Quantum Machine Learning for Fraud Detection

用于欺诈检测的量子机器学习

• 批准号: 10003408
• 金额: $ 6.24万
• 依托单位: HSBC BANK PLC
• 依托单位国家: 英国
• 项目类别: Feasibility Studies
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=10003408
• 关键词: Quantum; Machine; Learning; 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.

金融欺诈和未经授权的支付对数字经济构成了威胁。每年增加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.