Case Management: The Fraud and Money Laundering Connection

When it comes to fighting fraud and money laundering, the connection between the two is critical as fraudsters work to find a way to legitimize their illicit funds. Proceeds from criminal activity related to fraud need to be laundered, so money laundering often follows soon after fraud occurs. Typically, following a successful money laundering activity, fraudsters obtain immediate access to the funds and conceal the proceeds, making them appear authentic.

The worlds of money laundering and fraud have grown more intertwined than ever and there is a growing need for financial institutions to follow unified and consistent investigations processes and controls within their fraud and AML programs. Firms can achieve significant business efficiencies with an integrated fraud and AML investigations platform.

Unified Case Management: The Benefits

Bringing together AML and fraud programs offers many benefits, including:

  • Breaking down silos between fraud and AML compliance functions.
  • Gain a holistic, singular view of suspicious customer behavior across AML and fraud.
  • Increase collaboration among all financial crime investigations teams within a financial institution. 
  • Transfer alerts and cases easily when AML and fraud are within the same investigation platform.
  • Alerts can be transferred to the AML or fraud investigations efficiently after the initial triage is complete. This results in fewer duplicate SARs and more thorough SAR filings.
  • Eliminate redundant investigations and display all AML and fraud alert history for the same subject. With access to more comprehensive information, investigators can find resolutions rapidly. 
  • Much of the data required to detect money laundering is the same data that is needed to prevent fraud. A single case management solution enables sharing common data between fraud and AML, such as accounts and subject demographic data, KYC, transactional activities or account closure.
  • The unification of data provides a holistic view of a customer’s relationship with the bank and any concerns involving the investigated entities.
  • Most of the requirements of the fraud and AML investigations functions are similar. Teams can reduce overlapping functionality in IT systems by building once and reusing the same functions across both AML and fraud.
  • Reduce costs by removing redundant processes and systems.
  • Decrease case management software license fees and maintenance costs.
  • Improve controls and consistency by having a single FinCEN SAR filing platform.

Many financial institutions are only now realizing the many benefits of a unified case management solution.

Looking Forward: A Financial Crime Division

There has recently been a growing movement toward combining the various financial crime functions including AML, CDD, sanctions, fraud and cybersecurity under an enterprise-wide financial crime division. This new trend is now encouraged by regulators like FinCEN, who has released a recent advisory encouraging institutions to promote “greater communication and collaboration among their internal AML, business, fraud prevention and cybersecurity units.”

Breaking down the siloed approach to investigations and connecting disparate information with a case management tool can be the first step toward a unified view of financial crime across the organization. A unified case management solution with consolidated alerts and a centralized data model provides the actionable intelligence and insight needed to gain a holistic view of risk, ensure oversight and maximize operational efficiency in financial crime investigations.

Improving Anti-Money Laundering Compliance with Dynamic Customer Risk Profiling

Since the turn of the century, customer profiling has become more detailed and in-depth as anti-money laundering (AML) guidelines are incredibly important in today’s increasingly data-driven world. 

Prior to that, customer profiling in financial institutions primarily meant gathering details around identification, address verification and occupation of retail customers. The details for entities included additional information around legal documents authorizing the entity to conduct its business, their financial statements, authorized signatories, and so on. Once recorded in the financial institution’s registers, these details were rarely reviewed, unless the customers initiated the changes themselves.

Financial institutions then started defining risk models for their different customer categories (e.g., retail, corporate, banks, government bodies). Risk models of this generation relied on static parameters (e.g., risk models for retail customers used the country of domicile, country of residence, source of wealth, industry from which a customer’s income is derived, length of relationship with the financial institution). Scores for each parameter were assigned based on the input value, and finally an aggregated score was determined. The customer was then assigned a risk rating of low, medium or high by matching the score with the score range for the risk levels.

This absolute risk-scoring model had a limitation as it failed to take into account the weights of each of the risk factors used in calculation. As a result, the weighted average risk-scoring model was born, factoring in the weights to be associated with each risk parameter, which varied with risk models of different customer categories. This risk model is the most widely used by banks and financial institutions today.

Moving from Static to Dynamic

Whether absolute or weighted, both customer risk models use static customer information that does not change frequently. The risk ratings of customers are reviewed at pre-defined frequencies, based on the risk levels assigned to such customers, the higher the risk, the more frequent the review and vice versa. Therefore, every customer remains in his or her risk bucket for a specified period of time, which could range from six months to three years, until the next review and change of risk level if so assessed.

With the advancement of technology and the opening up of various new banking channels, customer behavior has significantly changed in the past decade,from physical branch banking to online accounts all these can now be accessed 24/7, transferring funds across the world has become very easy and even instant in some cases. Such transformations have benefitted customers, but have also provided financial criminals additional ways to launder money. In such a dynamic setup, risk profiling of customers using static information at periodic intervals may mean exposing the financial institution to the threat of financial crimes. This is why customers need to be continuously risk scored based on their activity (financial and non-financial), using a combination of static and dynamic parameters, and monitored based on their updated risks at all times.

How Customer Risk Profiling Impacts AML Compliance

Customer risk profiles are becoming increasingly popular among financial institutions for building risk-based AML compliance frameworks. Though their sophistication may vary in this aspect, we are witnessing a global trend of financial institutions transforming their financial crimes compliance systems, processes and policies to risk based programs. Customer risk profiling is now being integrated into the AML process flow, to strengthen oversight and create triggers for enhanced monitoring. Financial institutions now follow the rules below as a discipline:

  • Periodic review of customer profiles are based on their risk levels (i.e., higher the risk level, more frequent is the review)
  • Controls are applied on customers and their activities based on their risk levels. Enhanced due diligence (EDD) is conducted on high risk customers, and lower thresholds applied on their transaction limits across various products for stringent monitoring
  • Specific scenarios are designed to monitor financial and non-financial activities of high-risk customers, triggering alerts on deviations or breaching thresholds
  • Alerts generated on suspicious transactions are risk-scored higher if the customer involved is a higher risk one. This results in prioritization of such alerts and greater due diligence for investigation

Using Machine Learning for Dynamic Customer Risk Profiling

Machine learning has been transforming the way compliance is conducted in banks and financial institutions. From suspicious alerts and fraud detection engines to networks and linkage analysis of customers and transactions, machine learning has been a game-changer within the financial world. Machine learning can also be leveraged to use the same customer, associated parties, account and transaction data to monitor the former’s financial and non-financial activities continuously, incorporating the analysis into the risk engine. An ecosystem can aid in machine learning-powered dynamic risk profiling of customers for a future-proof risk based AML infrastructure as follows:

  • Creating a dynamic risk-scoring engine for customer risk profiling. Such an engine should calculate an overall risk score, preferably daily, as a weighted average of both dynamic and static risk attributes. Dynamic risk attributes can include alerts generated on the customer (weights can be associated with scenarios triggering the alerts), alert likelihood of being suspicious, alert closed as false or reported to FIU, actual transaction behavior going beyond expected behavior, and so on.
  • Leveraging machine learning algorithms for incorporating dynamic customer behavior data into the risk calculation engine. The machine learning models can analyze the transaction behavior and match them against a customer and their respective peer profiles to arrive at deviations, generate likelihood scoring of alerts being suspicious with similar results. These details can then form input for the dynamic risk engine to populate the daily customer risk scores and profiles.
  • Designing an automated workflow for monitoring change of customer risk ratings.As the engine generates new risk scores for customers every day, any adverse change to risk ratings of customers should be subjected to manual approval and oversight. An automated workflow can help in triggering escalations for review and approval, when a customer risk profile moves from either low or medium to high.
  • Triggering event driven review for dynamic downgrade of customer risk rating. EDD needs to be triggered for all customers who move into the high-risk category through the dynamic risk profiling. This will ensure an updated review of high-risk customers at all times. A complete audit trail should also be maintained.

Adopting of Dynamic Customer Risk Profiling

Over the past two decades, financial crimes across the globe have steadily risen. They have become evermore sophisticated too, making them difficult to detect.  

Even as regulations are becoming more stringent globally around AML and financial crimes compliance, both banks and criminals are trying to strengthen their systems and processes in an attempt to outsmart each other. Managing financial crimes risk continues to remain among top priorities of financial institutions, with millions of dollars being set aside every year for upgrading systems and processes around prevention, detection, investigation and reporting of such crimes. Dynamic customer risk profiling promises to help realize benefits across this functional value chain. In order for this to happen, financial crime risk managers may look at integrating dynamic customer risk profiling in AML for the following functions:

  • Dynamic review and KYC of customers, as opposed to periodic review, whenever there is a change in customer risk rating, as generated by machine learning-based dynamic risk engine
  • Alert generation and auto escalation when a customer risk category moves from low or medium to high during dynamic risk scoring
  • Updating of customer profile based on change in risk rating. This would mean changes to customer limits and thresholds for various financial and non-financial activities. This process also needs to be automated and manual review/approval need to be incorporated.
  • Refreshing customer data residing in other systems for the changed profiles in real time (e.g., data used for transaction monitoring scenario runs).

Risk profiling customers on a dynamic basis is becoming increasingly important, even as new data sources are being explored for gaining insights into customer behavior that can be used to assess their risk. Social media behavior is being seen as a storehouse of customer activity data, and social network analysis is already becoming a risk management buzzword. It is just a matter of time social media data gets integrated into customer risk engines, and dynamic risk profiling becomes part of the routine AML landscape.

 

3 ways AI can take on money launderers more effectively

It is estimated that around 2 to 5 percent of global GDP, or $800 billion to $2 trillion a year fall victim to money laundering. In the UK, money laundering is out of control with an estimated £100 billion flowing through the country each year. Yet recent reports show there has not been a single prosecution for money laundering since new regulations were introduced in 2017. 

1. Reduce false positives

Legacy systems have been great at providing compliance officers with mountains of paperwork. It is their job to sift through all the suspicious transactions coughed up by computers. ‍The problem is that most of the transactions are perfectly innocent but every single red flag, and there can be hundreds of thousands produced in a matter of hours, must be assessed individually by a human. While this is not an impossible task, it is extremely time consuming. Worse still, a real threat could pass through the net due to the sheer workload that people have to deal with on a daily basis. ‍

Fortunately, the vast majority of red flags produced by legacy systems should never have been raised in the first place, they are often merely a product of a system that is falsely matching data to incorrect criteria. ‍With today’s technology, we now know that there are far more efficient ways to detect suspicious behaviours. Modern technology such as AI is vastly more sophisticated and, if employed correctly, can reduce volumes of false positives considerably.

‍If modern technology can drastically reduce the volumes of false positives, and pop up more accurate threat identification, not only will a larger proportion of real alerts be reported, but it also won’t take as long to review them.

2. Put compliance in the driving seat

With AI becoming more and more advanced, everyone and their dog has claimed to implement it within their own business in some way. It has become a fashionable sort of thing. This type of ‍AI isn’t so much a set of rules or the ability to create rules. It goes much further than that. AI-based platforms can sift through far more data and adapt without the need for further human instruction, which only slows things down.

Methods of laundering money change constantly. Launderers stay ahead of the authorities in every way. They are extremely cunning in their methods, tweaking their ways to stay just one step ahead of the authorities. Compliance officers are often in the dark about exactly what it is they are looking for, and with the vast and increasing numbers of transactions passing through bank accounts on a daily basis, they are sitting on a ticking time bomb in the race to catch one of these suspected fraudsters.

However, with AI, searching for the ‘unknown unknowns’ becomes ever more achievable. Machine learning algorithms can change the way compliance officers find patterns of suspicious activity. AI programmes are so advanced, they can achieve all this in an instant, testing and adapting thousands of times quicker than any human ever could. ‍Ultimately this puts the compliance officer in the driving seat with a chance to pre-empt a money launderer’s next move. It’s a game changer!

3. Improve Intelligence

Apart from false positives and/or false negatives, there are many other challenges faced by those on the hunt for money laundering activities. ‍What happens after that is also somewhat worrying. Intelligence gathered from accurate analysis of transaction monitoring is right at the front-end in the fight against financial crime. ‍However, law enforcement agencies are showered with more reports of suspicious activity (SARs), many of these low quality reports, than they could ever deal with.

The 2018 National Crime Agency report on SARs shows that, between April 2017 and March 2018, the NCA received 463,938 SARs alone. ‍The explosion in SARs could be driven by a desire by those who must comply with AML regulations to show they are trying to detect wrongdoing. Add to this the fact that ineffective transaction monitoring systems tend to generate too many alerts (as we’ve already described), and the picture of over-reporting becomes clearer as multitudes of (false) alerts are investigated and consequently reported.

‍Although SARs are key to helping law enforcement fight financial crime, over-reporting hampers efforts all around. The current defensive reporting style is a significant burden on workloads and budgets for organisations and law enforcement alike. ‍AI will allow companies to strip back the redundant SARs with confidence, allowing analysts to direct their attention to the transactions more worthy of investigation.
‍It follows that fewer alerts will allow time for more thorough investigation. Consequently, more accurate intelligence will be passed on which will put law enforcement agencies in a stronger position to take on the battle against financial crime.

Get in touch with us today to book your free, no-obligation demo of ComplyRadar – it pays to comply!

Covid-19 and AML: The Impact, Challenges, and Future

The effects of Covid-19 have been felt in every industry, in every country. As the world continues the fight to quell the virus, everything about the way we live and work has changed. However, among all the chaos, heartache and fear, there are people who see the opportunities that Covid-19 has brought. Namely money launderers, terrorists, and fraudsters. For AML teams, there is no room for complacency now. As criminals exploit the new circumstances, novel ways of laundering money are rapidly springing up.

What impact has Covid-19 already had on financial crime and money laundering?

As with all of us, criminals have had to change the way they behave too. They have had to find new ways to launder and move goods as borders are shut, ferries are cancelled, and flights are grounded. The military has been deployed in some counties to ensure that lockdown rules are adhered to, which has made it increasingly difficult for fraudsters to use traditional scamming methods. 

Recently,  Europe’s top banking regulator instructed financial institutions to pay closer attention to transactions linked to international trade, as criminals look for new ways to move illicit funds and goods across borders. This also brings up the interesting situation of what traditional cash launderers will do now. With all non-essential businesses shut or slowly re-opening, we can only presume that money laundering fronts are now sitting on piles of cash without a way to get it into the system. Once this is all over, banks may see an increase in the volumes of cash deposits.

Secondly, another impact we are seeing is increased levels of coronavirus-related crime, which will ultimately affect levels of money laundering as criminals try to wash their funds. Organizations need to be on high alert to new and emerging threats, or what is known as the ‘unknown unknowns’. Many retailers are refusing to handle cash, opting for card payments, which means that bank transactions are on the increase. The larger volumes will make it easier for scammers to disguise their money laundering activities. 

Even before the Covid-19 crisis, many money laundering patterns were not being successfully detected.

The situation we are in now exacerbates the risk of anomalous transactions being missed. FinCEN reported several emerging trends connected to Covid-19. These include imposter scams, investment scams, product scams and insider trading, similar to those that occur following a natural disaster.

The FCA has warned we are likely to see an increasing number of scams in the coming months. In the UK, coronavirus-related fraud reports increased by 400% in March, and we expect this to continue to rise. Where, when, and how the money generated from these scams will be used is anyone’s guess, so it is essential to be vigilant. Covid-19 is also widening the money mule pool. This makes the need for ongoing and real-time client activity reviews more important than ever.

‍What AML challenges has Covid-19 created?

Without the right transaction monitoring technology and algorithms in place, efficiently detecting instances of money laundering and terrorist financing can be very difficult. But what is happening right now, is that everyone’s transactions are anomalous. The shopping landscape has changed with increasing numbers opting to purchase their items online, avoiding cash transactions altogether. What this means is many of the existing rules in place for monitoring transactions are not applicable.  

Those financial institutions with legacy screening systems, which lack the flexibility and agility to quickly respond to a crisis such as this, are likely to be overwhelmed with false positive alerts right now. This will create larger workloads for compliance teams, until these rules are tweaked sufficiently to reflect customers’ changing behaviors.

There are also the further complications that lockdowns have caused, such as working from home. A key issue will be maintaining effective internal controls across operations if offsite team arrangements are implemented. Furthermore, as financial institutions see larger fluctuations in profits, there is a risk too that they could introduce cuts or reorganize compliance teams. As you can imagine, the scenario that could potentially unfold is one where banks have fewer resources to review increasing volumes of alerts.

What should compliance teams be looking at to deal with these challenges?

The natural inclination might be ease off the risk parameters on monitoring to reduce these alerts, but this is problematic. It will expose more risk by creating more false negatives. Rather than increasing risk appetite, rules should be adjusted to reflect the changes in customer’s behaviors. With the help of machine learning, for instance, it is possible to detect the ‘unknown unknowns’, which are vitally important right now. For those with modern technology, making these changes will be the case of users making quick and simple rule adjustments. There is no need to wait for and rely on specialist data scientists to intervene.

Will monitoring and deadlines for regulatory compliance fall by the wayside, in light of the challenges of Covid-19?

The FCA has postponed activity that is not critical to protecting consumers and market integrity in the short-term, but it has emphasized that firms should not unnecessarily delay submissions of regulatory data. Firms are also expected to continue to take all steps to prevent market abuse risks, including enhanced monitoring. It would be wrong to assume a specific deadline will take a back seat, and even if it did, this would only be temporary. As soon as things return to ‘normal’, expectations would be as high as ever.

How can banks improve their AML systems and approaches?

Banks need to invest in the latest technology. This is the only way they can improve their current situation and increase the detection rates of genuinely suspicious transactions, outside of hiring many more people. There is no doubt that human skill is hugely important. The best situation is to implement new technology so that staff can be relieved of menial tasks to focus on the important jobs of investigating suspicious transactions.

Is now a good time to invest in new AML technology?

There has never been a more important time to invest in new AML technology. Covid-19 has made many firms re-evaluate their operations and processes. It is absolutely vital that they adapt to the new way of working as quickly as possible. 

Contact us to learn how ComplyRadar helps you address AML transaction monitoring requirements by automatically identifying suspicious behavior in real-time or on a scheduled basis, while minimizing false positives. It monitors transactions related to individuals, accounts, and entities to detect suspicious activity quickly and effectively, through a fully audited process to inspect and act on flagged transactions.

Harnessing analytics in the fight against fraud and money laundering

The fight against fraud and money laundering no longer sees businesses and compliance personnel going up against easily identifiable, shady criminals. Auditors can no longer identify patterns of suspicious behaviour months after the transaction was completed. It is no longer feasible to work in a reactive way. 

Transactions are done so fast nowadays that a manual method of monitoring is unacceptable today. Businesses need an automated monitoring system which detects and catches fraudulent intentions right away, and, if possible, before they even happen. In addition, in today’s fast-moving technological world, customers demand more than ever that their data is safe and protected. They do not want nor should they expect their data to be vulnerable to outside theft attempts. However, they still require financial institutions to enable fast online payments. There is a lot on the line but new tools for AML and fraud analytics are making it possible for companies to stay ahead of criminal intentions. 

Letting machines do the work

Machines are undoubtedly better equipped than people to handle large data sets since their computing capabilities allow them to process a huge number of transactions simultaneously. They are capable of recognizing thousands of fraudulent patterns, unlike the few that can be captured by rule setting. The drawback for these machines is that criminals are discovering loopholes and it is likely that whatever rules you set they will find a way to get past them. But what if you employed a smart system which had a mind of its own?  

New innovations in AML and fraud prevention have combined rule-based systems with smart machines and AI-based detection systems. A hybrid system such as this is able to detect and identify thousands of fraudulent patterns and learn while scanning the data. An automated, analytics-based transaction monitoring system can reveal new suspicious patterns and recognize organised crime more quickly, consistently, and efficiently. Therefore, such systems are a must have for businesses across a wide range of industries.

So how can you harness the power and insights of analytics in your fight against money laundering and fraud?

Identify your needs

The first step in adopting a transaction monitoring new system is the problem identification. To get optimal information, it is feasible to consult your employees as they are the compliance analytics experts at your organization and are therefore capable of pointing out what analytics you need, which data and techniques to use, and what results to report on. 

The experts in the organization can also identify the optimal combination of rule-based and AI approaches to adopt for the best possible chance to detect fraudulent behavior at the earliest point possible. There are also other decisions that need to be taken. For example, businesses need to optimize their existing threshold tuning, they need to explore big data, develop and interpret machine learning models, comb through text data to find relevant information, and prioritize alerts. Through the automation of these areas, an organization can both reduce the need for manpower – reducing costs – and improve its detection and prevention of money laundering and fraud. 

The benefits of the analytical approach

The analytical approach towards money laundering and fraud prevention has received numerous testimonials. Among other things, the quality of positives for further investigation has been found to be better. In addition, investigators are given a much clearer idea of why a possible positive has been flagged which results in a more efficient investigation. Efficiency in turn leads to the reduction in both false positives and false negatives. This improves customer experience.

The analytical approach makes it possible to discover complicated or organized fraudulent activity which rule-based systems would miss out on. Using analytics, organizations can group customers and accounts with similar behaviour together and subsequently set the appropriate risk-based thresholds.

Contact us to learn how ComplyRadar utilises a graph database, providing you with the power of true analytics to inspect and act on suspicious transactions in real-time.

COVID-19 and Transaction Monitoring: Managing AML Compliance

As a result of the ongoing global struggle with the COVID-19 pandemic, financial institutions are experiencing novel compliance challenges linked to this outbreak. The new strain of coronavirus has sent global financial markets into widespread turmoil and this unfortunately presents criminals with new opportunities to generate and launder funds gained from illicit activities. As a result, financial authorities around the globe have taken to adjusting their AML/CFT approach to cater for the new patterns of criminal behaviour being recorded.

Organizations are expected to be knowledgeable of the new ways that money launderers are using to exploit the pandemic and are also expected to ascertain how their AML/CFT compliance processes need to evolve and change in light of the elevated risks.

Changes in behaviour

The current pandemic has seen numerous countries set up stringent control measures such as restricting movement and therefore keeping people in their homes. The impact such measures have had on the global economy has brought about drastic changes in banking behaviours and in criminal behaviours too. Panicking at the current situation, people are withdrawing hard currency and are making more use of mobile apps thinking that it is safer. An increase in the use of virtual currencies has also been noticed.

Unlike in past times, digital banking services are now being used more frequently and by more people who would not normally use them, like the elderly. This increase in digital services use provides a challenge for AML/CFT, making it more difficult for compliance teams to identify between legal and illicit activities. Efforts to combat money laundering are complicated further by the creative changes criminals and fraudsters have taken a liking to. Illegal activity during this coronavirus crisis has taken the form of fake cures, fake charity, and government impersonations amongst others.

Media monitoring

The increased risk of money laundering activities brought about by the pandemic has unsurprisingly resulted in an increased presence of AML/CFT stories in the media. In times of crisis like today, financial institutions and banks would do well to adjust their media screening to capture news story types that could indicate and point out potential money laundering crimes. Story types worthy to keep an eye out for include:

Regulation – media stories focusing on regulatory punishments may shed some light on the actions taken by authorities against other firms accused of activities like price-fixing and collusion.

Financial difficulty – media stories focusing on debt, bankruptcy, closures, redundancies, and departures of top-level staff.

Violence or human rights abuse – media stories focusing on violence or sex offences in connection with state-level offences such as human rights abuse.

Criminality – media stories about accounting malpractice, financial crimes, dishonest billing, and predatory lending.

The money mule crime

The coronavirus pandemic has brought an increase in the use of money mules to transit illicit funds. Money mules are people who are legitimately employed by an organization under the pretense of a legitimate job but whose presence is only required to receive illegal cash into personal accounts. The mule would then withdraw that cash and deposit it into the accounts of the money launderer, all while pocketing a sizable commission as payment.

Unfortunately, the COVID-19 situation has driven people who are desperate for money to act as money mules under the pretense of depositing funds to their personal bank account to pay for coronavirus related health problems. The increase in unemployment has regrettably opened the doors for money launderers to approach jobless people to act as mules in the transformation of dirty money. Therefore, AML/CFT teams need to keep a sharp eye for any warning signs that a mule might be at work whenever large sums of money are deposited.

Adjusting compliance

Since the ongoing pandemic poses great uncertainty as to when it will finally be eradicated, banks and financial institutions alike must adjust to the new reality of compliance monitoring. This to accommodate the changing legitimate behaviour of consumers while identifying illegal activity from criminals. The upgrade of monitoring efforts not only includes the sufficient screening of transactions but should also incorporate changes to rule-sets to ensure that the software used is able to capture new typologies, such as the aforementioned money mule tactic.

Therefore, given this increased reliance on a strong AML/CFT platform, organizations should seek out technological solutions to help them in their money laundering compliance efforts. ComplyRadar allows firms to collect, analyze, identify, and report suspicious activities to the competent authorities. In conjunction with skilled compliance personal, ComplyRadar is able to ease the burden of compliance by delivering ongoing monitoring in periods of great change during regulatory upheaval.

Get in touch with us today to book your free, no-obligation demo of ComplyRadar – it pays to comply!

5 key challenges when tuning AML transaction monitoring software

Having a correctly tuned AML solution is essential for any organization dealing with high volumes of financial transactions. With compliance risks being higher nowadays, regulators are constantly inspecting AML transaction monitoring systems to ensure adherence to international regulations.

One of the main issues faced is the high number of false positives which can be caused by an incorrectly tuned transaction monitoring system. Then there is the other side of the coin – if an organization performs extensive tuning on its AML software, it runs the risk of reducing the effectiveness of the system. If caught by authorities this lack of effective monitoring could also result in fines.

Model and data alignment

The first step for any tuning exercise should be to understand the objectives of the model. Is it risk minimisation or detection of suspicious behaviour? The second step would be to identify the data feed that will be used for the model – meaning the type of data the model will be using to monitor transactions. The data type needs to satisfy the requirements of the model. For example, it is no use having a model which is able to monitor online bank deposits if the data that will be provided to the model will not include such information.

Therefore, organizations and institutions need to establish their main objectives when it comes to compliance so that their mitigation expectations are met via the model and data type they will be using. With these criteria established, it will be easier to tune the software according to their specific needs.

5 common tuning errors

  • Data and model conflict: when elements in the data field are misused and misread by the model, errors are made resulting in false positives. For example, if a ‘country’ field is left empty and filled in as ‘NA’ which stands for not applicable, the model may misinterpret it as being Namibia. Therefore, for the model, all transactions with an empty ‘country’ field will have originated from Namibia.

  • Structuring patterns: criminals use the structuring process to deposit illegal funds into accounts by breaking down the large amount of funds into a number of smaller transactions. These transactions are created in a way which fall below the reporting threshold in an attempt to avoid detection. However, honest businessmen also make use of this tactic on a frequent basis and therefore such transactions can be wrongly flagged. Therefore, the software needs to be tuned to catch suspicious activity via customer profiles and the pattern of transactions.

  • Velocity patterns: velocity patterns are noticed when a customer receives a deposit in their account and then rapidly withdraws the funds a few minutes later. This can be a sign of suspicious activity, although it can also represent a genuine client withdrawing money to pay their bills. Tuning the software to recognise certain patterns is essential for removing false positives.

  • Exclusion lists: these exclusions or ‘whitelists’ are created by organizations to exempt certain customers from being monitored by the AML software. This is usually done to minimise false positives given the customers’ reliable track record of legitimate transactions and honourable business practices. If, however, these lists are not reviewed periodically and patterns studied for abnormal behaviour, this limited amount of tuning could lead the organization into hot water with the relevant authorities. Reason being that without performing risk assessments on whitelisted clients, illegitimate transactions by usually reliable people can go under the radar, resulting in no flags being raised.

  • Suppressions: organizations sometimes tune their software to suppress alerts based on particular criteria in order to minimise false positives. They have to ensure however, that the system is properly tuned so that illegitimate transaction alerts are not suppressed in the future. If such correct tuning is not in place, an analyst may end up classifying an alert as a false positive given that they are unaware of the subsequent alerts which the software had suppressed.

The Solution

ComplyRadar utilises a full risk-based approach to eliminate disruption to genuine customers, detect potential criminal behaviour, and demonstrate full ongoing compliance. It sends you notifications on the transactions that matter and enables you to automatically apply a full-pattern analysis to instantly see suspicious transactions in real time. Get your free, no-obligation demo of ComplyRadar today. Visit https://www.comply-radar.com/ for more information.

Top 5 measures for AML transaction monitoring systems

Active or real time monitoring of financial transactions should be the order of the day for institutions and businesses handling high volumes of deposits and withdrawals. This capability is essential in mitigating risks, thereby improving the monitoring and detection process. By effectively and actively monitoring transactions, organizations are in a better position to meet their AML obligations without impacting their day-to-day operational processes.

Impediments to real time transaction monitoring

  • Data quality and mapping: consolidation of transactions on different platforms is perhaps the most challenging aspect of AML. This is because the data needs to be coherent for monitoring purposes. Data quality needs to be taken into account, especially since sometimes, similar batches of data are grouped together, making it difficult to monitor holistically.

  • Lack of feedback on SARs: an organization expects that whenever a suspicious activity report (SAR) is filed, that it receives feedback from the competent authorities about the investigation and the outcome. However, sometimes, this feedback is not up to standard or even non-existent to allow the self-improvement of the organization’s monitoring capabilities. This leads to improvement efforts being deemed subjective as people within the organization itself have to come to their own conclusion on what is working and what is not.

  • Scenario alerting techniques: if an organization keeps moving the goalposts as to what is considered risky and what is not, quality of monitoring will be diminished. Non-transparent analytical methods such as neural networks add another complex layer in relation to the unpicking and explanations for decisions.

5 measures for accurate transaction monitoring

  • SAR disclosure rates: by categorising the files of cases being submitted, understanding the reason behind the majority of submitted cases, and acknowledging the areas of business affected – an organization can extract valuable quantitative information which can be investigated.

  • False positive ratio: comparing the overall changes in this measure provides an indication of the monitoring system’s performance. When at high-level, the ratio provides the best indications of any mismatch between detection processes and overall risk.

  • Alert volumes: any changes in alerting volumes should be investigated. Some potential issues could be underlying within the transaction monitoring system itself. Some examples include scenario changes, rule changes, setting up of new scenarios, introduction of new business lines, and the introduction of new types of transactions.

  • Operational costs: costs also need to be monitored in two aspects. The first aspect is the size and capability of the investigative team which could also be handling day-to-day system tasks. Secondly, the supporting infrastructure around the team needs to be constantly assessed as well. In this case, operations growth needs to be taken into account.

  • Number of monitoring rules: as the number of scenarios or rules increases, so does the strain on system management processes which in turn could lead to a higher number of cases. An increased number of rules could be due to changes to existing compliance regulations, lack of consolidation in account segmentation, and new products or business lines being added.

The Solution

ComplyRadar helps you address AML transaction monitoring requirements by automatically identifying suspicious behaviour in real time or on a scheduled basis, while minimising false positives. Get in touch with us today to book your free, no-obligation demo. Visit https://www.comply-radar.com/ for more information.

Leveraging Graph Analytics to fight Money Laundering

It’s estimated that money laundering (ML) transactions account for anywhere between 2% to 5% of global GDP. This amounts to roughly $2 trillion per annum.

Today’s struggle against ML brings with it hefty fines to institutions found to have breached anti-money laundering (AML) regulations. Two giant cases were recently settled involving two banking conglomerates. Back in 2018, US Bancorp and the Commonwealth Bank of Australia were fined a total of over $1 billion between them for careless AML control and non-compliance offences respectively.

This ruling showed that no institution, no matter how big, will be afforded any leniency and that nobody is above the law when it comes to AML. Therefore, organisations need an effective, yet simple to implement AML system in place. But what are the challenges that impede these systems from coming into play?

Challenges to AML compliance

  • False positives – on average, 95% of transactions flagged as potential ML turn out to be false positives. That is a huge number of erroneous flags, resulting in more serious and potentially real ML attempts being missed.

  • Criminal technology – nowadays, criminals are getting more and more sophisticated in their attempts to launder dirty money. Technology has its downsides in that it is making things easier for lawbreakers to hide behind false names and complicated web identities.

  • Manual time-consuming investigations – turning a flagged item into a Suspicious Activity Report (SAR) takes between 20 minutes to a few hours to complete. This is because it takes time to dig into the multitude of records and networks which make each transaction possible. Given the huge manual time, this process is expensive and inefficient.

Luckily there are solutions which help in easing these burdens.

Graph Solutions for AML Compliance

Relational databases have until recently proved to be well equipped to support transaction monitoring and performing basic analytics. However, their one major limitation is not being able to identify relationships between datasets, which is an essential element when following money trails and assessing risks.

Fortunately, graph databases can perform all the above tasks – they can analyse relationships to reveal hidden networks and clusters which may be symptomatic of illegal activity. The way these databases work is rather unique in that they store data in nodes (customers and entities along with their attributes), thus creating connections.

For AML purposes, the money flow trails and parties forming part of ML transactions create a web of relationships. These relationships are created via the three phases of ML which are placements, layering, and integration.

Graph databases are a big help to the fight against ML. They bring fragmented pieces of AML data and combine them to create the story of a particular transaction or money movement including the means and systems used to process the money.

The future of AML compliance

A key aspect of combating money laundering activities is the clear representation of transactions in a manner which enables the understanding of the movement of the money in question through a number of accounts. These could potentially include several intermediaries. Graph analytics can also capture the means used to interact with the relevant accounts which can be utilised to further study the money transfer behaviour.

Graph analytics offers a unique solution to banking and financial services when it comes to AML efforts. There is a good argument to be made that this new technology will become the industry leader in the not too distant future and that it will be the expected and preferred technology by regulators.

An increasing number of financial service providers are investing in graph analytics for AML monitoring and compliance. They are recognising the fact that modern technology has turned the cumbersome AML compliance process into an advantage for their organisation. So, stay ahead of the pack and adopt this new technology now rather than tomorrow. Contact us to learn how ComplyRadar utilises a graph database, providing you with the power of true analytics to inspect and act on suspicious transactions in real-time.

What happens when your AML transaction monitoring rules are static

Although we live in a cutting-edge, modern tech world, a large number of payment service providers still use outdated, static systems to flag and identify suspicious transactions and payments. Remedying false positives can be a colossal waste of your team’s time. The situation can also lead to customers’ legitimate transactions being delayed while the team is handling and reviewing possible false positives. Given these facts, it is no surprise that a decade-old flagging system is no longer feasible for a number of reasons.

Too many flagged transactions

Most systems still use the static rules principle, which means that the system does not adapt to new rules but follows a stringent pre-defined list of criteria.​ This​ frustrating factor leads to transactions being flagged as suspicious because of their unique and modern ‘signature’. Obviously, if the signature is not found in the old processor’s list, it will mark it as a false positive, putting the expensive burden of review on the compliance team. Apart from being costly, a long review process increases operational costs, frustrates genuine customers and puts the company’s Anti Money Laundering (AML) compliance at risk.

Innovative criminals take advantage

Criminal exploits are made easier when old systems using extremely rigid rules are still being used. Static rule systems are slow to adapt, become quickly outdated, and have become increasingly predictable. These factors make it quite easy for criminals to hide their true intentions and mask transactions under the pretense of legitimacy. Likewise, with new rules and regulations put in place at an increasingly quicker rate, it is easy for an organization to fall out of compliance if it does not adhere to these latest laws. All this means increased costs through fines and updating of legacy systems.

Solutions to the problem

Fortunately, solutions do exist for these modern-day issues. For instance, AI-driven transaction monitoring enables organizations to stay ahead of the curve and always be compliant with new laws, while simultaneously improving customer experience. These systems have the capability to learn ‘on the job’ through continuous monitoring, inspections, and analytics – they actually get smarter. This means that now, criminal transactions can be identified more frequently and accurately as the system will have recognized the signatures used before and intercepted the transaction as suspicious. 

Internally, these new systems allow for a decrease in false positives and enable the compliance team to access real-time data involving legitimate behavior that is always changing. This further means that the systems are more cost-effective since manual reviews of false positives are eliminated. They also allow the compliance staff to focus more on the right investigations, thus creating an optimal AML workforce, unburdened by the constant manual reviews that hindered them in the past.

How ComplyRadar can help

ComplyRadar helps you address AML transaction monitoring requirements by automatically identifying suspicious behaviour in real-time or on a scheduled basis while minimising false positives. It monitors transactions related to individuals, accounts, and entities to detect suspicious activity quickly and effectively, through a fully audited process to inspect and act on flagged transactions. Contact us to learn how ComplyRadar enables you to fulfil your AML obligations whilst nurturing your genuine customers. It’s all about positive customer experience!