Deep learning and explainable artificial intelligence techniques applied for detecting money laundering–a critical review
Money laundering has been a global issue for decades, which is one of the major threat for
economy and society. Government, regulatory and financial institutions are combating it …
economy and society. Government, regulatory and financial institutions are combating it …
Money Laundering: A Bibliometric Review of Three Decades from 1990 to 2021
Purpose: This study aims to employ bibliometric analysis to condense multiple studies into a
single publication that not only gives insights into the growth and advancement of the …
single publication that not only gives insights into the growth and advancement of the …
[HTML][HTML] Predicting fraud in financial payment services through optimized hyper-parameter-tuned XGBoost model
S Dalal, B Seth, M Radulescu, C Secara, C Tolea - Mathematics, 2022 - mdpi.com
Online transactions, medical services, financial transactions, and banking all have their
share of fraudulent activity. The annual revenue generated by fraud exceeds $1 trillion. Even …
share of fraudulent activity. The annual revenue generated by fraud exceeds $1 trillion. Even …
Artificial intelligence in accounting and finance: Challenges and opportunities
Z Yi, X Cao, Z Chen, S Li - IEEE Access, 2023 - ieeexplore.ieee.org
The rapid expansion of artificial intelligence (AI) technologies presents novel technical
solutions to traditional accounting and finance problems. Despite this, scholars in …
solutions to traditional accounting and finance problems. Despite this, scholars in …
[HTML][HTML] Factors influencing the choice of technique to launder funds: The APPT framework
This paper proposes a new framework to provide insights into the techniques launderers
adopt to clean illicit funds, drawing on existing literature and theories including rational …
adopt to clean illicit funds, drawing on existing literature and theories including rational …
Anti-money laundering by group-aware deep graph learning
Anti-money laundering (AML) is a classical data mining problem in finance applications. As
well known, money laundering (ML) is critical to the effective operation of transnational and …
well known, money laundering (ML) is critical to the effective operation of transnational and …
Dark web data classification using neural network
There are several issues associated with Dark Web Structural Patterns mining (including
many redundant and irrelevant information), which increases the numerous types of …
many redundant and irrelevant information), which increases the numerous types of …
Incorporating machine learning and a risk-based strategy in an anti-money laundering multiagent system
CR Alexandre, J Balsa - Expert Systems with Applications, 2023 - Elsevier
Over the last decade, the international community has become more aware of the danger
and harm caused by the practice of the crime of Money Laundering (ML). Organizations to …
and harm caused by the practice of the crime of Money Laundering (ML). Organizations to …
Towards Understanding Asset Flows in Crypto Money Laundering Through the Lenses of Ethereum Heists
With the overall momentum of the blockchain industry, financial crimes related to blockchain
crypto-assets are becoming increasingly prevalent. After committing a crime, the main goal …
crypto-assets are becoming increasingly prevalent. After committing a crime, the main goal …
Mobile money fraud detection using data analysis and visualization techniques
Financial investigations in the realm of fraud detection demand rigorous data analysis to
identify anomalies and inform decision-making. This paper demonstrates the importance of …
identify anomalies and inform decision-making. This paper demonstrates the importance of …