About our research
Our research delivers technology-based solutions to regulatory and compliance challenges. We harness the power of data to drive predictive analytics and real-time monitoring, drastically reducing the burden placed upon business.
Training police for financial crime investigation
Objectives
Financial crime is now more widespread than ever before, costing its victims billions of dollars every year. To combat this growing threat, an equal increase in resourcing and training is required for those who investigate financial crimes, particularly the police. Previous research has suggested that police agencies tend not to prioritise financial crime, with officers lacking the necessary skills to respond to it effectively.
To address this need, the Queensland Police Service (QPS) mandated the completion of an in-house financial crime training program for all officers up to and including the rank of senior sergeant. Following this training, Dr Jacqueline Drew and colleagues undertook an evaluation of its success.
Output and deliverables
The study confirmed that police were under-trained in financial crime investigation, but found that the short, online training program delivered significant improvements in both knowledge and confidence. Of critical importance, it also improved attitudes about this type of crime that might have otherwise deterred officers from investigating it.
This research establishes there are clear benefits to investing in training officers to investigate financial crime, including improvements in knowledge, skills and attitudes. It also has important implications for other stakeholders, such as institutions and government regulators, regarding the need for upskilling in financial crime investigation.
Team
Associate Professor Jacqueline M. Drew, Dr Emily Moir and Detective Inspector Michael Newman
Published Article: Drew, J.M., Moir, E. & Newman, M. (2021).
Financial crime investigation: An evaluation of an online training program for police.
Policing: An International Journal, 44(3), 525-539.
Visualising Blockchain Transaction Behavioural Pattern
Abstract
The transaction data of blockchain networks contain a rich and useful set of intrinsic relationships between blocks, transactions, wallets, and smart contracts. However, the complexity of these relationships and the inherent nature of the data structure and the variety of applications make it challenging to present the data in a readily useable format. Data visualisation tools facilitate efficient ways of understanding implicit and explicit characteristics of complex data sets. The existing visualisation approaches on this topic mainly focus on specific use cases and fail to provide adequate user interaction. This paper proposes a novel graph-based visualisation approach to incorporating automated graph modelling and generalised graph algorithms from blockchain transactions. Our approach enables users to interact directly with the blockchain data using graph queries and provides exploration capabilities through graph patterns. Four case studies are presented, demonstrating the benefits of the proposed approach in identifying the behaviour of anomalous nodes. The visual assessment of behavioural patterns informs the challenges in classifying anomalous nodes. The proposed approach also has the potential to be used in other types of networks for inferring and verifying heuristics.
Team
Samantha Jeyakumar, Dr Zhe Hou, Dr Eugene Yugarajah Andrew Charles, Professor Marimuthu Palaniswami, Professor Vallipuram Muthukkumarasamy.
Exploring identity economics
Objectives
For 30 years, Bernie Madoff ran one of the world’s most famous Ponzi schemes, defrauding his clients for total of $65 billion. But how did he persuade otherwise sophisticated investors to forgo due diligence and ignore obvious red flags?
Madoff’s success can be attributed to what’s known as identity economics—the idea that seeing yourself as part of a specific social group can have a major influence on your decision-making abilities, even when it comes to large sums of money. This runs counter to neo-classical economic assumptions of self-interest, and so offers a powerful new way to understand how and why people make certain decisions.
While the exact conditions that influence decision-making are not yet clear, this research aims to demonstrate that the condition of ‘common knowledge’ or knowledge symmetry is a defining determinant in group identification and, therefore, in investment decisions. By showing how identifying as part of a group can cause people to act and invest counterproductively, it is possible to overcome the self-interest assumptions of neo-classical economics.
Output and deliverables
Demonstrate that group identity is so powerful that it can overcome self-interest in investment decisions.
Benefits:
- A more complete theory of identity economics
- Valuable insights for policy makers and regulators to prevent scams through appropriate regulation
- Alert consumers to scams that rely on the allure of exclusivity, uniqueness and notions of a special in-group
Team
Mr Ryk Bliszczyk, PhD Candidate and Prof Andreas Chai, Director, Adacemy of Excellence in Financial Crime Investigation and Compliance