Do you wonder how certain technologies will affect you in your career? The Tech Scope column features articles on fintech, artificial intelligence, cryptocurrency and more technological advancements in the anti-financial crime field.
Our contributor is Ari Redbord, head of legal and government affairs at TRM Labs. If you would like to contribute to this column or have any topics you would like to suggest, email editor@acams.org.
Artificial intelligence (AI) has enhanced financial services, improved regulatory compliance and more. However, AI poses an equally dangerous threat to ethical governance, data protection and cybersecurity, as well as fundamental human rights. There are over 70 proposed definitions for AI, one of which describes it...
The anti-money laundering (AML) community is drowning in false positives. A 2018 review by Microsoft found most transaction monitoring systems have a 95%-99% false positive rate.1 Machine learning and artificial intelligence have been widely viewed as the solution to reducing the number of false...
Financial institutions (FIs) are using advanced analytical techniques, machine learning or some form of artificial intelligence (AI) to reduce compliance costs—or at least that is how it seems. The reality is that many FIs are taking advantage of these advanced methods. The institutions using...
Financial crime and compliance teams are continuously trying to improve money laundering detection while ensuring better alignment with regulators’ expectations. Areas hindering this success are the perpetual concerns for transparency and the partnership with model governance teams. Ten years ago, when the Office of...
This article will provide the setup and execution for a technique to identify how probable it is that a transaction monitoring (TM) alert is nonproductive―sometimes referred to as a false positive. The basis of this exercise was to apply machine learning algorithms to the...
In movies, investigators put the pieces together. With a massive wall of mugshots, maps and post-it notes, they connect the dots using years of honed intuition—and red yarn. It is an analytical process, and it is exciting. In reality, investigators, especially anti-money laundering (AML) investigators,...
The application of big data and machine learning in anti-money laundering (AML) transaction monitoring (TM) programs is undeniably vast. Over the past five years, financial institutions (FIs) have assessed how they could implement and benefit from machine learning capabilities. Some FIs have already leveraged machine...
New technology platforms offer the promise of dramatic improvements in financial crimes programs for banks and other financial institutions (FIs). But for all its power and potential, technology cannot adequately address today’s anti-money laundering (AML) challenges by itself. To be effective, new technology must...
I was not in the office in Cambridge the day two deadly explosions ripped through the finish line of the Boston marathon. I was working from home, fielding calls from concerned employees. For many Boston natives, one of the worst things about the bombing...
This is part two of a two-part series outlining the key phases and technology required to build social media monitoring driven by artificial intelligence (AI) for anti-money laundering (AML). Part one focused on the first two steps in this pipeline: collection and extraction. Part...
