Deep Learning Anomaly Detection as Support Fraud Investigation in Brazilian Exports and Anti-Money Laundering

By Ebberth L. Paula, Marcelo Bronzo Ladeira, Rommel N. Carvalho, Thiago Marzagão. 2016

120 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Sociology and Political Science

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Patrick Zschech
2. Kai Heinrich
3. Christian Janiesch
4. Rommel N. Carvalho

In Sociology and Political Science

1. Patrick Zschech
2. Christian Janiesch

Citations in the corpus

• Machine learning and deep learning (2021, background): “However, due to their capability of quantifying reconstruction errors, which are assumed to be significantly higher for anomalous samples than for regular instances, they can also be applied for detecting anomalies, such as fraudulent activities in financial markets (Paula et al. 2016).”
• Identifying Anomalies in Parliamentary Expenditures of Brazilian Chamber of Deputies with Deep Autoencoders (2017, methodology): “We choose Autoencoders based on two successful applications in fraud detection [4], [5] in the Brazilian government.”