64th ISI World Statistics Congress - Ottawa, Canada

64th ISI World Statistics Congress - Ottawa, Canada

Utilizing Nighttime Light Data to Predict Money Laundering Activities

Author

AA
Agung Andiojaya

Co-author

Conference

64th ISI World Statistics Congress - Ottawa, Canada

Format: CPS Paper

Keywords: big-data, nighttime-light, suspicious-transaction-reports, viirs

Abstract

The relationship between money laundering and GDP has been extensively researched by several academics. While some of them indicated having a negative association, others claimed the contrary. Two years ago, when the pandemic hit the world, the environment of data creation and consumption underwent a significant transformation. Data supply has been driven by limited funding and human activity, but on the other hand, the amount of data needed to combat pandemic effects has increased (OECD, 2020). This essay will examine the connections between nighttime light (NTL), economic expansion, and money laundering in Indonesian provinces. The examination of the usage of NTL in foretelling the potential rise in cases of money laundering is further expanded upon in this article. The research based on the panel data model's findings will indicate whether the NTL data is statistically significant or not significant for forecasting the rise in cases of money laundering brought on by the province's economic expansion.