読み込み中Money laundering is the process of taking money that has been obtained illegally and making it seem like legitimate income. It’s believed that anywhere from 2% to 5% of global GDP is laundered each year, potentially equating to over a $1 trillion being moved about in this way.
Because this widespread practice has been established to make it harder to track the legitimacy of funds, anti-money laundering (AML) techniques have been introduced to stop the spread of illicit financial gains in many different arenas, including financial institutions and insurers. These investigations aim to stop the spread of illegal funds and stabilize financial systems on a global basis.
An AML investigation is a process that organizations rely on to detect, prevent, and then combat all activities associated with money laundering. It can also be used to stop terrorist financing, as well as any other form of financial criminal activity. Aiming to ultimately protect the stability and integrity of the wider financial system, AML investigations serve to directly stop any criminal networks that might look to exploit loopholes.
An investigation will be conducted in a number of phases, with a focus on identifying anything suspicious, looking for inconsistencies in financial reporting, and attempting to identify the source of any illegal activity. By pulling together statistical analysis and customer activity, an investigation aims to identify, spotlight, fight against, and ultimately prevent money laundering.
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As we’ve discussed, an AML investigation will be spread across a number of different steps. Each of these works towards the larger goal of identifying or preventing money laundering from continuing, or even occurring to begin with. The most important parts of any successful AML investigation are:
An AML investigation first begins when an alert is triggered on an account. This can occur as a result of an internal review, an automated monitoring system, or even someone noticing suspicious customer behavior on a given account.
At this stage, an internal analyst will scrutinize whether the red flag triggered by the alert needs to be pursued. If an alert is valid, the investigation process enters the next stage. If not, it will be dismissed and chalked up as a false positive.
At this stage, a case is created from the alert, or series of alerts related to the same customer. The case is then assigned to an investigator, who in turn will review the reason that the alert was generated as well as severity and exposure of the potential risk.
Details about the customer should be collected or relied upon next. This data might already exist as part of customer due diligence (CDD), or, if additional information is required, via outside systems such as LexisNexis, credit bureaus, and even Google.
Next comes a detailed breakdown of all transactions made on the account. Investigators will look for anomalies, suspicious movements of money, patterns, and unusual behavior, and may even go as far as to carry out further research on the customer’s full financial footprint and history.
If there’s cause for alarm, an investigator will track and assess the source of the money being moved and potentially laundered. At this point they will determine whether it comes from a legitimate source or not.
If it’s determined that money laundering may have taken place on an account, a Suspicious Activity Report (SAR) will be filed with the relevant regulatory authorities. If these authorities rule that money laundering has taken place, then the account will be frozen.
It’s important to store and provide a detailed portfolio of documentations, findings, and actions that were carried out during the investigation. This helps others, such as managers or outside departments, during their own investigation, while also making it easier to monitor the same accounts and customers in the future if a similar alert is triggered.
If the investigation comes to a conclusion, the accounts in question still need to be monitored in order to prevent future crimes. This means assessing all transactions made by an account or the customer in question.
An AML investigation will be triggered by any number of warnings or red flags. Often manifesting in some form of suspicious activity, these occurrences will breach any procedural habits outlined in a company’s AML Compliance Program. Triggers come in many different forms, but common examples include things like:
Frequent cash transactions of irregularly large amounts
Complex and layered transactions
A series of rapid and unexplainable transactions
Withdrawals made on an account immediately after a deposit
Refusal to provide the necessary documentation or compliance with regulatory checks
Inconsistent transactions in comparison to a customer’s profile and background
Large transactions made involving high-risk countries
Internal referrals highlighting odd behavior
If any of these triggers is spotted on an account, an investigation is the wisest and safest next procedural course of action.
Not every case in AML will be handled identically. Depending on the nature and severity of the case at hand, a number of different techniques might be employed as part of an investigation. Here are some common examples of different use cases:
Risk-driven targeting
This proactive approach to AML sees the risk factors assessed against a particular account or product in an attempt to prevent money laundering from becoming a reality. Several facets will be scrutinized, including geographic location, types of transactions, information about the individual account holder, and any channels through which a product might move.
Collaborative intelligence
Interconnectivity between different financial institutions and even across national borders will also serve to aid AML efforts. The sharing of AML techniques and intelligence, as well as data and an overall framework, serves to harmonize and concentrate all approaches to an investigation.
External law enforcement
Owing to the regulatory and legal nature of AML techniques, law enforcement will work to strict compliance programs in order to correctly enforce the management of any investigations. If an investigation determines that the involvement of law enforcement is a necessity, they’ll work with a financial organization to resolve the issue.
Regulatory guidance
This is the set of procedures that should be used as a guiding post for anyone conducting an AML investigation. Compliance programs ensure that AML is carried out by the book at every stage, guaranteeing that an accurate and legal conclusion is made on every account.
Controls testing
Also referred to as an AML audit, this approach to AML tests the effectiveness of different compliance procedures that are used as part of an investigation. This includes, but is not limited to, internal controls, customer due diligence processes, and transaction monitoring systems. Controls testing assesses whether an AML investigation, if conducted, would operate in accordance with regulatory compliance procedures.
As with any complex financial procedure, those conducting an AML investigation will be met with challenges throughout the course of the process. While these might make completing an investigation more challenging, they can be circumvented. Here are some common AML challenges, as well as how to adapt and overcome them:
As the practice of money laundering continues to adapt and evolve, so too must the methodology and technology used when collating and processing data from several different sources. This need to remain in a constant state of progression can be challenging in some instances, where adaptability might not be obvious or even manageable.
In order to best counter this potential hurdle, data ingestion needs to be finitely assessed at every stage of the process, such as data collection, data triage, data fusion, data analysis, and data communication and dissemination. Using the right software and methods can keep an organization operating at the desired level.
In-the-moment decisions and actions often need to be taken to prevent or combat money laundering. But it can be difficult to execute continuous data flows and transaction monitoring on real economy insights. This is particularly challenging if you’re relying on manual workers to monitor AML cases.
By utilizing transformative technologies that provide context to data operations with a real-time AI-driven approach to decision intelligence, some systems will be able to uniquely integrate and connect internal and external data to do the following: provide a single view of risk necessary to safeguard public systems, cut through rapidly increasing data complexities, exceed the demands of complex regulatory requirements, and holistically understand risk.
Strict regulations regarding the sharing of personal information across national and international authorities can sometimes make it difficult for AML procedures to progress quickly. The need to keep customer data private can also serve as a detractor in being able to act as fast as is required to combat money laundering.
Fortunately, there are solutions that enable AML bodies to share data on agreed procedures and frameworks in a timely manner. This is made possible by the utilization of technology-enabled data minimization, which is encrypted and secured in order to prevent sensitive information being leaked. This allows bite-size chunks of data about an account to pass through the framework, without potentially compromising a customer’s personal data.
Just as technological advancement has to be made in order to keep up with criminals, so too do AML compliance regulations need to be constantly adopted and updated. This can leave organizations floundering to keep up, or even unaware of a shift in regulatory approach. Furthermore, introducing changes can be time-consuming and require significant resource reallocation.
A compliance officer may be required to ensure that procedures are being both updated and adhered to. Having a dedicated professional in a role such as this would also ensure a streamlined management of any required adaptations, meaning an optimization of resource utilization, with minimal assets going to waste.
AML case management software is a platform that provides automated regulation of an entire AML investigation lifecycle. Making the whole AML compliance process smoother and easier to navigate, this software relies on advanced technology like AI and machine learning to streamline the entire process.
Some key areas where this kind of technology can be of use throughout an investigation are:
The primary objective of this kind of software is to manage the entire investigation from start to finish. A compliance team will be guided through every part of the process, with the automated software ensuring there’s no deviation from the optimal procedural path.
It can be hard to spot and flag unusual transitions if they’re being manually assessed. AML software is capable of identifying and alerting a financial organization to a potential money laundering scheme. This can also help to reduce the chance of false positives being flagged.
Having automated solutions pull in and add context to customers is hugely beneficial to investigators that are trying to understand the risk profile for each customer. Automating this process saves countless hours searching outside systems.
An overlooked facet of an AML case is the collation and management of all supporting documents and resources. AML software is able to store these in a centralized location, making it simple to refer back to them immediately when required. The digital nature of this storage system also reduces the need for cluttered real-world storage solutions.
Whether you’re asking the software to create the full report itself, or just pull out key data to be utilized as part of one, an artificial support system makes it possible to do this in an instant. This saves time and resources for employees.
In order to execute AML case management as efficiently as possible from start to finish, a series of best practice techniques should be utilized. These may look different depending on the exact nature of your case, but will ultimately serve to heighten the efficiency of the process and get the results you need:
Get a thorough and detailed understanding of all legislation and regulations in the jurisdiction where you operate
Use a proactive risk-based approach to AML investigations
Meet all requirements for AML compliance
Rely on AI and other automated AML software
Automate data integration and analysis
Provide consistent and ongoing training to all employees
Manage all cases in one centralized location to help visualize data
Regularly update policies and procedures
Run annual audits to test your system
Create and maintain detailed documentation for every individual AML investigation
In order to understand the impact that your AML investigation has had, it’s vital to assess it against relevant metrics and KPIs. While some might be obvious, there are assessment metrics which might not immediately be clear to someone conducting an investigation. Use the following KPIs and metrics where relevant to your own case:
This tracks the number of suspicious activity reports that have been passed on to law enforcement and regulatory bodies. These metrics provide a clear understanding of the number of high-risk and likely cases of money laundering that have occurred, and serve as a top-level overview of how well wider AML prevention practices are working.
Tracking the number of false positives that a system triggers as red flags is also highly valuable. This will give an organization the opportunity to fine-tune their alert system if it’s determined that a disproportionately high number of cases are being incorrectly flagged.
While far from the only factor, a poor AML approach from a financial institution can lead to an increase in customer turnover rates. Tracking motivators for leaving might help to determine whether AML procedures are the root of a high turnover rate.
The operational costs of AML compliance include factors such as team compensation, department expenses, and maintenance costs of AML case management system software and hardware. Measuring these costs (usually as part of a SAR) is another useful way to examine if your AML case is as efficient as it needs to be.
This overview gives an institution the opportunity to assess any underlying changes to business practices or market trends that might result in a shift in the total volume of alerts. It also tests the sensitivity of any transaction monitoring system when paired with the false positive KPI.
Quantexa’s Intelligence-led Investigations turns data into intelligence and empowers specialists with a context-driven, single view of new and material risk. This leverages internal and external data for more efficient operations, systems, and approaches that go beyond a single event, relationship or activity enabling a more holistic understanding of customers, employees, counterparties and their related risk.
This rich aggregation of disparate data supports strategic threat analysis, develops intuitive investigative capabilities, and facilitates intelligence-led action. This provides insights against complex typologies and accelerates more consistent, effective and economical investigation of criminal, terrorist, or fraudulent activity.
Quantexa’s Decision Intelligence (DI) platform is a data-agnostic solution, which does not have a predefined data model. Any dataset and all entity attributes within a dataset can be ingested, cleansed, and combined for matching, analysis, and monitoring. Quantexa’s platform provides a broad set of capabilities with a number of proprietary features for parsing, cleansing, and standardizing data. This world-class platform provides a single, authority-wide entity and network generation for many user profiles, allowing several investigation methods while avoiding data duplication.
With the broad capabilities of Quantexa’s platform, intelligence officers have a powerful tool that can run different analytical tasks, covering a risk-based approach and integrating third-party data and external sources for deeper investigation. Providing the right solutions and technology is crucial for an effective and efficient way to combat financial crimes. These solutions must also comply with existing legislation concerning data protection and data privacy. Investigation results also need to be documented and evidence must be stored in a case management system for cross-authority collaboration and prosecution. Through the Quantexa Decision Intelligence platform, and the context derived through connected data, a holistic understanding of customers and counterparties drives trusted, confident decisioning.
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