Here is a list of potential red flags that the financial institutions must be cautious of, suggesting possible involvement of smurfing:
Financial institutions must develop and implement internal solid AML policies, procedures, and controls to detect and prevent smurfing timely. The key AML measures to prevent smurfing are:
Awareness among financial institutions’ employees is crucial to identifying smurfing-related red flags. Employees must be trained to understand the risks associated with smurfing, identify smurfing activities attempted through the financial institution, and report suspicious activities.
Employees must be trained in-house by the Compliance Officer, or some third-party expert can be hired to impart the training. The training program should include discussion around risk indicators and case studies based on actual real-life scenarios. Case studies can help employees better understand the technique and related red flags. This helps the employees correlate the training with on-job activities and, thus, helps employees understand their roles and responsibilities in preventing smurfing.
Another important aspect of employee training is ensuring employees stay updated with regulatory amendments and evolving ML typologies, including smurfing methods. Thus, ongoing training of the employees must be ensured through periodic sessions (refreshers course), internal circulars, etc.
Real-time or Ongoing Monitoring systems help financial institutions detect unusual transactions or suspicious activities. These systems should be based on robust logic and monitoring rules, suggested being fully automated, and intelligent data analytics should be used to ensure their relevance and effectiveness.
Using Artificial Intelligence (AI) can help financial institutions identify inconsistent patterns or trends in large datasets considering the past records, overall business risk, and the customer risk profile, suggesting potential risk indicators. AI can also help financial institutions detect new techniques that criminals may use for laundering illegal money.
Another important aspect of monitoring transactions to identify suspicious activities is to use reliable and independent data sources, such as watchlists and adverse media, to support the internal alerts generated during ongoing monitoring.
To effectively manage the risk, financial institutions must first identify the risk exposure, specifically the vulnerabilities to smurfing. A periodic Enterprise-Wide Risk Assessment must be conducted, and basis the risk assessed, the necessary risk mitigation measures must be deployed.
Moving one step ahead, the finical institutions must also assess the risk each customer poses to the business – customer risk profiling must be conducted using risk scoring models. Considering each customer’s risk profile, the monitoring program can be designed and applied, i.e., high-risk customers should be subject to frequent and increased monitoring.
Designing and implementing effective internal controls is very important for a financial institution to safeguard itself against smurfing. Financial institutions can help reduce risk exposure and avoid reputational damage with adequate employee training, a strong and comprehensive monitoring program, and timely risk assessment of the business and customers.
Financial institutions are critical in preventing money laundering activities, especially smurfing. Financial institutions must adopt additional checks and measures while performing customer due diligence to prevent smurfing.
Customer due diligence involves identifying the customer and verifying the customer’s identity, customer risk classification, and ongoing monitoring of the customer’s information and transactions. Financial institutions can timely identify money laundering activities by implementing effective customer due diligence processes and avoid non-compliance regulatory fines and reputational damage.
Verifying customer identity is the first and most crucial step of the CDD process. Financial institutions must ensure that their customers are genuine and not associated with criminal activities. Customer identity verification includes obtaining customer identification documents such as passports, driver’s licenses, and national identity cards. Financial institutions must also conduct screening against the Sanctions List and perform background verification to ensure the legitimacy of the person and the identity documents.
Verifying customer identity is essential for preventing money laundering activities and exposing the business to the hands of financial criminals.
Monitoring customer transactions is another vital aspect of CDD. Financial institutions must regularly monitor customer transactions to detect and report suspicious activities such as depositing or withdrawing vast sums of cash divided into multiple small-value transactions.
Financial institutions can use various tools and technologies to monitor customer transactions, such as transaction monitoring systems built upon AI or machine learning. These tools can analyze customer transactions in real-time and identify inconsistent customer activities.
Identifying customers posing the business with higher risk is important to prevent smurfing. High-risk customers include persons whose transactions are inconsistent with the customer’s business activities, persons reluctant to share identity documents, individuals or businesses with active connections with high-risk countries, or politically exposed persons (PEP).
Financial institutions must develop and implement increased checks and verification measures for high-risk customers. Enhanced Due Diligence (EDD) shall be performed, which includes obtaining information about the customer and beneficial owners’ source of funds and wealth, understanding the purpose of the transaction and business relationship, and seeking senior management approval before establishing a business relationship or conducting transactions with high-risk customers.
EDD is one of the important measures to identify and prevent smurfing activities, using adequate customer verification processes, continuous transaction monitoring, and identifying high-risk customers, increasing the financial institution’s overall risk.
Collaboration with other financial institutions and regulatory authorities is essential to prevent smurfing. This involves smooth information of information, best AML practices, conducting joint investigations, and developing industry-wide control standards.
Financial institutions must share information and best practices to identify and prevent smurfing activities. This includes sharing information about known smurfing syndicates, account numbers, and techniques and collaborating on research and development of effective solutions to identify and reduce the impact of smurfing activities.
Financial institutions can also share the best practices for identifying and reporting suspicious activity related to smurfing to the FIU.
Joint investigations can help to identify and prosecute the individuals and groups involved in smurfing activities. Financial institutions should collaborate with regulatory authorities and other financial institutions to facilitate these investigations, such as providing corroborative evidence to support investigations.
Collaboration and cooperation between financial institutions are necessary to implement industry-wide best measures and standards to identify and prevent smurfing. This includes developing standard operating procedures, AML framework, and aligning AML regulatory requirements.
Collaboration between financial institutions and regulatory authorities aids in combating smurfing activities. Financial institutions can reduce the impact of smurfing and safeguard the financial system by sharing information on already proven smurfing elements, supporting investigations, and developing the best industry-wide standards.
Smurfing is a common technique used to launder illegal money, given its simple nature of breaking large values into smaller amounts to surpass the AML threshold. Here, financial institutions can deploy technology to detect and prevent smurfing activities.
Advanced technologies like Artificial Intelligence (AI) and Machine Learning (ML) can help understand the trends and track customer behaviour to identify smurfing activities. AI and ML algorithms can analyze the massive volume of transactions and customer information to identify unusual or inconsistent activities.
Even emerging technologies – Blockchain and Distributed Ledger Technology (DLT) can also provide a secure transactional trail, reducing the risk of manipulating or structuring the transactions, thus reducing the risk of smurfing activities. By leveraging blockchain and DLT, financial institutions can create a transparent and immutable transactional record, making it difficult for criminals to disguise or conceal their activities or conduit financial crime.
The other technologies that can significantly assist financial institutions in combating smurfing are advanced analytics and data mining that can identify unusual patterns of transactions indicating the possibility of smurfing or other money laundering activities.
Financial institutions can prevent smurfing activities with the right technology and AML solution. With AI and ML, blockchain and DLT, and advanced analytics and data mining, financial institutions can up their AML compliance and safeguard their operations from the risk of smurfing.
Niyeahma is an AML consultancy service provider offering end-to-end AML support to financial institutions, Virtual Asset Service Providers (VASPs), and Designated Non-Financial Businesses and Professions (DNFBPs). Niyeahma can assist financial institutions in designing robust AML/CFT policies and procedures, implementing adequate internal controls, enhancing the Customer Due Diligence framework, and training employees to stay vigilant in detecting smurfing instances.
Financial institutions must identify, report, and timely prevent smurfing activities. Niyeahma assists financial institutions in identifying the right technology and AML tool to identify the unusual activities suggesting smurfing.