It is estimated that money laundering accounts for three to five percent of the global economic output. Financial institutions are the conduit through which money, and therefore money laundering, flows. Because of this, the financial system is the focal point of anti-money laundering programs.
Because of the international and intricate nature of this issue, a collaborative and cohesive analysis environment is necessary for effectively addressing the unique challenges that analysts face when looking for solutions to complex problems like money laundering.
Savanna’s collaborative workspace and multiple discovery and visualization tools are ideal for analyzing complex problems like money laundering. Savanna provides the necessary analysis tools to adapt and respond to evolving situations, to investigate each point of interest and to discover connections and evidence in the data to implement strategies for anti-money laundering policies.
A team of analysts is tasked with investigating suspicious wire transfers to identify notable threat actors and countries.
1: Frame the Problem
First, a senior analyst uses a Crumbnet to create a narrative mind map that outlines the steps needed to identify corruption threats and prevent potential money laundering. The senior analyst adds discovery nodes to represent questions and assumptions, adding a comment for team members to attach knowledge collected throughout the analysis to corresponding nodes. The Crumbnet is now set up to act as a central, collective workspace where the team can collaborate and make updates as the analysis progresses.
2: Visualize Data to Find Meaning
With multiple data visualization tools, the analysts are able to quickly find trends and outliers in large data sets.
Using an uploaded CSV file containing suspicious wire activity, the analysts use Linknet to create a link chart visualizing all relationships and connections. After defining the entities and relationships they wish to view, it becomes apparent that there are two notably suspicious companies with transactions to the same bank in the Caymans. Within minutes and with only a single data set, they have already narrowed their focus from twenty companies to just two. They can then share the Linknet directly with team members, so they can drill down into the two suspicious companies: a fast food restaurant and an auto salvage shop.
With Graphic, they can customize Grid (CSV) data to visualize the fast food restaurant account transfers as a bar chart. A quick glance at the chart shows that the transfers to the Cayman bank are twice as much as all the other transfers combined. They take a screenshot to be used later in a report, which is automatically saved to the Space they’re working in.
They then create a Map to geospatially visualize the auto salvage account transfers. The category lens gives them a detailed view of the amount and location of each transfer, and they use callouts to show transaction amount, location and beneficiary. Here, the analysts can easily see each wire from the auto salvage shop made to the Caymans and take a screenshot to be used later.
3: Capture Known Information
With Savanna’s dynamic Occurrence dossiers, the analysts collaboratively populate an information network about the suspicious companies and their participants. Occurrences are building blocks that capture people, organizations, things, places and events related to a problem. In this case, the analysts make Organization Occurrences for the two suspicious companies: the auto salvage shop and the fast food restaurant. They connect existing events, social media accounts, and participants to each Occurrence, quickly building a complete dossier to share with team members.
4: Discover New Information
At this point, the team wants to know more about the Central Cayman Accountants, so they use Savanna’s Search tool to quickly find relevant content, including a report detailing Central Cayman Accountants’ lack of web presence, address or proprietor. This new information leads the team to suspect that this is a shell company through which the fast food restaurant is laundering money. They save the search so that they will be alerted to any new content that is created or uploaded with indexed mentions of Central Cayman Accountants.
In Timeline, the analysts drop multiple Occurrences, such as the Organization Occurrences for suspicious companies, onto a visual span of time to draw connections between events within each Occurrence. Visualizing event times from multiple Organization Occurrences side-by-side gives the analysts the opportunity to reiterate the involvement of Central Cayman Accountants with both suspicious companies, confirming the analysts’ suspicions of money laundering activities.
Finally, the senior analyst creates a Linknet to visualize the relationships between the auto salvage shop, the fast food restaurant and Central Cayman Accountants, where it’s easy to see that Clara White has a connection to both suspicious companies, a relationship that’s documented in each Occurrence but was undetected in the original wire transfer data.
5: Place Evidence in Context
The analysts revisit the Crumbnet outlining the corruption and money laundering threat and add discoveries and evidence collected throughout their analysis. The Crumbnet’s narrative modeling elements accelerate and improve decision-making by providing the analysts with a big picture narrative of the analysis so far, with the ability to drill down to supporting evidence for more detail.
The analysts compile their findings in a report with Savanna’s Production tool. In Production, they capture relevant content from multiple sources, such as the Timeline image showing suspicious wire transfer activity. They can also easily add links to existing Savanna charts and models, such as the Linknet showing connections between Organization Occurrences that reveal suspicious activity. Once the Production is complete, they can share it with other Savanna team members or export it to PDF to send to fellow analysts and decision-makers for further action.
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