5.3 Location Intelligence to Support AML/CTF Investigations

Role of Geospatial Analyst

The role of analysts to assess risks in AML/CTF investigations is based on the principle that a risk has two primary components of probability and impact. Determining the probability of money laundering or threat financing guides the method of descriptive, predictive, or prescriptive geospatial analysis. Investigating authorities determine the vulnerable parties, institutions, and communities at risk from suspicious illicit activity. Impacts of the risks vary in terms of cost, human safety, reputations, community order, and ethical business practices.

The geospatial analyst in AML/CTF investigations analyzes geospatial factors surrounding suspicious activity to identify risks, anomalies, and relationships through scientific process and discovery. Not all agencies or investigative teams include geospatial analysts; it depends on the agency, structure of investigative team, and an understanding of the advantage location intelligence provides an investigator over adversaries or suspicious groups.

The geospatial analyst links unusual financial transactions to data on people and businesses in geographic locations. Let us reiterate for this course that the geospatial analyst is not a legal authority, does not determine suspicion of activity, nor identify parties as conducting illicit or legal financial transactions. We are teaching methods of geospatial analysis in support of investigations to identify anomalies, patterns in geospatial and business information, and risks to proximal institutions or places.

Geospatial analysts work on location-based problems, questions, and search for patterns. Their role for Finance is to use geospatial analysis tradecraft to provide location intelligence to clear ambiguity of where transactions meet regulatory or slip under thresholds. Investigators respect rights to privacy and societal responsibilities. This is an ethical issue for analysts to "stay in their lane", know and follow guidelines of the financial organization, investigatory agency, and role.

Advantage of Location Intelligence

Location intelligence can be described as the practice of collecting, enriching, and analyzing business information, georeferenced data, and geospatial information to discover contextual insights for location-based challenges or opportunities. The process of location intelligence is conducted confidentially to gain a decision advantage for the organization or to disadvantage competitors.

Assessing the human dimension in AML/CTF is difficult and requires behavioral knowledge, training, and experience; not typically a forte of geographers (except advanced human geography). Including a geospatial analyst in an investigative team adds a highly specialized skillset in location intelligence to work collectively towards uncovering illegal activity. There are significant elements of these investigations which are not released outside of the AML/CTF community, with good reason. To reiterate, the purpose of introducing AML and CTF investigations in Lessons 5 & 6 is to share additional methods of geospatial analysis and career options for your personal development.

Uses of location intelligence in AML/CTF investigations are to:

• identify possible illicit activity and persons involved through geospatial modeling;
• connect geospatial patterns of suspicious financial activity related to place and time;
• assess growth, direction, volume, and potential for money laundering; and
• identify network connections related to location and electronic banking linkage.

Asking business questions to organize the analysis workflow.

What can the geospatial analysis and geospatial intelligence communities do to help Finance mitigate the threats they face? The geospatial analyst's role is defined by the requirements of the organization. Following an organized approach to the inquiry, the analyst starts with asking pertinet questions, gathering data, and cerating GIS models. The organization determines the specific goals of the analysis, such as to:

• Discover patterns of money laundering
• Assist FIs to "know their customers"
• Comply with national and international practices

Geospatial analytics improves the accuracy of risk assessments and reduces the time to identify suspicious activity. AML/CTF is yet one more use case of geospatial analytics to solve a complex problem. Location intelligence and geospatial intelligence adds a previously unappreciated decision advantage to anti-money laundering and counter threat finance investigations. Geospatial analytics integrates the business segments of customers, financial institutions, and transactions to detect banking anomalies. We're combining geospatial information, analytical modeling, remote sensing where it fits, artificial intelligence (AI) and machine learning in proven workflows.

Transactions

Transactions provide us the first introduction to entities and represent relationships between entiites. These are exchanges of resources, as physical transactions that occur primarily in physical space, the real world, and as logical transactions conducted in cyberspace. Geospatial analysts examine transactions to connect entities and locations. Critical metadata of spatial and temporal registration define events and transactions in space and time.

Spatial registration describes attributes of event data and temporal registration defines transaction data. All transactions have a precise beginning and end. However, one should be aware of FI rules that determine the timing and availability of funds in wire tranfers, deposits, lending arrangements, and withdrawals.

Analysis to produce location intelligence.

AML/CTF investigations leverage GIS, link analysis, and geospatial analysis to determine which FIs are most at risk to exposure of illicit finance. Intial research links unusual financial transactions to data on individuals in geographic locations.

Geospatial predictive and prescriptive models improve the accuracy of risk assessments and can reduce time to identify suspicious activity. A core skill in producing location intelligence is designing a workflow specific to the problem:

The expected result of this analysis workflow in AML/CTF investigations is to:

1. create geospatial models to identify and visualize patterns, risk areas, and corrdors, and
2. identify regions likely exposed to the flow of illicit funds, potential funding streams, and risks to nearby FIs.

Predictive & Prescriptive Analytics of Financial Fraud

Data analytics involve processes of inspecting data and to correlate the volumes of Big Data into useful information. In money laundering investigations, certain types of digital analysis may be used to isolate patterns of fraudulent activity. Analysis techniques include graph mining to detect suspicious transactions or spatial data mining to identify geographic patterns. For smaller data sets, commercial database and spreadsheet packages adequately support data analytics.

Predictive geospatial analysis estimates the likelihood of future events, of a similar event occuring in a different location under congruent circumstances. Predictive analytics applies forecasting to statistical models to understand, "What could happen?"

Prescriptive analytics combines outcome optimization and statistical simulations to develop courses of action. This form of geospatial analysis provides choices of actions or solutions for decision makers answering, "What should happen; what should we do?"

Geographic, Financial, and Security Data

When does an analyst have all the required, relevant, and georeferenced data at the start of analysis? So much effort is expended to collect and enrich data; internal data, open source, third-party, civil, and gained through observations. Raw financial data may additionally be obtained from individuals, FI databases, service providers, and MSB or Informal Value Transfer Services (IVTS).

It may be possible, with proper authorizations, to access financial data collected in commercial sector databases, e.g. retail outlets, supermarket chains, loyalty programs, telecommunication, insurance, financing, airlines, utility, car service, and deliverly companies.

Presentations to reach decisions, inform leaders, brief an investigation team

Communicating the location intelligence results of an AML/CTF investigation impact comprehension, resolving competing theories, and visualizing the geographic information. The analyst may present findings for actionable decision, to alert authorities, or referral for additional or other forms of analysis. There's no single, optimal format for presentation, this is a key competency to learn. Presentation techniques and tools depend on the organization, objectives, and decision maker preferences. Formats and geospatial information often:

1. Map, visualize, chart findings to depict patterns, relationships, situational awareness
2. Provide intelligence and feedback to stakeholders
3. Perform descriptive, real time, predictive or prescriptive analysis (what happened, is happening, may happen, or should happen?)
4. Present for action, decision making