exploit their identities for financial gain.

Key Takeaways for Fraud and AML Teams

The revelations from “Den sorte svane” underscore the urgent need for enhanced fraud prevention and AML measures within Nordic financial institutions. Here are some key takeaways:

• Graph and Mapping of Relations: It is crucial to develop advanced graph and mapping capabilities to understand the intricate relationships between customers. This enables the identification of suspicious networks and connections that may indicate criminal activities.
• Real-time Deviation Analysis: Strengthening deviation analysis to operate in real-time is essential. Delays in detection and response can allow criminals to complete transactions before alerts are acted upon.
• Tighter KYC Procedures: Implementing real-time KYC or Perpetual KYC (pKYC) procedures is vital. Banks must quickly react to changes in directorships and corporate structures, utilizing data from public sources like the CPR or CVR.
• Information Sharing: Enhanced information sharing both within the financial sector and with public authorities is imperative. Without this collaboration, detecting and preventing these sophisticated schemes becomes significantly more challenging.

Den Sorte Svane: A Call to Action for Nordic Financial Institutions

“Den sorte svane” serves as a stark reminder of the complexities and dangers of financial crime. For Nordic financial institutions, the documentary is a call to action. By adopting advanced detection models, real-time analysis, tighter KYC procedures, and enhanced information sharing, we can better protect our financial systems from exploitation.

Data lies at the core of any effective AML program. To embrace greater AML transparency, banks need access to comprehensive, accurate, and meaningful data. 

The good news is that, in many cases, such data exists within the bank’s ecosystem. The bad news is that the correct data isn’t always available to AML departments. Sometimes, the data utilized by AML systems and investigators isn’t helpful.

AML transparency is not just about access to data. It’s about enabling effective analysis and presenting its results in a way that is clear for stakeholders. Learn the critical challenges with banks’ AML data strategy and how improved data practices can enhance their AML programs’ transparency and decision-making.

2 Common AML Data Challenges for Banks

High-quality data is critical for banks to detect and prevent financial crimes. However, the data landscape is far from ideal, presenting various challenges. These challenges include:

1. AML Data Blind Spots

A significant amount of data never finds its way into AML monitoring. This creates blind spots in a bank’s defenses. 

For example, some data is only available in front office systems. Meanwhile, other channels might disconnect from AML systems. Other data may be accessible but only available in a format that is too difficult to utilize effectively. 

In the past, front office staff helped by noticing strange behavior and giving more information for investigations. However, the digital age has led to a decline in human interaction within the banking sphere. This has significantly reduced such controls’ effectiveness. 

In other words, digital banking channels have replaced brick-and-mortar branches as the primary client interface. AML monitoring in today’s world requires better data usage from online banking channels.

2. A Deluge of Irrelevant AML Data

Moreover, the issue isn’t just the lack of data; it’s also about the quality of the available data. Low-quality information inundates many AML systems. This contributes to a high false positive rate and inefficient investigations. 

This information overload hinders investigators from dedicating their valuable time to substantial investigations. Instead, lots of confusing, irrelevant, or misleading data bog them down. The prevalence of high-volume but low-value data muddies the water and interferes with AML teams’ ability to see the most essential information.

Both of these issues with data transparency can hinder AML teams’ effectiveness, making it harder to make and rationalize their decisions. Banks must reconsider how their AML programs embrace automation, give compliance professionals the tools needed to make decisions, and ultimately stay ahead of new regulations.

7 Strategies to Improve AML Transparency

To enhance AML transparency, banks must employ strategies that address these challenges and pave the way for more effective anti-money laundering efforts. The following steps provide a robust roadmap for banks to overhaul their AML transparency.

1. Flexible Systems for Rapid Adaptation

Automation is already a key feature in many banks’ AML systems. This typically refers to the rules-based monitoring many banks use to trigger transaction monitoring reviews. However, in today’s rapidly evolving banking environment, relying solely on static rules is ineffective. 

Criminals quickly adapt and outmaneuver, and AML programs must keep up. Instead, banks need flexible AML systems that quickly adapt to new developments. AML monitoring should be viewed as a dynamic process that adjusts swiftly to align with the industry’s shifting needs.

2. Reevaluate the Relevance of Data

As banking trends change, so does the relevance of specific data types. New data types should become part of the AML review process when new channels and technologies emerge. Conversely, previously reliable data may need to be updated as client behavior changes. 

Banks must carefully select the most relevant data to incorporate into their AML systems and adapt their monitoring approach for optimal use. AML transparency depends on presenting clear narratives, which makes it imperative to understand which data holds the most significance for AML teams’ decision-making processes.

3. Stay Ahead of Regulatory Changes

As new payment types and channels emerge, so do new financial crime risks. Banks must proactively familiarize themselves with these new avenues and understand how criminals exploit them to effectively combat these challenges, even ahead of regulatory changes, which can be slow to appear. 

By taking the initiative in implementing proactive controls, banks can remain ahead of regulatory changes and, in turn, protect their customers effectively.  Banks that understand new threats and prepare for new rules will be better situated when new regulations or guidance are issued.

4. AML Teams Should Act as Business Partners, Not Preventers

In too many cases, AML departments are perceived as obstacles in a bank’s path to business development. This is partly because the culture of AML compliance is to avoid unnecessary risk. However, banks will want to embrace new payment channels and methods, and AML teams can be an essential part of these initiatives. By assuming the partner role, AML teams can help their banks navigate new payment channels and enable business growth while continuing to mitigate AML risk.

5. Challenging the Status Quo

Many AML processes have been practiced the same way for a long time. These include highly manual tasks and reliance on repetitive workflows. 

However, banks can no longer accept these processes as the norm because they’ve always been done that way. It’s time to embrace more advanced automation, AI, and machine learning to make AML processes more transparent. This both improves efficiency and helps banks understand how decisions are made, thereby identifying opportunities for improvement.

6. Ensuring Explainable Decisions

With the growing prevalence of AI and machine learning, the transparency of decision-making is paramount. Banks must ensure that the decisions of their systems are transparent and easy to understand, even for individuals lacking technical expertise. After all, how can banks truly embrace transparency if they cannot explain their decisions to relevant stakeholders?

7. Nurturing a Culture of AML Transparency

Nurturing a culture of AML transparency is not just a step; it’s an ongoing commitment. Banks must instill this culture across all levels of the organization, from top leadership to operational and IT staff. Transparency should be a guiding principle in all AML practices and policies, including data and technology strategies. It’s vital as banks increasingly embrace digital approaches and rely less on front-office staff for essential insights into customer behaviors.

The need for AML transparency is not a choice; it is a necessity. To maintain the integrity and security of the financial services industry, banks must centralize clean and relevant data, ensuring it is complete and accurate. In a world where financial crime is an ever-present threat, transparency is the key to strengthening the industry’s defenses against money laundering and financial crimes. 

This complicated anti-money laundering systems architecture results in a higher cost of ownership for banks and financial institutions. Simply keeping legacy AML systems in place to appease the status quo won’t cut it, especially as the banking industry faces pressure to embrace real-time transaction monitoring in AML.

Banks are in an AML Innovation Arms Race — Whether They Realize It or Not

This year, we saw the tech giant Google partner with HSBC, an influential global bank, to launch an AI-powered solution for anti-money laundering. The solution, known as “Anti-Money Laundering AI” or “AMLAI,” is a cloud-based solution that uses artificial intelligence and machine learning technology to uncover suspicious transactions. 

The partnership between a top tech player and a global financial institution effectively kicked off an arms race in the banking sector. Banks must innovate their AML programs, notably in transaction monitoring, to keep pace with the latest technological innovations. As banks look to implement real-time AML decisioning, they will need to prove the data they have provided is accurate, complete, and readily available. 

4 Anti-Money Laundering Systems Architecture Challenges

Before financial institutions (FIs) can rush to innovate, they must first accept the shortcomings of existing AML legacy systems.

Legacy AML solutions are disparate

Too many AML systems are composed of multiple technologies at different maturity levels. The fact is they are disparate solutions of different technologies, vendors, and maturity, some in implementation, some much older, with a lot of IP and complexity written into them. 

This arrangement means data can reside in multiple systems and is very difficult to support from the perspective of data analytics, data modeling, data availability, and data quality. Using multiple technologies also creates a complicated customer experience. End-users must log into several systems to obtain data. Since these systems don’t communicate clearly with each other, end-users could wind up with conflicting information, producing many false positive alerts. If the most recent and relevant data is unavailable, the lag time can affect customer screening, customer KYC processes, and transaction monitoring, making suspicious activity reporting difficult. This is further compounded when data is needed for AI and machine learning processing.

Moreover, governance of different systems can stretch operating systems and drive up the bank’s ownership cost. Overseeing and maintaining multiple systems requires bank staff to connect insights across various tools. Juggling several governance requirements results in operational and financial inefficiencies. 

The end result is added strain on investigative staff. These professionals are expensive and passionate about AML. However, they can’t focus on where existing or new risk typologies reside or evolve because of existing governance. This isn’t good for the institution either because it increases the total cost of compliance.

Changing systems is an expensive undertaking

Banks invest a lot of effort and time into building the rules, scenarios, testing, and governance behind their anti-money laundering systems architecture. As a result, the cost and risk of change are incredibly high.

For example, if a bigger bank has invested $20 million in an AML system, the cost to change a solution may seem low. However, the cost to replicate the intellectual property and processes behind the system, and the update for approval and governance, can be greater than the original solution’s cost, license price, or implementation. In other words, banks have already invested large sums in their existing infrastructure and are reluctant to spend more to change it.

Changing the status quo is challenging

Finding a suitable replacement is not just challenging because of the technological requirements. There is also a tangible risk in replacing a legacy system that can affect multiple stakeholders. 

A head of compliance like a Bank Secrecy Act (BSA) officer or a money laundering reporting officer won’t be easily swayed by a vendor claiming they can reduce operational costs by 20% by implementing a new solution. Instead, these professionals want proof that the new solution won’t miss any risk coverage like potential money laundering transactions. If the new solution fails to do its job, these team members could cause a non-compliance problem or land in legal trouble.

Updating or even adjusting an AML system can be costly. But winning the hearts and minds of different organizational stakeholders can be even more challenging – and expensive.

Too much vendor noise

Even if stakeholders agree that their AML legacy system requires an update, the question remains: which vendor to choose? 

If you’re like me, your inbox is probably overflowing with vendor outreach messages promising to fix some of your most pressing issues. Bringing new technologies into a bank’s infrastructure involves a rigorous procurement process. Even after completion, a bank’s IT or engineering team may return to the vendor for specific questions. 

In the worst-case scenario, imagine the vendor shutters or goes out of business. The bank will have invested in a costly solution and cannot connect with anyone who understands how the solution works.

How Banks Can Improve AML System Architecture 

As banks seek to implement real-time transaction monitoring, they must address some of the maintenance challenges of their existing anti-money laundering systems architecture. Here’s how banks can embrace solutions that enable smarter AML maintenance.

Pick scalable solutions 

Any AML solutions a bank implements should not be static. Instead, it should be flexible enough to evolve with changing needs and circumstances. On-prem solutions are ill-suited for this reality. It’s smarter to rely on cloud-based solutions to connect to your bank’s network to install patches and run updates. This arrangement also offsets the need to procure new physical servers and hardware.

Focus on cost reduction

Scalability should be sufficiently elastic, so you’re operating at peak capacity. If you have a lot of headroom you’re not using, there’s a cost associated with that. A cloud-based infrastructure can scale up and down and cost exact demand at a precise time rather than carrying an overhead of capacity. The elasticity of demand requirements of the capacity provision. Look for solutions that enable banks to pay for capacity as needed, resulting in smarter AML infrastructure spending. They should also allow greater flexibility for testing new highly data-intensive typologies with large historical data sets.

Focus on compliance-oriented requirements

Data moves quickly in the digital age. Any solution a bank implements should be managed as if in your own environment. Banks relying on a 3rd party vendor to handle data must ensure the vendor complies with the jurisdictions where the bank operates.  

Whether they like it or not, the banking industry is essentially in an arms race regarding innovation. New innovations are being released on a regular basis. Banks that fail to adapt quickly or that adapt to today’s groundbreaking innovation in five years will likely be 10 years behind their competitors. 

Legacy anti-money laundering systems architecture has numerous maintenance challenges and a high bank ownership cost. While many stakeholders are likely reluctant to embrace updates and changes to their AML systems architecture, waiting to innovate is not an option. Recent high-profile partnerships between banking and technology organizations demonstrate that real-time AML transaction monitoring is on the horizon.

To prepare for this new reality, banks must enhance their anti-money laundering systems architecture with the latest solutions. Otherwise, both customers and regulators alike will be able to detect the pig beneath the lipstick.

Unfortunately, the staggering level of AML false positive burnout can contribute to a talent gap in the industry. Financial institutions should find ways to improve their workflows, even though there may be many incorrect results. That’s where advanced technology can be a differentiator.

Here’s how banks can help compliance teams avoid AML false positive burnout.

What are AML False Positives?

Anti-money laundering efforts produce false positives when they mistakenly mark a genuine customer’s transaction (or multiple transactions) as suspicious. However, upon closer inspection, nothing suspicious is found. This happens when the transaction(s) triggers one or more of the financial institution’s rules. As a result, the transaction is flagged as a potential money laundering activity.

Around 95% of system-generated alerts are considered false positives by some estimates.  This large number of AML false positives in transaction monitoring is music to the ears of financial criminals and money launderers. The longer analysts chase false positives, the longer criminal activities can continue uninterrupted. Ultimately, financial institutions waste valuable resources pursuing non-AML risks.

3 Ways False Positives Contribute to AML Analyst Fatigue

The overwhelming volume of false positives weighs down review teams, contributing to AML analyst fatigue in three key ways:

1. The Human Factor. Chasing false positives can feel like spinning relentlessly on a hamster wheel. Many analysts want to channel their inner crime-fighting detective by pursuing meaningful investigations and stopping financial criminals. If they find their job is simply a checklist task, they eventually feel bored and disparaged by the work. Many analysts will consider working at another company or leaving the role altogether if there are no clear career advancement opportunities.
2. The Tech Factor. The state of an organization’s technology can also contribute to burnout. The results can be devastating if an FI’s system relies on hundreds of rules operating ineffectively. This arrangement creates a tall maintenance order that analysts, specialists, and managers must address. Giving analyst teams a system that requires constant maintenance and oversight is a frustrating experience that negatively impacts morale.
3. The Repetition Factor. Analysts often also feel bogged down by the repetitive, manual tasks that are part of their responsibilities. These tasks include accessing relevant customer data for investigations. Performing these manual tasks over and over again can leave analysts feeling stressed.

The 4 Real Costs of AML False Positives

Banks risk severe repercussions if the factors contributing to high false positive rates and analyst burnout are left unchecked. Banks that do not address false positive burnout among their AML compliance teams take significant risks in four key areas:

1. Operational Costs.  False positives ultimately add friction to legitimate customers’ transitions and waste analysts’ time. These FIs risk damaging their operational margins if their false positive rate keeps rising. As the FI’s volume grows, so will its false positive rates and operational costs (as well as opportunity costs). 
2. Internal Audits. Large alert volumes take longer to investigate. FIs risk violating their service level agreements (SLAs) and disrupting their internal processes if they do not quickly investigate alerts. This could have regulatory consequences as regulators audit workflows to understand why the organization failed to follow its SLA.
3. Criminals Go Undetected. Arguably, the most severe consequence of false positive overload and analyst burnout is that illicit activity goes undetected. Criminals keep making money from illegal activities like drugs, guns, trafficking, and terrorism. Meanwhile, analysts waste time on wrong leads.
4. Compliance Talent Shortage. The banking industry is facing a significant talent shortage. Professionals’ increased workloads – often driven by false positives – can drive talent away. Some organizations are turning to AI and machine learning to uncover money laundering activities. These organizations may become top destinations for AML compliance talent by making their daily jobs more manageable.

3 Steps for Banks to Reduce AML False Positives in Transaction Monitoring

Reducing false positive rates mitigates AML team burnout. Here are three steps banks can take to keep team members engaged and improve operational efficiencies.

1. Implement Strong AML Training Programs

Analysts want to succeed at their jobs but will feel discouraged if they spend most of their time addressing AML transaction monitoring false positives. Banks can train AML analysts to better identify suspicious activity and its opposite, improving their effectiveness in preventing it. Training programs engage staff in AML trends, motivating them to try new tactics and prevent crime.

2. Cross-Train AML Compliance Professionals

Giving team members exposure to different functions and activities will keep teams engaged. Rotational programs are a great way to offer team members career advancement and skills development. This will help them view their original role better and understand how it affects other parts of the organization.  

3. Upgrade Your Bank’s AML Technology

FIs need low-maintenance technology systems they can trust. Staff who aren’t worried about their transaction monitoring system breaking or spending most of their time maintaining rules are focused on investigations. Ask your FI’s service department about your team’s issues and how leadership can help. Research what other companies are using and how they like their systems. Finally, consider implementing machine learning solutions that automatically perform some repetitive, manual tasks that bog down AML teams. That solution should also be smart enough to help you decide when to alert (and when not).

AML compliance teams are committed to doing their jobs and stopping financial criminals. They should feel supported in their roles. FIs must help team members avoid burnout from false positive fatigue and keep their teams focused on productivity. Providing proper career development, opening rotation opportunities, and investing in the right technology are critical to impacting false positive burnout. Investing in advanced technology will be crucial in helping banks address the industry’s skills shortage and avoid losing hard-to-find talent to competitors.

We’re thrilled to share the findings from our recent report, The State of Global AML Compliance 2023. This comprehensive analysis is based on survey responses from 271 AML professionals hailing from 77 global companies. The insights offer a panoramic view, covering the overall sentiment, variations by roles, and regional intricacies.

Key Takeaways from Feedzai’s State of Global AML Compliance 2023 Report

1. AI and the AML Nexus: Over half of the respondents (51%) believe AI is pivotal to the future of AML programs. Integrating AI technology in compliance and fraud detection can reshape our approach to financial security.
2. Challenges in Modern Money Laundering: A significant portion (46%) identifies the escalating sophistication of money laundering techniques as one of their biggest challenges. These evolving tactics necessitate an equally adaptive AML strategy.
3. The Crypto Connection: A striking 53% reported that money laundering activities are widely connected to cryptocurrency transactions. Yet, 40% admit they are not leveraging technology to monitor these transactions. This poses the question: Are we keeping pace with the digital currency revolution?
4. Effectiveness of Current Regulations: An overwhelming 80% opined that money laundering regulations, in their current form, are, at best, somewhat effective. This calls for a collective re-evaluation and, possibly, a paradigm shift in regulatory framework

These insights, while revealing, are just the starting point. A wealth of detailed findings and nuanced interpretations is waiting to be explored.

Join the State of AML Compliance Conversation on September 13th

I’m thrilled to moderate a webinar on September 13th, where Feedzai’s Karin Yuklea and Nick Parfitt will delve deeper into the report’s findings. But this isn’t just a one-sided presentation; it’s a platform for open dialogue. We want to hear your thoughts, concerns, and vision for the AML future.

As an AML professional, you’re an integral player in this evolving narrative. By understanding the global perspective, you can better anticipate challenges, leverage opportunities, and actively shape the AML landscape.

So, grab a cup of coffee, block your calendar, and join us for a candid conversation with global peers to challenge prevailing notions and co-create the roadmap for a safer, more transparent financial future.

In this blog, we’ll outline key money mule red flags and how banks can use novel technology to protect themselves and their customers. 

What are Money Mules?

A money mule is a person who, intentionally or unintentionally, gets money from criminal activity and passes it on, getting a small share of it in return. It can also be someone duped into thinking they are applying for a job online. Alternatively, a bad actor can use a romance scam to manipulate victims into moving money on their behalf.

Why Are Young People at Risk of Becoming Money Mules?

Young people are frequently targeted for money mule recruitment. That’s because this group spends considerable time online and is often more trusting of people they meet on social media and other digital channels. 

Plus, most younger people don’t have deep financial resources compared to older adults. Too often, this situation makes them highly vulnerable to money-making opportunities that seem too good to be true (because they are!). As a result, they may hastily dismiss the red flags of a money mule recruitment scheme. Many young people also don’t fully understand the gravity of the crime they are committing, instead seeing it as easy money that doesn’t harm anyone.

Social engineering is one of the critical things about money mules and how criminals are successful. They target vulnerable people who may be eager to make what sounds like easy money.

Students, for example, are at exceptionally high risk because they could be approached at a university by somebody who is in an organized gang. The criminals give them a pitch such as, “Hey, look, we just need to use your bank accounts. You know, small amounts going in and out, and you take a commission. It’s no big deal and easy money for you.” 

Unfortunately, the students won’t realize these seemingly harmless money transfers have serious legal consequences. 

Interpol recently has been running a campaign since August 2022. The campaign “Your account, your crime” is focused on placing the burden of responsibility back onto the individual. The campaign’s goal is to remind people that whether money mules are aware of their crimes or not, they can face serious legal consequences for their participation. 

How Behavioral Biometrics Identifies Money Mule Red Flags

But there’s also the chance that criminals take over a person’s account. For example, the person’s credentials could be leaked on the dark web and compromised without the account owner realizing it.

Using the stolen data, criminals can access the person’s bank accounts and move money in and out without necessarily knowing about it. Unless the account owner reviews their statements regularly, the crime can go undetected for a long time. 

Behavioral biometrics is a powerful technological solution to counter account takeover attacks after leaked credentials. Behavioral biometrics ensures that the person operating the device has built up a profile with the bank for a long time. The technology works by confirming the person’s identity and monitoring how they handle their mobile devices. The technology measures the tilt of the device, how quickly the person types, how much pressure they put on the keypad, or how fast they enter data. These behavioral patterns are challenging to mimic if you’re not that person. 

Money Mule Red Flags for Banks to Monitor

While it’s a challenge for a bank to spot a money mule, there are several red flags banks can monitor.

Track Known Transactional Activities

The first one is to monitor an existing account’s transactional patterns. Do the customer’s activities fall out of the norm of how they usually behave? Understanding the CDD profile and the bank account’s original intentions is critical and may require tuning your transaction monitoring systems.

For example, your bank may start seeing an account make international transfers, which the customer was not using or used very infrequently. This is a red flag for money mule activity.

Watch for Money Mule Commissions

Banks can also monitor if customers collect a commission related to suspicious or criminal activity. Let’s say the customer earns a commission for funds coming in and going out. The amount retained in the account is 10, 12.5, or 15% of the total. This is a red flag that strongly indicates the customer is taking a commission off the funds. 

This is especially concerning when you think about the rapid movements of funds. When payments come in, 10% gets taken off, and then a transfer goes out within a very short period. 

Look at Known Money Mule Data

Banks can also protect themselves by looking for more novel sources of known mule account information. This includes looking on the dark web, where credentials of individuals and known account takeovers have been published. Banks can use that information as part of the inbound payments stoppage process. 

The information can also support anti-money laundering (AML) and transaction monitoring investigation processes. 

What if a mule is identified outside the organization, but it’s trying to get into the organization, or vice versa? Banks can use novel techniques like link analysis to understand those payment flows and other potential accounts that may be compromised or part of a more comprehensive network. 

New legislation will go into effect in the UK next year, holding financial institutions liable for fraud coming in and moving out of their organization. With the money mule threat rising, banks must carefully consider if their defenses can identify critical red flags to identify money mule activity.

The Top 3 SAR Filing Pain Points

Financial institutions file suspicious activity reports (SARs) with regulatory bodies whenever suspicious transactions are flagged. This includes agencies like the Financial Crimes Enforcement Network (FinCEN) in the United States, BAFIN in Germany, TRACFIN in France, COAF in Brazil, and Bank Negara Malaysia in Malaysia.

What makes the SAR filing process such a minefield? Let’s review.

1. SAR Filing is a Highly Manual and Labor-intensive Process

Banks and financial institutions are required to file SARs whenever a transaction is suspected of being tied to illegal activity. Analysts must collect relevant evidence, including transaction details and adverse news, and manually attach them to the SAR. This can be highly time-consuming for analysts.

2. The SAR Process is Highly Error-Prone

SAR reporting also requires manually entering personal information like names, addresses, and dates of birth into SARs forms. Any information entered inaccurately can compromise the SAR’s accuracy and hamper any relevant investigation.

3. It’s Important to Explain Your Decisions

Filing a SAR is not the end of the story. Regulators will want to understand the decision-making process. Compliance professionals must be able to produce an audit trail of how an analyst reached the decision outlined in the SAR. Many organizations use a Maker-Checker process or a Four-Eye check. In this process, a second person, usually a manager, reviews and verifies the completeness and quality of the information before a SAR is submitted. 

4. Dependency  on Vendors

Rules-based systems are common for AML transaction monitoring, but many systems create a high rate of false positives. Banks need to be agile in adjusting and tuning rules to get productive alerts and effectively respond to the latest financial crime patterns. However, some transaction monitoring systems are rigid and aren’t self-serviceable – forcing banks to rely on their vendor to adjust the rules when necessary. Further delays occur when banks and financial institutions don’t have a self-service option for transaction monitoring, clogging up their alert queues with overwhelming false positives and impacting their ability to report suspicious activity effectively.

The 3 As of Seamless SAR Reporting

Feedzai’s SAR Manager upgrades the conventional SAR filing process by offering banks a self-service option. SAR Manager improves the traditional reporting process by introducing three As into the workflow: auditable, automation, and autonomy. 

Auditability: Strengthening Compliance Efforts

Maintaining a comprehensive audit trail is essential in suspicious activity reporting. SAR Manager provides transparency in all updates and decisions made in the system. This level of audibility ensures that banks have all the necessary evidence to explain their decision-making process to regulators and law enforcement agencies. Additionally, SAR Manager includes a SAR activity log, which records every action taken, such as the creation and updates of SARs, along with date and time stamps. This feature simplifies compliance efforts and reinforces transparency.

Automation: Streamlining the SAR Reporting Process

SAR Manager saves time and enhances accuracy by minimizing manual errors. It also provides a more intuitive and automated user experience for analysts. The system automatically populates entity details and guides analysts through creating suspicious activity reports. Analysts gain access to dynamic form building and are prompted to fill out relevant information, ensuring a more streamlined and error-free process. 

Autonomy: Putting Control in the Hands of Banks

Further upstream, Feedzai’s AML Transaction Monitoring solution has self-service rules, allowing banks to better manage their risk strategy and rules performance. Once an analyst determines that a suspicious activity report is warranted, they’re able to utilize the integrated SAR Manager within the same user interface. One key advantage of SAR Manager is the ability it provides to banks to configure roles and permissions for different analysts and managers. This self-service option allows banks to tailor the system according to their specific needs, creating distinct roles such as Level 1 analysts, Level 2 analysts, and managers. By granting banks the tools to configure these roles internally, SAR Manager eliminates the need to rely on external vendors for making changes, thereby enhancing efficiency and reducing dependency.

How Feedzai’s SAR Manager Works

SAR Manager’s true strength lies in its ability to integrate seamlessly with other anti-money laundering tools. It provides a consolidated platform that houses all the necessary evidence and data required for SAR submissions. From Know Your Customer (KYC) and Customer Due Diligence (CDD) to customer and payment screening, SAR Manager offers a comprehensive suite of AML solutions. This eliminates the need for banks to rely on multiple resources, streamlining the entire reporting process and increasing efficiency.

Here’s how it works in operation:

SAR Creation

Banks gain access to a step-by-step guided flow to produce a SAR, structured dynamically according to their local jurisdiction.

Maker-Checker Process

The solution includes a process to enforce a four-eye check. The investigator will create the SAR (the “Maker”) and submit it for approval to their manager (the “Checker”). Usually, the Bank Secrecy Act (BSA) Officer or Money Laundering Reporting Officer (MLRO) approves or rejects the SAR.

Flexible Roles and Permissions

SAR Manager includes configurable role-based access control of a user role for the SAR Manager component that allows permissions to be assigned accordingly. Roles can be assigned based on the following:

• L1 Analysts
• L2 Analysts
• QA Analysts
• Managers
• MLROs

SAR e-Filing

The system automatically generates the XML file aligned with the regulator-specific format for the bank’s preferred jurisdiction. XML files are then made available to be downloaded by the user.

SAR Manager simplifies the SAR reporting process, empowering banks with greater autonomy, automation, and audibility. By putting control back into the hands of analysts and managers, streamlining the filing process, and enhancing compliance efforts, SAR Manager offers an invaluable solution for financial institutions. With the ability to configure roles, automate form filling, and maintain a comprehensive audit trail, banks can optimize their SAR reporting capabilities and improve overall efficiency in combating financial crime.

Rules-based legacy systems simply aren’t able to address the sophistication of today’s financial crime tactics. Banks need to optimize their anti-money laundering (AML) alerts to effectively detect and intercept suspicious activity. 

Here’s how Feedzai addresses the challenge using patented AI technology to optimize AML alerts — without requiring banks to immediately retire existing rules-based systems entirely. 

4 AML Challenges for Compliance Officers

Before looking at how next-generation anti-money laundering solutions can help, let’s first look at the common pain points caused by rules-based legacy systems.

Many Banks’ AML Systems Were Built in the Pre-Digital Age

The financial services industry has experienced a digitalization boom in the past few years. However, many existing AML systems use a rules-only approach to transaction monitoring. Without next-generation anti-money laundering methods in place, banks’ legacy systems will be unable to meet the demands of the digital banking workflow. The result is a large amount of false positive alerts. This leaves compliance teams scrambling to address non-issues instead of focussing on the real threats. 

Compliance Officers Feel Burned Out

It’s estimated that about 95% of a bank’s system-generated alerts are false positives. Banks’ compliance officers must address this struggle to remain focused and avoid being disheartened. A recent survey found that nearly half (49%) of compliance officers feel overwhelmed at least weekly. If compliance officers can’t trust the quality of AML alerts, they risk spending valuable time on unproductive suspicious activity reports (SARs). In other words, compliance professionals often chase false positives instead of focusing on high-value alerts and identifying financial crimes. No wonder so many compliance officers feel burned out on the job.

Despite Heavy AML Investments, Banks Still Face Fines 

Banks worldwide spend billions on financial crime compliance each year. Yet despite these heavy investments, global regulators fined banks roughly $5 billion for anti-money laundering failures. That’s a 50% increase in fines issued a year earlier. In other words, after investing large sums of money to fix a problem, the problem persists. 

Banks clearly need a more effective alternative if they want to stay ahead of financial crime trends. This means adopting next-generation anti-money laundering solutions for next-generation challenges while causing minimal disruption to existing processes. That’s how Feedzai’s AML solution optimizes alerts for more effective compliance.

Barriers to Embedding Machine Learning in AML

Machine learning is critical to enhancing AML reporting. However, there has been some reluctance in the past to implement it. There are several reasons for this. First, regulators have been busy clarifying the guidelines for appropriately using artificial intelligence (AI) and machine learning. For example, an EU-based challenger bank was recently fined for using AI in its AML workflows by its central bank but successfully overturned the decision in the courts.

Second, while some banks understand machine learning’s potential, they are concerned that the investment required to shift to machine learning will mean too much disruption for their operations. Taken together, banks have been reluctant to invest in machine learning technology.

How Feedzai Delivers Next-Generation Anti-Money Laundering Solutions

These are significant obstacles to machine learning implementation for AML operations. But Feedzai’s AML solution optimizes alerts to help reduce compliance officers’ workloads. Here’s how.

Compliance Teams Appreciate Optimized AML Alerts

Next-generation anti-money laundering capabilities start with more effective AML alerts. Feedzai’s AML optimization capabilities prioritize the most critical alerts, creating a domino effect that addresses many other obstacles outlined here. Compliance officers will be able to focus on higher-priority alerts. These alerts require the greatest attention because they are more likely to result in a SAR filing. When the teams are at capacity, they will know exactly what to address first without feeling they’ve missed potentially critical alerts. In other words, AML optimization can reduce work fatigue.

Keep Existing Rules-based Systems

Many banks put off implementing AI and machine learning investments. This shift can be especially challenging for banks that still rely on rules-only legacy banking systems. Fortunately, banks that cannot immediately shift away from their rules-based system can still benefit from using machine learning for AML. Instead, banks can keep their legacy systems and add machine learning capabilities on top of them. It also helps evaluate existing governance and risk-based approaches with an experienced vendor. 

Embed AML Optimization into Existing Solutions

Feedzai’s AML optimization capability can enhance existing systems by adding an intelligent machine learning layer. With this approach, banks don’t have to wait until a contractual agreement with another vendor expires. They can start seeing the benefits of next-generation anti-money laundering solutions immediately. 

A rules-based approach to AML is insufficient in the digital age. Banks need to embrace next-generation anti-money laundering solutions to meet today’s financial crime challenges. They also need to optimize their AML alerts to reduce false positives, employee burnout, and avoid fines. Machine learning is the smart way to supplement existing rules-based systems and deliver on regulatory priorities.

2 Key Areas Where AI Boosts AML Monitoring

There are multiple use cases where banks can use AI to enhance their AML transaction monitoring capabilities. These use cases typically fall into two distinct categories: detection and operations.

The key principle of detection comes down to a simple concept: keeping your regulators happy. AI can reduce the risk of your financial institution becoming exposed to financial crime. On the operations side, AI can help compliance analysts use their time more effectively, especially when it comes to opening suspicious activity reports (SARs).

Let’s dive further into how AI enhances each use case.

Part 1. How AI Boosts AML Detection in 3 Steps

It’s tempting for banks to think about AI and machine learning as a one-and-done task. After all, once you build a model and train a model, it’s easy to think that the hard work is behind you. However, at Feedzai, we assure you that this is just the beginning of your bank’s machine learning journey. Implementing AI for AML detection requires a vision and execution of several important steps.

We advise our customers to follow a three-step process for their machine learning and AI journeys. 

1. Start by Supplementing Existing AML Rules with ML

Start improving AML operations with existing rules by getting input from your analysts. These rules will generate alerts and the alerts will prompt action from your operations team. This includes filing SARs or escalating alerts. Collecting analyst feedback on the performance of rules and alerts is an essential component of a successful ML system.

2. Add Supervised Machine Learning to Your Rules

After supporting your existing rules with machine learning, you can then implement supervised machine learning. This involves training supervised models based on your analysts’ feedback signals. Like any other machine learning solution, your supervised models will require training until you are confident in their performance. 

For example, is the model capable of reducing the number of false positives after learning the patterns from the rules? If rules are defined well, the models will be able to see new risky patterns in a holistic way. As a result, analysts will have very high confidence when they trigger alerts. Even if the first or second rule triggers an alert, the model will be able to predict with 99% accuracy that it’s a false positive. Once you gain confidence in the model, you can use it to reduce false positive alerts and generate more insightful and useful ones.

3. Implement Unsupervised Machine Learning 

In the third stage, your bank can implement unsupervised machine learning models. There are several activities that may look suspicious to your bank, but analysts will not know exactly what to look for. In other words, what are the known unknowns? Designing rules to uncover known unknowns is extremely challenging.

For example, imagine that you get 10 alerts pointing to strange activity each week. It’s not necessarily suspicious, but it definitely is strange and unexpected. The model has never encountered a situation like this before. Because it’s an unknown event, the model can trigger an alert for further review. Once the review is complete, you can incorporate the feedback back into your rules or your supervised machine learning model.

At the end of the day, you’ll end up with a very mature system combining the rules of a static AI system with supervised machine learning, and unsupervised machine learning, all in one system. With this combination, your bank will become much more agile in its AML transaction monitoring program. 

Part 2. The Operational Side of AML Transaction Monitoring

Beyond detection, banks should also focus on upgrading their operations for AML systems. Enhancing operations can free up how analysts spend their time. Improving operations automates much of the analysts’ workflow and allows them to focus on what’s most important to them instead.

Analysts typically have to work with lots of different systems that require a heavy manual lift. They often need to copy information from multiple sources and paste it into a big Excel spreadsheet. From there, they need to perform a series of manual queries in the document. This process is both error-prone, highly time-consuming, and inconvenient.

This disparate arrangement makes it difficult for analysts to do their jobs efficiently. What would make it easier is to have all the data in one place instead of being forced to consolidate and review hundreds of transactions at a time. This includes the transaction monitoring system, reference data, and relevant enrichments. If all of these elements are centralized in one place, analysts can easily access the important information they need. When an alert is triggered, the system can present that information in the most useful format for the analyst.

Concentrating data in a centralized location can spare analysts a cumbersome review process. They can instead review transactions in a more efficient way, often with the help of visualization tools. From there, analysts can look at network information with graph analytics and determine where money is moving. In other words, analysts can “follow the money” more easily. 

Analysts shouldn’t have to waste time connecting pieces of information as if they were solving a jigsaw puzzle. And they shouldn’t have to manually review their work. By improving operations, analysts can spend more time studying the pattern right in front of them. From there, they can make an assessment if the pattern is suspicious or not, if it needs an escalation, or if they should file a SAR.

AI has transformed significantly in the past year alone. Now is the time for banks to ask themselves how to use this technology to upgrade AML operations and stop financial crime.