By Jack Nicholls; Aditya Kuppa; Nhien-An Le-Khac

Machine Learning and Deep Learning methods are widely adopted across financial domains to support trading activities, mobile banking, payments, and making customer credit decisions. These methods also play a vital role in combating financial crime, fraud, and cyberattacks. Financial crime is increasingly being committed over cyberspace, and cybercriminals are using a combination of hacking and social engineering techniques which are bypassing current financial and corporate institution security. With this comes a new umbrella term to capture the evolving landscape which is financial cybercrime. It is a combination of financial crime, hacking, and social engineering committed over cyberspace for the sole purpose of illegal economic gain. Identifying financial cybercrime-related activities is a hard problem, for example, a highly restrictive algorithm may block all suspicious activity obstructing genuine customer business. Navigating and identifying legitimate illicit transactions is not the only issue faced by financial institutions, there is a growing demand of transparency, fairness, and privacy from customers and regulators, which imposes unique constraints on the application of artificial intelligence methods to detect fraud-related activities. Traditionally, rule based systems and shallow anomaly detection methods have been applied to detect financial crime and fraud, but recent developments have seen graph based techniques and neural network models being used to tackle financial cybercrime. There is still a lack of a holistic understanding of the financial cybercrime ecosystem, relevant methods, and their drawbacks and new emerging open problems in this domain in spite of their popularity. In this survey, we aim to bridge the gap by studying the financial cybercrime ecosystem based on four axes: (a) different fraud methods adopted by criminals; (b) relevant systems, algorithms, drawbacks, constraints, and metrics used to combat each fraud type; (c) the relevant personas and stakeholders involved; (d) open and emerging problems in the financial cybercrime domain.

IEEE Access ( Volume: 9), 2021, 22p.

Bennett, Phil; Cucos, Radu; Winch, Ryan

From the document: "The intersection of AI and transnational crime, particularly its application in human trafficking, represents an emerging and critically important area of study. This brief has been developed with a clear objective: to equip policymakers, law enforcement agencies, and the technology sector with the insights needed to anticipate and pre-emptively address the potential implications of AI on trafficking in persons. While we respond to the early instances of the use of AI by transnational criminal organisations, such as within Southeast Asia's cyber-scam centres, a more systemic approach is required. The potential for transnational criminal organisations to significantly expand their operations using AI technologies is considerable, and with it comes the risk of exponentially increasing harm to individuals and communities worldwide. It is imperative that we act now, before the most severe impacts of AI-enabled trafficking are realised. We have a unique time-limited opportunity--and indeed, a responsibility--to plan, train, and develop policies that can mitigate these emerging threats. This report aims to concretise this discussion by outlining specific scenarios where AI and trafficking could intersect, and to initiate a dialogue on how we can prepare and respond effectively. This document is not intended to be definitive, but rather to serve as a foundation for a broader, ongoing discussion. The ideas presented here are initial steps, and it will require innovative thinking, adequate resourcing, and sustained engagement from all sectors to build upon them effectively."

Organization For Security And Co-Operation In Europe. Office Of The Special Representative And Co-Ordinator For Combating Trafficking In Human Beings; Bali Process (Forum). Regional Support Office .NOV, 2024