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 powerful solutions for detecting and preventing financial crimes, including money laundering, financial crime detection, cryptocurrency attacks, and market manipulation. These quantum approaches leverage the inherent computational capabilities of quantum computers to overcome limitations faced by classical methods. Furthermore, the paper illustrates how quantum computing can support enhanced financial risk management analysis. Financial institutions can improve their ability to identify and mitigate risks, leading to more robust risk management strategies by exploiting the quantum advantage. This research underscores the transformative impact of quantum algorithms on financial risk management. By embracing quantum technologies, organisations can enhance their capabilities to combat evolving threats and ensure the integrity and stability of financial systems
Scalability metrics focus on determining the maximum volume of data, entities, and interconnections that can be effectively analysed within a given timeframe using quantum computing. Furthermore, the fidelity and interpretability of explanations provided by quantum models can be quantitatively assessed to aid in investigating detected malicious activities. Finally, the capability of quantum computing to identify previously unknown activities or schemes beyond what classical methods can achieve, known as novelty, is an essential metric to consider. Proper evaluation against these benefits and metrics is crucial to validate the value proposition of quantum computing in the context of detecting and combating malicious finance activities. It enables a comprehensive assessment of the effectiveness, efficiency, and practical applicability of quantum approaches, ultimately
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5 Enhancing Money Laundering Detection with Quantum Algorithms The application of financial analytics, network analysis, and machine learning plays a crucial role in detecting patterns, relationships, and risk factors that may go unnoticed by humans but could potentially indicate money laundering or other forms of financial crime. Various techniques are employed for this purpose, including transaction monitoring, customer due diligence, funds flow analysis, behavioural analysis, network analysis, transaction benchmarking, transaction structuring detection, predictive modelling, entity resolution, and data aggregation. Transaction monitoring involves scrutinising wire transfers, deposits, withdrawals, and other activities for suspicious patterns, including cross-border flows. Customer due diligence entails screening customers against watchlists and sanction lists and monitoring transactions t
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Fund flow analysis focuses on tracing the movement of funds across interconnected accounts, corporations, and individuals to unveil money laundering schemes. Behavioural analysis utilises machine learning to establish profiles of typical account usage and identify anomalous transactions that deviate from established behaviours. Network analysis aims to map connections between transacting parties, identifying clusters, common intermediaries, and underground banking systems. Transaction benchmarking involves comparing transaction amounts, frequencies, geographies, and counterparties against peer groups to flag outliers. Transaction structuring detection aims to identify the intentional fragmentation of transactions to evade reporting requirements. Predictive modelling involves developing models that assess transaction risks and prioritise alerts for investigators. Entity resolution focuses on linking related transactions, accounts, and id
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Finally, data aggregation combines internal and external data sources to gain a more comprehensive view of risks. By leveraging these techniques and technologies, financial institutions and law enforcement agencies can enhance their ability to combat financial crime effectively. As mentioned before, QML algorithms, such as QSVMs, can analyse large transaction datasets and swiftly identify suspicious patterns indicative of money laundering. These quantum algorithms outperform their classical counterparts, enabling faster and more accurate classification tasks. Additionally, quantum graph algorithms, specifically quantum walks applied to transaction networks, excel in detecting hidden connections between entities and uncovering complex money laundering rings or schemes at an accelerated pace compared to classical algorithms [32, 22].
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