++Archer Materials (ASX: AXE)++ has signed a research services agreement with the CSIRO’s data and digital specialist arm to advance quantum machine learning (QML) for fraud detection in financial transactions.
The 12-month project will focus on the development of QML models to enhance the detection of fraudulent activities, with initial outcomes including prototype quantum models and performance benchmarks across simulated financial datasets.
It will build upon CSIRO’s expertise in the development of new and innovative QML models and has the potential to offer enhanced reliability, training speed-up and unique feature extraction capabilities.
Fraud Detection Framework
Once QML models are trained, the fraud detection framework will rapidly identify and flag any anomalies which arise from fraudulent financial activity, which remains a major global challenge costing organisations billions of dollars each year.
Fraud detection requires rapid and accurate identification of anomalies such as forged signatures, unauthorised transactions, and identity manipulation.
Traditional systems are often reactive, complex to maintain, and limited in their ability to detect sophisticated patterns.
Hybrid quantum machine learning models that adapt classical neural network architectures can enable applications beyond fraud detection in areas such as healthcare, energy grid optimisation, manufacturing, autonomous vehicles, defence and aerospace, cybersecurity, and AI.
Critical Global Challenge
“Fraud detection is a critical challenge globally and quantum machine learning offers a transformative way to address it,” Archer chief executive officer Simon Ruffel said.
“This partnership with CSIRO represents a significant step in expanding our quantum intellectual property portfolio and reinforces our strategy to commercialise near-term quantum applications with widespread impact.”
After holding the positions of chief technology officer and engineering manager since joining the company in 2024, Mr Ruffel was recently promoted to Archer’s top job to lead the company’s development of semiconductors to solve issues in medical diagnostics, sensing, and computing.
He brings more than 20 years’ of international experience on technology projects and managing multi-functional hardware, process and software engineering teams for organisations including Microsoft, Applied Materials (US), and the University of Sydney.
