By Abbas Panjwani, Greg Oxley, William Downs

Criminals successfully stole £1.2 billion from individuals through banking fraud and scams in 2023 according to industry figures. 1 Fraud accounted for over 40% of crimes against individuals in England and Wales in 2024. And the government has said the total impact of fraud when counting things like emotional harm and investigatory costs is over double that of the direct losses from fraud. Despite the prevalence of fraud and the public costs, the government and police approach to fraud in recent years has been criticised for failing to take the crime seriously. For example, in the year ending March 2024, only around 2% of fraud offences recorded by police were referred to territorial forces for investigation. In 2023 the government published its fraud strategy which included a range of measures to stop fraud happening in the first place, pursue fraudsters when they are successful, and help victims. Some of the main initiatives include establishing a National Fraud Squad of specialist investigators, overhauling the fraud reporting system and requiring payment service providers to reimburse victims of authorised payment fraud. The nature and scale of banking fraud Industry body UK Finance estimates that criminals successfully stole £1.17 billion through banking fraud and scams in 2023. Of this £709 million was unauthorised and £460 million was authorised, levels which have remained broadly flat since 2021. Unauthorised fraud is where the fraudulent transaction is carried out by a third party, not the victim. Authorised fraud involves the victim being tricked into paying money into another account that is controlled by a criminal. This is also known as Authorised Push Payment (APP) fraud. Payment providers almost always reimburse victims of unauthorised fraud. For APP fraud, in 2023 62% of losses were reimbursed, a figure which has increased over time. From October 2024, payment providers are required by law to reimburse APP victims, subject to some conditions.

London: House of Commons Library, 2025. 32p.

By Jetson Leder-Luis and Anup Malani

Data from the Department of Housing and Urban Development (HUD) indicate an unprecedented 43 percent increase in the number of people residing in homeless shelters in the United States between 2022 and 2024, reversing the gradual decline over the preceding sixteen years. Threequarters of this rise was concentrated in four localities – New York City, Chicago, Massachusetts, and Denver – where large inflows of new immigrants seeking asylum were housed in emergency shelters. Using direct estimates from local government sources and indirect methods based on demographic changes, we estimate that asylum seekers accounted for about 60 percent of the twoyear rise in sheltered homelessness during this period, challenging media and policy narratives that primarily attribute this rise to local economic conditions and housing affordability.

WORKING PAPER · NO. 2025-45

Chicago: University of Chicago, The Becker Friedman Institute for Economics, 2025. 21p.

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.