Related papers: Detection of money laundering groups using supervi…
Money laundering (ML) is the behavior to conceal the source of money achieved by illegitimate activities, and always be a fast process involving frequent and chained transactions. How can we detect ML and fraudulent activity in large scale…
Given a stream of money transactions between accounts in a bank, how can we accurately detect money laundering agent accounts and suspected behaviors in real-time? Money laundering agents try to hide the origin of illegally obtained money…
Conventional anti-money laundering (AML) systems predominantly focus on identifying anomalous entities or transactions, flagging them for manual investigation based on statistical deviation or suspicious behavior. This paradigm, however,…
In the age of the Internet, people's lives are increasingly dependent on today's network technology. Maintaining network integrity and protecting the legitimate interests of users is at the heart of network construction. Threat detection is…
Bitcoin is by far the most popular crypto-currency solution enabling peer-to-peer payments. Despite some studies highlighting the network does not provide full anonymity, it is still being heavily used for a wide variety of dubious…
Almost all countries in the world require banks to report suspicious transactions to national authorities. The reports are known as suspicious transaction or activity reports (we use the former term) and are intended to help authorities…
The rise of Web3 and Decentralized Finance (DeFi) has enabled borderless access to financial services empowered by smart contracts and blockchain technology. However, the ecosystem's trustless, permissionless, and borderless nature presents…
The rise of digital ecosystems has exposed the financial sector to evolving abuse and criminal tactics that share operational knowledge and techniques both within and across different environments (fiat-based, crypto-assets, etc.).…
The problem of anomaly detection has been studied for a long time. In short, anomalies are abnormal or unlikely things. In financial networks, thieves and illegal activities are often anomalous in nature. Members of a network want to detect…
This systematic literature review examines the role of machine learning in fraud detection within digital banking, synthesizing evidence from 118 peer-reviewed studies and institutional reports. Following the PRISMA guidelines, the review…
Today, money laundering (ML) poses a serious threat not only to financial institutions but also to the nations. This criminal activity is becoming more and more sophisticated and seems to have moved from the clichy of drug trafficking to…
Anti-money laundering (AML) regulations mandate financial institutions to deploy AML systems based on a set of rules that, when triggered, form the basis of a suspicious alert to be assessed by human analysts. Reviewing these cases is a…
Sustainable financial markets play an important role in the functioning of human society. Still, the detection and prediction of risk in financial markets remain challenging and draw much attention from the scientific community. Here we…
Predicting crime using machine learning and deep learning techniques has gained considerable attention from researchers in recent years, focusing on identifying patterns and trends in crime occurrences. This review paper examines over 150…
Analysis of criminal networks is inherently difficult because of the nature of the topic. Criminal networks are covert and most of the information is not publicly available. This leads to small datasets available for analysis. The available…
Objectives: To combat money laundering, banks raise and review alerts on transactions that exceed confidential thresholds. However, the thresholds may be leaked to criminals, allowing them to break up large transactions into amounts under…
The number of blockchain users has tremendously grown in recent years. As an unintended consequence, e-crime transactions on blockchains has been on the rise. Consequently, public blockchains have become a hotbed of research for developing…
USDT, a stablecoin pegged to dollar, has become a preferred choice for money laundering due to its stability, anonymity, and ease of use. Notably, a new form of money laundering on stablecoins -- we refer to as crowdsourcing laundering --…
Financial fraud refers to the act of obtaining financial benefits through dishonest means. Such behavior not only disrupts the order of the financial market but also harms economic and social development and breeds other illegal and…
Studying corruption presents unique challenges. Recent work in the spirit of computational social science exploits newly available data and methods to give a fresh perspective on this important topic. In this chapter we highlight some of…