Fighting financial crimes such as money laundering and terrorist financing is a never-ending battle due to the persistent evolution of activity and channels criminals use to commit crimes. In response, the United Nations Office on Drugs and Crime (UNODC) has developed a standard software solution called goAML.
Alessa now supports goAML reporting, helping users meet goAML filing requirements for multiple jurisdictions worldwide. It automatically identifies and populates regulatory reports from its data analytics engine, validates content and manages report submissions using its flexible workflow and case management capabilities.
But why is this important? This article provides an overview of the goAML system and its impact on FIUs and the global financial community.
Introduction to goAML
The goAML solution is a system that acts as a standard AML platform for Financial Intelligence Units (FIUs). It encourages all countries to form their own FIUs to serve as a national center to receive and analyze suspicious transaction reports (STRs) and financial data in order to detect, identify and block money laundering channels and terrorist financing.
The following capabilities make up the goAML platform:
- Data collection and clean up
- Ad hoc inquiries with data matching module
- Structured analysis (strategic and tactical)
- Profiling and rules-based analysis
- Workflow system
- Task assignment and tracking
- Document management
- Intelligence file management
- Integration and/or data acquisition
- Charting and diagraming
- Intelligence report writer
Along with these features, goAML is accompanied by a web application that provides secure web-based communication between the FIU and its reporting institutions, and allows reports to be completed, submitted and attached via secure email.
Currently, goAML is being used or evaluated by an estimated 50 nations, including Mexico, New Zealand, Ireland, Luxembourg, Nepal, and other countries across the Caribbean, Europe, Africa and Asia.
With this increasing adoption of goAML, having an advanced technology solution to automate goAML reporting is needed to help compliance teams satisfy financial regulators while reducing workloads and costs.
FIU Decision to Use goAML
There are a number of factors determining each FIU’s decision to select this solution to collect anti-money laundering (AML) information from regulatory bodies.
To support the collection of suspicious activity reports (SARs), suspicious transaction reports (STRs), currency transaction reports (CTRs) and other types of regulatory reports, goAML offers a range of features that are intended to effectively support the submission, verification and analysis of reports submitted through the solution.
Fourteen features are currently included. Here is a summary of those key features, which are swaying many FIUs to implement the platform.
1. Data Collection
This component of the goAML solution allows reporting entities to submit regulatory reports and other information offline (i.e., through XML data exchange such as email attachment, CD, HD, etc.), online (through direct upload of XML data messages), or by filing and submitting online forms through the FIU’s secure website. Of particular note is that this feature allows for the reporting of multi-party transactions, which is useful for organizations that deal in high-value goods and often report single-sided transactions, despite there being several parties to the transaction.
2. Data Evaluation and Clean Up
This feature of goAML occurs in a ‘staging area’ outside the main system environment and automatically reviews the data it receives to verify it is both accurate and complete. If the system determines that the submitted data is complete and accurate is it pushed through to the main database. If not, it is sent back for correction to the entity that reported it.
3. Ad Hoc Queries and Matching
With the goAML solution, users can make ad hoc queries, allowing them to find information on a range of criteria (such as name, address, country, account number, full-text search, etc.). Upon submitting a query, goAML presents a list of entities that match the search and—where they exist—links them.
In terms of matching, goAML has an importer that inputs external data in any format on people, entities and accounts and matches that information against the FIU’s database. This process can be either scheduled or be scheduled as a regular task.
4. Statistical Reporting on Information/Reports Received and Processed
Statistical reports can be auto-generated and ad hoc statistics obtained from the system by goAML. This capability helps complete reports on any of the activity that happens in the solution.
Compliance-related reports can be prepared that include the types, numbers and values of reports collected by an FIU, and can also identify the organization that completed the report by name, address and type. FIUs are also able to report on and analyze their own activities as needed.
This functionality helps FIUs identify financial institutions that have missed their deadline for submitting reports or that have failed to report their side of a transaction that has been reported by another institution.
5. Structured Analysis at Both Tactical and Strategic Levels
With this feature, in-depth analysis can be performed on information contained in reports and any data stored in the database.
Trends and patterns of an individual’s financial transactions, account activity and movement of money can be analyzed.
Using information in the database, tactical and more general strategic analysis can be performed on specific targets.
Options for strategic analysis include analysis pivoting on occupation and business type, account activity, signatories, primary account holder, top players, timelines, etc.
6. Profiling Tool
The goAML platform stores profiling data, with profiling scheduled to run only before any new data is distributed, ensuring that the new data is compared with the saved profile data before it influences it so that exception reporting is more accurate.
To help identify profile trends, profile data is appended to structured tables along with a date time stamp. Profiling in goAML works based on occupation and business type, persons, entities, geographical area, etc.
7. Rules-Based Analysis
This feature offers fully customizable rules-based analysis, including rules that have dynamic risk scores. Data is continuously monitored to determine whether any reported transactions fall into certain patterns, and analysts are alerted if a pattern changes or an exception is detected.
8. Workflow Management System
The workflow management system moves work items through a series of sequenced steps to help FIUs effectively manage information. This includes receiving and processing reports, analysis function, and management of feedback and dissemination processes.
9. Task Assignment and Tracking
The goAML solution offers the ability for managers to assign, reassign or remove tasks, within either ad hoc or predetermined timeframes. Tasks can be monitored to ensure progress is being made and that appropriate actions are being completed. When a task is due or overdue, the system sends an alert to staff.
10. Document Management with Full-Text Search Capability
Document management in goAML lets documents be captured via e-transfer or scanning and optical recognition, then be filed based on a range of characteristics. Documents can be retrieved through text search functionality.
11. Intelligence File Development and Management System
After an analyst has gathered the initial information and determined that a report, further analysis or escalation is warranted, this component creates and then manages an intelligence file. Any analytical work that follows is completed within this file. After analysis is finished and the case is to be escalated, a case file is generated for sharing with end user agencies.
12. Data Acquisition/Integration from External Sources
This component is responsible for collecting and storing external data in a predefined format. Data can be accessed, queried and retrieved from external databases electronically, so long as such databases exist and there are rules in place regarding the sharing of information.
If it’s not possible to allow electronic access to data, the solution can use preformatted templates to create requests for transmission to external agencies via mail, fax or email.
13. Integrated Charting and Diagramming
With goAML, users can create visualizations, including of transactional link analysis, automated link analysis using addresses, phone number, company board membership, etc. Charts can be built manually for individual cases by using the available diagram options and icons.
14. Intelligence Report Writer
Representing the final stage in goAML’s analytical process, the intelligence report writer acts as a template and process for FIUs to complete and disseminate their final intelligence packages to their end user agencies. Supplementary information such as charts and documents can be attached to the report as needed.
Why goAML Matters
For goAML to be an effective reporting tool, the data that is submitted must be complete, thorough and accurate. With FIUs in more jurisdictions adopting goAML—and given that the UNODC has taken a clear view that regulators are not responsible for data quality issues—it will fall to financial institutions to collect the appropriate data and to verify its quality.
Now more than ever organizations that complete and submit regulatory reports will have to make significant investments into improving the quality of their data, especially customer information.
Although customer data is collected during onboarding to fulfill know your customer (KYC) requirements, there are often issues around how the information is then maintained in order to always be current.
Call on Technology to Improve Data
Technology can be leveraged in many areas to improve the quality of data recorded and maintained over time by financial institutions, including customer due diligence (CDD), sanctions screening and suspicious activity monitoring.
Data Quality and Customer Due Diligence
When completing KYC to onboard a customer, it’s necessary to identify a customer and then verify that they are, in fact, who they say they are. Performing CDD both verifies the customer’s identity and determines what is considered normal behavior and activity for that customer, helping to establish how risky that particular customer is.
Data analytics can be leveraged to ensure that all information has been verified and policies complied with during CDD and onboarding, thereby helping to improve the quality of data. The analytics can help ensure that:
- CDD is thorough, leading to more accurate data
- Customer information is logical and appropriate—for example, that all social security numbers have the correct amount of numbers
- Accounts have been periodically subject to a refresh of CDD
- Based on customer risk profiles, customer information is updated, with higher-risk customers being updated most frequently
- All accounts have been assessed for risk and marked as a high, medium or low risk for AML risk, thus determining how much enhanced due diligence (EDD) should be performed.
What Sanctions Screening Can Do for Data Quality and Risk Management
All customers should be screened against sanctions lists when they first open an account or just after to ensure that they are not a politically exposed person (PEP) or another type of high-risk client.
Preferably this screening is completed in real-time during onboarding, and then periodically thereafter as a customer can become a higher risk over time.
Information can be checked against both internal and external lists such as OFAC and OSFI, as well as against third-party lists like the Refinitiv World-Check database and Dow Jones.
Better Data Leads to Better Suspicious Activity Monitoring
High-quality data is essential when it comes to monitoring for suspicious activity. Effective transaction monitoring systems require as much accurate information as can be gathered, including data on the customer him/herself as well as the accounts, products, financial institutions and jurisdictions involved.
Linking transactions that are indicative of patterns related to various financial crimes can be missed or overlooked when KYC data from multiple branches are not funneled into a single database and scrubbed for accuracy and consistency.
Small issues—missing fields, extra punctuation, spelling errors and wrongly interpreted abbreviations, for example—can make it challenging or can even prevent an AML system from detecting transactions that should be reported.
In addition to poor internal customer data, working with non-traditional financial institutions such as money services businesses (MSBs) or with a correspondent bank, introduces further opportunities for problematic information.
Organizations such as these may bring with them missing, inconsistent or duplicate data, or may have introduced errors simply through manual data entry, all of which can reduce the quality of data and lead to overdue regulatory reports.
Technology can be relied on in all of the cases to help identify and fill in gaps in data, and to correlate multiple transactions that, once aggregated, may exceed reporting thresholds and must be reported.
Improve Data Quality, Decrease Regulatory Reporting Problems
With goAML being implemented in increasing numbers by countries such as Ireland, New Zealand and Mexico, AML compliance departments will be expected to understand the solution and ensure that the quality of their data is as high as possible to avoid fines and penalties for not meeting reporting requirements.
As mentioned above, Alessa now supports goAML reporting, helping users meet goAML filing requirements for multiple jurisdictions worldwide. It automatically identifies and populates regulatory reports from its data analytics engine, validates content and manages report submissions using its flexible workflow and case management capabilities.
Not only does our automated goAML regulatory reporting feature improve efficiency, accuracy, consistency and traceability of any filings, but Alessa also provides a number of tools for financial institutions to improve their data collection and data quality to ensure they are submitting quality reports.
To learn more about how technology can help improve the quality of your data and complete and submit goAML reports contact us.