The combatting of terrorist financing (CTF) is a prime concern for global financial institutions’ (FI) which serve as a front-line defense mechanism for local, state, federal, and international law enforcement and intelligence agencies. Terrorist attacks have evolved from large complex attacks to smaller ones perpetrated by one to two attackers, often from the same country where the attack occurs.

Another dynamic that FIs have had to adapt for is the foreign fighter typology where citizens travel to conflict zones to fight for terrorist organizations, such as ISIS. The commonality between these two groups is that they require money to achieve their goals. Numerous reports over the past several years have documented that many terrorist attacks occurring in Europe have cost under $10,000, with some of the attacks requiring much less money to be carried out.

Foreign fighters need small amounts of cash to buy travel tickets and other necessary items. Small money movements are often hard to detect in a FI’s transaction monitoring system; however, recent developments in artificial intelligence (AI) and data science have given FIs the opportunity to enhance their CTF efforts.

Often, foreign fighters receive training or instructions in an intermediary country before they can enter a conflict zone. Traditional TM system rules and scenarios can only detect very specific and static activity based on the parameters of the designated activity. For example, if a foreign fighter from Kentucky wanted to fight with ISIS in Syria, the subject would have to receive funds from a personal or outside source, then travel to a country in Europe, and then eventually into Syria.

At each step in the process, the subject will most likely require additional funding for lodging, food, and expenses. A foreign fighter’s journey can vary in time based on the required training and logistics for his or her role. Current AI solutions that are supplementing traditional TM systems for FIs can detect fund transfers in any amount that are received in multiple geographic locations over a specific period of time regardless of amount or source.

Once the AI solution resolves an entity, it can easily track small dollar transactions via credit cards, ATM withdrawals, wire transfers, ACH, or branch activity. The key to successful foreign fighter detection by FIs is the ability to track geographic movements of customers over time. AI-enhanced data science solutions can provide this tactical and strategic solution for FIs today and enhance their CTF detection and reporting capability.

Investigators are only human and are more likely to focus on the dangling and shiny object. For example, in a set of financial transactions, would an investigator most likely focus on the $1,000,000 transaction? Or a series of ATM withdrawals ranging from $25 to $300 over a three-day period in different geographic locations totaling about $3,000? Speaking from personal mistakes, the answer is the single $1 million transaction.

This is where AML investigators start to fail because subjects planning terrorist attacks only require small amounts of money to carry out their plans. Attacks can be fulfilled for the cost of a cell phone and a few complimentary components or extend to weapons and/or vehicle purchases. AI solutions are being utilized today that can detect anomalies in small monetary transactions that relate to CTF typologies.

Data science and AI solutions can’t and won’t solve every problem, but they are, nonetheless, viable options for FIs to consider in their CTF risk mitigation profile.


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