By Cassandra Cross  · Thomas J. Holt 

Using the premise of a genuine relationship, romance fraud ofenders deceive victims for monetary gain. Research on romance fraud has grown, but limited work explores the demographic correlates of victimisation. An assumption exists that older persons are more susceptible to fraud, though this dynamic is not consistently evident in the literature. This article analyses 2686 romance fraud complaints to Scamwatch, an online Australian fraud reporting portal, to identify correlates between being 65 years and older and their risk of victimisation. The fndings illustrate that seniors were not more likely to sufer monetary losses to romance fraud and were less likely to lose personal information compared to victims in other demographic groups. This study afrms the challenge of using demographics to predict romance fraud victimisation and emphasises the need for additional research in this area 

  Security Journal (2025) 38:46

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.