Quantum Machine Learning for Fraud Detection

Financial fraud and unauthorised payments represent a threat to the digital economy. With the annual increase of 5% in transaction rates, and 60.3 million payment card transactions performed in the UK every day, accurate fraud detection requires the analysis of large datasets. For the card transaction total of £829 billion in 2019, £620.6 million were lost due to fraudulent and unauthorized operation \[UK Finance, Fraud the Fact 2020 report\]. The current fraud prevention rate is estimated to beat 62% percent. To minimise the incidence and impact of cyber threats, this must be improved.

HSBC uses artificial intelligence (AI) in various branches of business to improve operations, offer data-driven predictions, increase customer satisfaction, and detect financial fraud. The latter requires analysing financial data to identify and flag the unusual and potentially fraudulent activity. As the strategies employed by fraudsters change over time, the detection needs to be performed without prior knowledge about normal (nominal) and abnormal (fraudulent) transactions. In this ISCF Germinator project, HSBC will partner with the University of Exeter to develop quantum computing protocols for anomaly detection to address this challenge: advancing the state-of-the-art unsupervised Machine earning (ML) methods with quantum computing approaches. Testing these methods as a proof-of-concept, we will assess the power of unsupervised ML with quantum resources and estimate the timeline for their future implementation.

Our goal is to develop a quantum-enabled solution which will significantly reduce and prevent fraud, whilst building on current state-of-the-art ML solutions.