[HTML][HTML] Financial fraud: a review of anomaly detection techniques and recent advances

W Hilal, SA Gadsden, J Yawney - Expert systems With applications, 2022 - Elsevier
With the rise of technology and the continued economic growth evident in modern society,
acts of fraud have become much more prevalent in the financial industry, costing institutions …

[HTML][HTML] Fraud detection: A systematic literature review of graph-based anomaly detection approaches

T Pourhabibi, KL Ong, BH Kam, YL Boo - Decision Support Systems, 2020 - Elsevier
Graph-based anomaly detection (GBAD) approaches are among the most popular
techniques used to analyze connectivity patterns in communication networks and identify …

Financial cybercrime: A comprehensive survey of deep learning approaches to tackle the evolving financial crime landscape

J Nicholls, A Kuppa, NA Le-Khac - Ieee Access, 2021 - ieeexplore.ieee.org
Machine Learning and Deep Learning methods are widely adopted across financial
domains to support trading activities, mobile banking, payments, and making customer credit …

Generating synthetic data in finance: opportunities, challenges and pitfalls

SA Assefa, D Dervovic, M Mahfouz, RE Tillman… - Proceedings of the First …, 2020 - dl.acm.org
Financial services generate a huge volume of data that is extremely complex and varied.
These datasets are often stored in silos within organisations for various reasons, including …

[KNYGA][B] Machine learning and AI for risk management

S Aziz, M Dowling - 2019 - library.oapen.org
We explore how machine learning and artificial intelligence (AI) solutions are transforming
risk management. A non-technical overview is first given of the main machine learning and …

A survey of community detection methods in multilayer networks

X Huang, D Chen, T Ren, D Wang - Data Mining and Knowledge …, 2021 - Springer
Community detection is one of the most popular researches in a variety of complex systems,
ranging from biology to sociology. In recent years, there's an increasing focus on the rapid …

Detecting money laundering transactions with machine learning

M Jullum, A Løland, RB Huseby, G Ånonsen… - Journal of Money …, 2020 - emerald.com
Purpose The purpose of this paper is to develop, describe and validate a machine learning
model for prioritising which financial transactions should be manually investigated for …

Stgsn—a spatial–temporal graph neural network framework for time-evolving social networks

S Min, Z Gao, J Peng, L Wang, K Qin, B Fang - Knowledge-Based Systems, 2021 - Elsevier
Abstract Social Network Analysis (SNA) has been a popular field of research since the early
1990s. Law enforcement agencies have been utilizing it as a tool for intelligence gathering …

Machine learning techniques for anti-money laundering (AML) solutions in suspicious transaction detection: a review

Z Chen, LD Van Khoa, EN Teoh, A Nazir… - … and Information Systems, 2018 - Springer
Money laundering has been affecting the global economy for many years. Large sums of
money are laundered every year, posing a threat to the global economy and its security …

KYC optimization using distributed ledger technology

J Parra Moyano, O Ross - Business & Information Systems Engineering, 2017 - Springer
The know-your-customer (KYC) due diligence process is outdated and generates costs of up
to USD 500 million per year per bank. The authors propose a new system, based on …