Quantum algorithms: a new frontier in financial crime prevention

Abstract Financial crimes’ fast proliferation and sophistication require novel approaches that provide robust and effective solutions. This paper explores the potential of quantum algorithms in combating financial crimes. It highlights the advantages of quantum computing by examining traditional and Machine Learning (ML) techniques alongside quantum approaches. The study showcases advanced methodologies such as Quantum Machine Learning (QML) and Quantum Artificial Intelligence (QAI) as potential solutions for detecting and preventing financial crimes, including money laundering, financial crime detection, cryptocurrency attacks, and market manipulation. These quantum approaches may leverage the inherent computational capabilities of quantum computers to overcome limitations faced by classical methods, contingent on continued advances in hardware and error correction. Furthermore, the paper illustrates how quantum computing could support enhanced financial risk management analysis. Financial institutions may improve their ability to identify and mitigate risks through quantum technologies as the field matures. The paper’s primary contribution is a structured three-layer mapping framework connecting financial crime typologies with classical, machine learning, and quantum countermeasures, accompanied by a feasibility assessment of key quantum algorithms. All capability claims are grounded in an explicit discussion of current NISQ-era limitations and a phased roadmap for future experimental validation.