An interesting article in the MIT Tech Review, “The pandemic has changed how criminals hide their cash — and AI tools are trying to sniff it out”, describes how the coronavirus pandemic, with its lockdowns, travel disruption and restrictions on economically or cash-sensitive activities, is a headache for criminals trying to launder money.
Each year, according to UN estimates, between $800 billion and $2 trillion of criminal proceeds are laundered and reintroduced into the economy, usually using small businesses that primarily handle cash, and moving it illegally across borders. But the restrictions imposed by the pandemic have forced criminals to seek new strategies to launder their ill-gotten gains that are proving difficult for the authorities to tackle.
The solution could lie in using machine learning’s ability for anomaly detection. Applied to a set of data, anomaly detection identifies outliers that may indicate fraud or data quality problems, without the need to have previously labelled the data. Unsupervised learning tools for anomaly detection — those I am most familiar with are developed by BigML, a company to which I am a strategic advisor — assign a score to each case in a data set of between 0% and 100%, and scores of 60% or more are usually considered outliers.
In many cases, detecting, isolating and eliminating outliers is done systematically to improve the quality of a data set before analysis to find mis-entered information, problems with reading instruments, etc., and can lead to significant improvements in the accuracy and performance when assessing classification and regression models. But in other cases, such as banking or credit card transactions, cyber security, etc., this type of analysis can be used to identify patterns of fraud or intrusion, and can be applied very quickly and easily.
Soon, we will be able to subject data to such analyses at multiple control points. In politics, for example, this kind of technology can detect and isolate cases of corruption, which tend to generate irregular patterns. In the economy at large, the progressive reduction in the use of cash and the consequent analysis of the time series of electronic transactions will also make possible more immediate and direct ways of detecting patterns of money laundering or tax evasion, prompting criminals to seek more sophisticated methods to carry out their activities.
Illicit money is a huge problem that has major social and economic repercussions. For criminals, the pandemic has proved a perfect storm when it comes to blocking their usual sources of laundering, which also coincides with a greater availability of simple analytical tools for detecting their activity patterns. Can we count on the political will needed to put such systems in place? Would this not be a good argument for increasing the adoption of machine learning tools by the authorities?
(En español, aquí)
"machine" - Google News
August 09, 2020 at 04:29PM
https://ift.tt/33IVUVI
Why not use machine learning to crack down on money laundering? - Medium
"machine" - Google News
https://ift.tt/2VUJ7uS
https://ift.tt/2SvsFPt
Bagikan Berita Ini
0 Response to "Why not use machine learning to crack down on money laundering? - Medium"
Post a Comment