The popular documentary “Tiger King” on Netflix gripped the world with its in-depth look at the business of exotic animal trafficking and the character of those involved.
This billion-dollar business is often linked with other types of organized crime, such as terrorism, drug trafficking and arms trading. Around the world, criminals exploit wildlife to generate funding, with support provided by cash produced by other crimes. This is a part of the perpetual ecosystem that is money laundering.
Banks are bound by law to highlight suspicious customer activity, so it is imperative to be able to identify, evaluate and track criminal behavior so that the flow of financials can be traced to the source.
Criminals, especially those within sophisticated groups, change their tactics regularly to avoid detection. Collaboration between the crimefighters looking out for this illegal activity allows for shared intelligence to catch criminals, who may move from bank to bank to evade detection. With shared information, financial crimes investigators would be aware of suspicious behaviors that are indicative of specific crimes – such as animal trafficking – and be able to intervene much earlier.
Machine learning and adaptive behavioral analytics, which can monitor and recognize anomalies across the enterprise, can be vital tools in this effort.
A challenge is that advanced machine learning models can only iterate through the different indicators if high-quality data is being fed through, and most financial crime experts admit that such data difficult to obtain. The adage “garbage in, garbage out” rings especially true in this scenario.
Spotting specific crimes like human and wildlife trafficking, drug smuggling and terrorist financing is notoriously difficult. It requires experts who know the clues – such as information on cross-border movement of money, animals or people, shipping and customs documents and networks of entities – that indicate complex criminal networks. These clues can vary from group to group and location to location.
Working in conjunction with experts who have specific knowledge of certain types of financial crimes ensures an appropriate machine-learning model is built, that the right data is being fed into it, and that the proper logic is applied in real time to thousands of transactions per second.
The model is built to monitor for known suspicious behaviors, such as transacting with a high-risk jurisdiction or an unusual purchase. In these cases, the behaviors may be legitimate – anyone can make an impulsive purchase — so the model must accurately identify the risk without flagging every bit of activity as a false positive that analysts must investigate.
As behaviors change, the models adapt and assess the legitimacy of new activity based on recent data and, while this happens automatically, an analyst can also step in and adjust a model to improve its performance.
For example, frequent one-way air tickets may indicate human trafficking. This activity can then be extrapolated to find similar behavioral indicators. The human experts can then assess whether the model has correctly identified suspicious activity using anomaly detection, informing the machine-learning models with feedback that will improve performance. This blend of human knowledge and the power of machine learning enables financial institutions to keep up with the constant stream of criminals who are continually adapting their behavior in an effort to perpetuate financial crimes.
“Tiger King” put the issue of wildlife trafficking into a brighter spotlight. Even though terrorist financing and drug trafficking are still more widely regarded as money-laundering risks, banks must be able to know what data to use to recognize all behavioral patterns of financial crime, not just the more prominent ones .
Ensuring that there are experts in bank task forces that can inform the rules and machine-learning models to identify wildlife trafficking is key. It is an effort that combines advanced machine learning and adaptive behavioral analytics with cross-industry expertise and shared intelligence between financial institutions to track the source of funds, conduct network analysis and identify suspicious accounts.
Not only will this ensure “Tiger King” doesn’t get a sequel, it will make the world a much safer place overall.
In this month’s BAI Executive Report, we examine where things stand with fraud protection and how it can be done more efficiently and effectively, including looking at the role of both humans and technology in fraud prevention strategies. Download Now...
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