Transformation of Suspicious Activity Reporting to combat Financial and Cyber Crime

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An integral part of how the United Kingdom tackles money laundering criminality is through the use of Suspicious Activity Reports (SAR). These were first introduced in 1986 by the Drug Trafficking Offences Act, and have evolved through the 2002 Proceeds of Crime Act and the 2019 Money Laundering Regulations. Organisations may file a SAR with the National Crime Agency if they believe they are being utilised as part of a money laundering campaign, however over the years the effectiveness of SARs have been questioned. For example, its deficiencies included an ineffective SARs database, weak monitoring of enforcement outcomes, inadequate training and the lack of government support for the scheme.  It has therefore been suggested that SARs are under-used by law enforcement agencies, and law enforcement bodies continue to have poor management information on how SARs are utilised.

To address this concern, a multi-disciplinary team of UWE Bristol researchers are working with Synalogik Innovations to overcome the shortcomings of the UK Suspicious Activity Reporting scheme, in collaboration with Cardiff University and University of Reading. The multi-disciplinary team of academics includes Phil Legg (Professor of Cyber Security, UWE), Sam Bourton (Lecturer in Law, UWE), as well as Nic Ryder (Professor of Financial Crime, Cardiff University), and Dr Henry Hillman (Lecturer in Law, University of Reading). The team have a long-standing history of working with Synalogik Innovations, in relation to identifying and mitigating against Counter-Terrorism Financing and Organised Crime Groups through the use of technology. In this latest Innovate UK project, the team will explore how Natural Language Processing can aid the creation of SARs, as well as the verification of information presented, and the identification of further supporting information, using the SCOUT platform developed by Synalogik Innovations. With an improved search capability to facilitate the creation and reporting of SARs, we aim to provide a more efficient approach that can help reduce the time in understanding and responding to threats in our society.

Measuring the Suitability of Artificial Intelligence in Autonomous Resilience for Cyber Defence

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Artificial Intelligence has attracted wide use in many aspects of society, from facial recognition and recommendation systems, through to predicting crime rates and autonomous vehicles. AI technologies are widely used in defence, including how agent-based systems can detect and respond to cyber threats when under attack from adversaries.

Whilst this continues to be a ripe area of research, there are important questions to be asked about the suitability of AI within autonomous resilience for cyber defence, relating to the usability of AI, specifically on how end users may utilise the decisions that are generated by an AI defence system, and how an end user can better understand and reason about how the decisions of the AI are formulated.

UWE researchers Professor Phil Legg and Andrew McCarthy are working with TRIMETIS and PA Consulting to address this important research question, supported by QinetiQ and the Defence Science and Technology Laboratory (DSTL). The project is part of the SERAPIS Framework that supports rapid research and innovation to supply into the UK Ministry of Defence.

This programme of research will impact on how the UK can better identify, investigate and respond to threats in the cyber domain, as well as the impact of cyber across traditional defence areas of land, sea, air and space, and understand the role that artificial intelligence and agent-based systems will have in maintaining the defence and security of the UK.

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