Money Laundering Observation from Outer Space

Abstract

This paper examines the potential of nighttime light (NTL) data as an alternative data source to predict the number of money laundering events. The study is based on the assumption that money laundering as one of financial crime categories is linked to economic development, and previous research has explored the relationship between NTL and both economic data and crime. Panel regression analysis with random effects was used to investigate the potential of NTL data to estimate money laundering activity, which was measured using Suspicious Transaction Reports (STRs) data as a proxy variable. The results suggest that NTL data can be a promising tool for estimating money laundering activity, providing new insights into the use of alternative data sources in predicting this illegal activity. The findings of this research could also contribute to the development of more effective anti-money laundering strategies by law enforcement and policymakers.

Keywords

• nighttime-light data
• suspicious transaction reports (strs)
• money laundering
• google earth engine
• alternative data sources