# Quantum Algorithms: A New Frontier in Financial Crime Prevention

Abraham Itzhak Weinberg  AI-WEINBERG, AI Experts, Tel Aviv, Israel,
<EMAIL_ADDRESS>Alessio Faccia  University of Birmingham Dubai, Dubai,
UAE<EMAIL_ADDRESS>
###### 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 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.

Keywords: Quantum Finance, Financial Crimes Mitigation, Artificial
Intelligence, Quantum Algorithms, Quantum Machine Learning (QML), Machine
Learning (ML), Cryptocurrency

##  1 Introduction

Entering the Quantum Decade, as highlighted by IBM [1], signifies the
beginning of a transformative period in quantum computing. This decade is
expected to see the development of quantum computing from theoretical
exploration to practical applications that tackle some of the most complex
challenges across various industries. IBM’s Quantum Network, a global
community of over 210 Fortune 500 companies, leading academic institutions,
startups, and national research labs, illustrates a collective effort to
utilise the potential of quantum computing. This network exemplifies
collaborative efforts to accelerate progress in quantum computing, with the
ultimate aim of developing applications that solve previously unsolvable
problems. As we explore the capabilities of quantum computing, especially in
improving quantum algorithms for fighting financial crimes, it is clear that
the Quantum Decade is not just a prediction of technological progress but a
call to action for organisations to prepare for the quantum revolution.
Quantum Machine Learning (QML) strives to develop and implement techniques
that can be executed on quantum computers to tackle the standard tasks of
supervised, unsupervised, and Reinforcement Learning (RL) found in classical
Machine Learning (ML). QML’s utilisation of quantum operations sets it apart,
coupling the extraordinary properties of quantum computing, such as
superposition, tunnelling, entanglement, and quantum parallelism. This paper
relates to Quantum Neural Networks (QNNs), the quantum equivalent of classical
neural networks.
QML represents a complex intersection of quantum computing and machine
learning (ML), aiming to revolutionise how we approach data analysis, pattern
recognition, and decision-making processes. QML addresses the core tasks of
supervised, unsupervised, and reinforcement learning central to classical ML
but with a distinctive twist by leveraging techniques executable on quantum
computers. Integrating quantum operations—taking advantage of superposition,
tunnelling, entanglement, and quantum parallelism—enables QML to process
information in ways that classical systems cannot match. At the heart of this
innovative field are Quantum Neural Networks (QNNs), which embody the quantum
counterpart to classical neural networks. QNNs promise to enhance
computational capabilities significantly [2], offering new avenues for complex
problem-solving and algorithmic efficiency. Unlike their classical
counterparts, QNNs can theoretically handle vast datasets with unparalleled
speed, thanks to their ability to operate in high-dimensional Hilbert spaces.
This capability is particularly promising for tasks requiring large-scale data
analysis in bioinformatics, climate modelling, and financial forecasting.
Furthermore, QML is not merely about transposing existing ML algorithms into
the quantum domain; it includes creating new algorithms native to quantum
computing. These quantum-specific algorithms are designed to exploit the
natural advantages of quantum states, potentially leading to algorithmic
complexity and computational speed breakthroughs. For instance, quantum
algorithms for pattern recognition can operate more efficiently by exploring
all possible patterns simultaneously due to quantum superposition.
Additionally, QML’s exploration of quantum entanglement offers novel ways to
represent and understand complex correlations in data that classical
algorithms struggle to untie [3]. This aspect is particularly intriguing for
developing more sophisticated models of human cognition and social networks,
where the intricacies of interconnections and influences are beyond the reach
of classical computation. The synergy between quantum computing and machine
learning, as embodied in QML and QNNs, heralds a new era of computational
intelligence. By transcending the limitations of traditional computing
paradigms, QML opens the door to solving some of the most challenging problems
facing various scientific and industrial fields today. As we stand on the
brink of this computational revolution, the potential applications and
implications of QML and QNNs continue to unfold, promising profound impacts on
technology, society, and our understanding of systemic complexity.
Table 1 below categorises the most relevant types of Financial Crimes. It
offers a comprehensive framework for classifying financial crimes, integrating
and harmonising key concepts from globally recognised entities such as the
Association of Certified Fraud Examiners (ACFE), the Financial Action Task
Force (FATF), Anti-Money Laundering (AML) standards, the United Nations Office
on Drugs and Crime (UNODC), and the International Monetary Fund (IMF). This
framework encapsulates the vast range of criminal activities these
organisations aim to combat by categorising financial crimes into broad areas
such as fraud and scams, money laundering, terrorist financing, corruption and
bribery, cybercrime, market abuse, tax evasion, and identity theft. Each
category and subcategory reflects the definitions and concerns highlighted by
these entities, ensuring a broad coverage that includes various types of
financial crimes identified in their reports and recommendations.
The framework’s inclusivity allows for a structured approach to understanding
financial crimes, offering a solid foundation for further analysis, policy
formulation, and the development of countermeasures. For instance, the FATF’s
focus on money laundering and terrorist financing finds a direct
correspondence in Table LABEL:table:countermeasures, ensuring that the global
priorities in combating financial crimes are well-represented. Similarly, the
ACFE’s extensive work on fraud and corruption is mirrored in the detailed
subcategories under ‘Fraud and Scams’ and ‘Corruption and Bribery’, respecting
the depth of their research and guidance. The inclusion of cybercrime
acknowledges the growing concern of entities like the UNODC and IMF regarding
the digital dimension of financial crimes, emphasising the evolving nature of
these threats. Moreover, the framework’s structure facilitates easy mapping
and integration of specific guidelines, recommendations, and reporting
requirements from these organisations, making it a versatile tool for academic
and practical applications. It supports the alignment of national and
international efforts in the fight against financial crimes, encouraging a
unified approach that can enhance effectiveness and cooperation across
borders.
In essence, Table 1 serves as a foundational piece in the larger puzzle of
global financial crime prevention, offering a starting point to explore,
match, and integrate the diverse and rich perspectives of leading
organisations in the field. It aims to foster a holistic understanding and
approach to combating financial crimes, reflecting the complexity and
interconnectivity of these illegal activities in the modern world.

Category |  Subcategory |  Description
---|---|---
Fraud and ”Scams” |  Consumer Fraud |  Deceptive practices causing loss in consumer transactions.
|  Corporate Fraud |  Misconduct within corporations for financial gain.
|  Securities and Investment Fraud |  Market manipulation, Ponzi schemes, insider trading.
|  Insurance Fraud |  False claims to obtain insurance benefits.
Money Laundering and Proceeds of Crime |  Traditional Money Laundering |  Concealing origins of illegally obtained money through transactions.
|  Cryptocurrency Laundering |  Using digital currencies to hide the source of funds.
|  Asset Laundering |  Reinvesting criminal proceeds into assets to conceal origins.
Terrorist Financing |  Domestic Financing |  Funding terrorist activities within a country.
|  International Financing |  Transferring funds across borders to support terrorism.
Corruption and Bribery |  Public Sector Corruption |  Misuse of official powers in the public sector for personal gain.
|  Private Sector Corruption |  Bribery and misconduct in private industry.
|  Transnational Bribery |  Bribes paid to foreign officials for business advantages.
Cybercrime |  Financial Data Breaches |  Unauthorized access to financial data leading to theft or fraud.
|  Online Fraud and Scams |  Cyber methods targeting individuals or organizations for financial gain.
|  Ransomware and Extortion |  Demanding payment to prevent or undo cyberattacks.
Market Abuse and Insider Trading |  Market Manipulation |  Artificially inflating or deflating the price of securities.
|  Insider Trading |  Trading based on non-public, material information.
Tax Evasion and Avoidance |  Domestic Tax Evasion |  Illegally hiding income from national tax authorities.
|  Offshore Tax Evasion |  Using foreign jurisdictions to evade taxes.
|  Aggressive Tax Planning |  Exploiting loopholes to minimize taxes without direct illegality.
Identity Theft and Financial Impersonation |  Identity Theft |  Using someone else’s personal information for financial gain.
|  Financial Impersonation |  Assuming a false identity to commit financial crimes.
Table 1: Categories of Financial Crimes.

Table 2: Technological and Algorithmic Countermeasures to Financial Crimes. Financial Crimes |  Technological Countermeasures |  ML Countermeasure Approach |  Examples of Quantum Algorithms for Countermeasure Approaches
---|---|---|---
Consumer Fraud |  AI-powered fraud detection systems |  Supervised learning for pattern recognition |  Quantum anomaly detection algorithms use Quantum Support Vector Machines (QSVM) to identify unusual patterns.
Corporate Fraud |  Digital forensic tools |  Network analysis for detecting fraudulent transactions |  Grover’s search algorithm for fast retrieval enhances quantum clustering algorithms to identify groups with similar fraudulent patterns.
Securities and Investment Fraud |  Blockchain for transparent transactions |  Anomaly detection algorithms |  Quantum Principal Component Analysis (QPCA) for dimensionality reduction in large datasets, improving classification accuracy.
Insurance Fraud |  Predictive analytics for claim analysis |  Classification algorithms for fraudulent claims |  Quantum algorithms for pattern recognition and anomaly detection in claims data, potentially using Quantum Approximate Optimisation Algorithm (QAOA).
Traditional Money Laundering |  AI-powered transaction monitoring |  Clustering algorithms for detecting suspicious activities |  Quantum machine learning (QML) for network analysis, using quantum-enhanced clustering to reveal hidden structures in transaction data.
Cryptocurrency Laundering |  Blockchain analytics tools |  Anomaly detection in cryptocurrency transactions |  Quantum simulations to trace complex cryptocurrency flows and identify laundering schemes with unprecedented speed and accuracy.
Asset Laundering |  Digital asset tracking systems |  Machine learning models for asset tracing |  Quantum-enhanced algorithms for analysing asset registration and transaction patterns to uncover illicit financial flows.
Domestic Financing |  Network analysis tools |  Regression analysis for fund tracking |  Quantum algorithms for complex network simulations, aiding in the identification of covert funding channels for terrorist activities.
International Financing |  Cross-border transaction monitoring |  Deep learning for pattern recognition in financial flows |  Quantum simulations to model and analyse international financial networks for tracking terrorist financing across borders.
Public Sector Corruption |  E-governance platforms |  Text mining for anomaly detection in procurement data |  Quantum computing techniques for deep analysis of government procurement data to identify patterns indicative of corruption.
Private Sector Corruption |  Compliance monitoring AI |  Predictive analytics for risk assessment |  Quantum-enhanced pattern recognition in corporate transactions and communications to detect bribery and misconduct.
Transnational Bribery |  International database integration |  Machine learning for identifying risk patterns |  Quantum algorithms for analysing global financial transactions to uncover transnational bribery schemes.
Financial Data Breaches |  Encryption technologies |  Anomaly detection for unauthorised access |  Quantum key distribution (QKD) for secure communications, ensuring data integrity against cyber threats.
Online Fraud and Scams |  Behavioural analytics |  Supervised learning for email filtering |  Quantum-enhanced machine learning for real-time fraud detection, significantly reducing false positives in online transactions.
Ransomware and Extortion |  Anti-malware tools with AI |  Predictive models for threat detection |  Quantum-resistant encryption methods to safeguard data against ransomware attacks and ensure privacy.
Market Manipulation |  Real-time market surveillance systems |  Time series analysis for detecting manipulation |  Quantum optimisation algorithms to solve complex market prediction models more efficiently than classical algorithms.
Insider Trading |  Anomaly detection in trade data |  Classification models for unusual trading patterns |  Quantum algorithms for analysing vast amounts of market data and communications to detect insider trading activities.
Domestic Tax Evasion |  Automated tax reporting and analysis systems |  Regression models for income prediction |  Quantum Fourier Transform (QFT) algorithms identify cyclical patterns in financial data indicative of evasion schemes.
Offshore Tax Evasion |  International tax compliance databases |  Network analysis for offshore entities |  Quantum-enhanced analysis of global financial transfers to detect and trace offshore tax evasion strategies.
Aggressive Tax Planning |  AI for legal document analysis |  Machine learning for loophole identification |  Quantum computing is used to analyse complex tax planning strategies and identify potentially abusive schemes.
Identity Theft |  Biometric verification systems |  Machine learning for biometric authentication |  Quantum-enhanced machine learning for real-time identity verification, significantly reducing the risk of identity theft.
Financial Impersonation |  Digital identity verification platforms |  Deep learning for face recognition |  Quantum simulations for detecting and analysing patterns of financial impersonation across digital platforms.

The above classification matches the technological countermeasures, the ML
Countermeasure Approaches and the Examples of Quantum Algorithms that can be
used for each case as it is disclosed in Table LABEL:table:countermeasures.

##  2 Advantages of Quantum Artificial Intelligence (QAI) Over Conventional AI
Methods

Quantum Artificial Intelligence (QAI) offers several advantages over
conventional Artificial Intelligence (AI) methods [4]. Quantum algorithms
provide exponential speedups for ML, simulation, and optimisation tasks,
enabling real-time or predictive analysis of significantly larger datasets.
Additionally, QAI can solve problems that are intractable for classical
computers, such as integer factorisation and certain machine learning tasks,
due to the exponential resource requirements of these problems. Moreover, the
massive parallelism of quantum systems allows QAI models to potentially
recognise complex patterns across vast amounts of data, surpassing the
capabilities of classical AI. Quantum techniques like amplitude amplification
and quantum dimensionality reduction excel at automatically discovering
meaningful features without human input. Quantum representations can encode
highly non-linear and intricate relationships between variables, often present
in real-world data. Furthermore, entanglements of quantum systems have the
potential to provide new intuitive insights about data beyond what classical
models offer. Certain QAI algorithms, like variational circuits, can maintain
advantages even in near-term device noise.
Additionally, combining quantum and classical techniques into hybrid systems
may yield capabilities greater than either paradigm alone. QAI offers
performance, representational, and insightful benefits compared to
conventional AI methods. Its speed, scalability, pattern recognition
abilities, feature discovery techniques, representation of non-linear
relationships, potential for new insights, noise tolerance, and hybrid
approaches all contribute to its promise in advancing AI capabilities. In
addition to the above, several metrics can be used for comparing QAI to
traditional ML, such as Accuracy, Training time, Inference time, Data size,
Scalability, Generalization, Flexibility, Explainability, Energy efficiency,
and Cost of training/deployment [5, 6].
Firstly, in terms of accuracy, QAI can potentially achieve higher accuracy
with larger and more complex datasets, thanks to quantum advantages. Secondly,
QAI models could significantly reduce training time by leveraging quantum
acceleration or amplification. Additionally, QAI may enable near-instant
inference on large datasets, thereby improving inference time. Moreover, QAI
has the potential to effectively process massive datasets that surpass the
limits of classical computing, addressing big data problems. Furthermore,
quantum parallelism could exponentially enhance the scalability of QAI models,
allowing for the incorporation of more training data and parameters. QAI
models may also exhibit better generalisation by enhancing pattern recognition
capabilities. With quantum computational flexibility, QAI models can be easily
adapted or retrained for new tasks, enabling highly versatile lifelong
learning models. In terms of explainability, certain QAI approaches, such as
variational circuits, may offer more interpretability compared to deep
learning models. Additionally, QAI’s inherent parallelism can lead to
significantly lower power consumption, making it more energy-efficient for
specific tasks. Finally, while there may be initial costs associated with
hardware, expertise, and infrastructure, the cost of training and deploying
QAI models is expected to decrease over time as technology progresses, making
it a more economical choice.

##  3 Unleashing the Power of Quantum Computing in Financial Crime

Quantum computing offers a range of powerful applications in financial crime
detection and prevention. QML models, such as Quantum Neural Network (QNN) and
Quantum Support Vector Machine (QSVM), have the potential to analyse massive
financial transaction datasets much faster than classical AI methods [7]. They
can identify complex money laundering or fraud schemes that might go unnoticed
by conventional approaches. Quantum Natural Language Processing (QNLP) can
swiftly sift through various texts, including reports and calls, to detect
signs of economic crimes by better understanding semantics and contextual
concepts [8, 9].
Quantum Reinforcement Learning (QRL) agents can interact with synthetic
environments that simulate financial systems at scale [10, 11]. It enables
them to identify new vulnerabilities exploited by criminals, providing
valuable insights for strengthening security measures. Quantum recommendation
systems can learn customer behaviours and promptly detect anomalous activities
[12], allowing for immediate flagging and investigation before significant
losses occur. Furthermore, quantum dimensionality reduction techniques extract
meaningful features from high-dimensional time series and other real-world
financial data. It enhances the ability to accurately detect anomalies and
suspicious patterns [13]. Quantum clustering algorithms also play a
significant role by grouping related criminal activities or entities that
transcend borders or employ dissimilar techniques [14], providing a more
comprehensive understanding of interconnected criminal networks.
Quantum simulations of markets and investment strategies offer insights into
systemic risks and allow for testing interventions at a speed and scale that
classical computers cannot match [15]. Finally, combining the advantages of
both paradigms, hybrid quantum-classical models can potentially transform how
financial intelligence agencies and banks leverage data to prevent financial
harm. By harnessing the power of quantum computing alongside classical methods
[16], these hybrid models can revolutionise the approach to combating
financial crimes. In summary, the applications of quantum computing in
financial crime detection and prevention are vast and promising. From
analysing large datasets to understanding complex semantics and detecting
anomalies in real-time, quantum computing is poised to reshape the context of
safeguarding financial systems and preventing illicit activities.
As mentioned before, QML algorithms, such as QSVM, can revolutionise the
analysis of large transaction datasets by detecting patterns that indicate
money laundering, fraud, and other illicit activities [17, 18]. The quantum
version of these algorithms can perform the classification task exponentially
faster. In addition, quantum annealing and Variational Quantum Eigensolver
(VQE) algorithms offer the opportunity to optimise risk models for extensive
portfolios, enabling the detection of abnormal financial activities associated
with economic crimes like insider trading. Quantum Principal Component
Analysis (QPCA) and other dimension reduction techniques can extract
meaningful features from multidimensional financial time series, aiding in
identifying anomalies and suspicious transactions related to theft and
embezzlement. Furthermore, quantum order finding algorithms, such as Shor’s
algorithm [19], can analyze periodic patterns in financial market indicators
and transactions, uncovering cyclical behaviour exploited by perpetrators of
economic crimes such as pump-and-dump schemes. By leveraging quantum graph
algorithms, particularly quantum walk-based algorithms applied to transaction
networks, hidden connections between entities involved in economic crimes can
be detected faster than with classical methods [20]. Quantum simulations of
financial processes and models have the potential to accelerate stress testing
of controls and risk analysis, facilitating the identification of new
vulnerabilities exploited by economic criminals [21]. Moreover, quantum
sampling abilities can generate realistic synthetic economic crime datasets,
which can be used to train machine learning models for detection and
prevention. Finally, combining hybrid quantum-classical algorithms with
investigative techniques and domain expertise holds the promise of
revolutionising economic crime fighting. By harnessing the power of quantum
computing alongside classical approaches, we can potentially enhance our
ability to combat and prevent economic crimes more effectively.
Financial crimes such as money laundering, terrorist financing, fraud, and tax
evasion have significant financial implications for society, costing billions
of dollars each year. These illicit activities erode trust in the financial
system and facilitate more severe criminal behaviours. In the past, financial
institutions primarily relied on human analysts to monitor transactions and
report suspicious activities. However, modern financial systems’ scale,
complexity, and speed have surpassed human capabilities. Enormous volumes of
data are generated daily, holding the potential to reveal criminal patterns.
Advanced analytics and AI offer promising solutions to this challenge. By
processing vast amounts of unstructured data, including transactions,
documents, and communications, AI technologies can augment human analysts and
identify intricate patterns and anomalies that might go unnoticed. Equipped
with the right tools and access to aggregated transaction data, regulators and
law enforcement agencies can more effectively detect criminal networks, track
illicit financial flows, and prevent unlawful funds from infiltrating the
financial system.
From a business perspective, financial firms aim to fortify their platforms
against criminal exploitation, minimise fines and reputational risks, and
assure customers that their assets are secure. Advanced monitoring and AI-
based systems safeguard the interests of all stakeholders involved. Moreover,
the application of these new technologies holds great promise in enabling
authorities to swiftly and efficiently identify threats to public safety, such
as terrorism, human trafficking, corruption, and other forms of organised
criminal schemes that undermine society as a whole.
QML models, such as QNN and QSVM, offer the ability to analyse massive
financial transaction datasets at a much faster pace than classical AI
methods. These models excel in identifying complex money laundering or fraud
schemes that may elude traditional approaches. Quantum natural language
processing enables quick sifting through texts like reports and calls,
leveraging a better understanding of semantics and contextual concepts to
detect signs of economic crimes more effectively [22, 23]. Furthermore,
quantum reinforcement learning agents can interact with synthetic environments
that simulate financial systems on a large scale. It allows them to identify
new vulnerabilities exploited by criminals, providing valuable insights for
enhancing security measures. Quantum recommendation systems learn customer
behaviours and promptly detect anomalous activities, enabling immediate
flagging for investigation, thus mitigating potential significant losses.
Quantum dimensionality reduction techniques extract meaningful features from
high-dimensional time series and other real-world financial data, improving
anomaly detection capabilities. Quantum clustering algorithms comprehensively
group related criminal activities or entities that transcend borders or employ
dissimilar techniques, enhancing the understanding of interconnected criminal
networks. Moreover, quantum simulations of markets and investment strategies
contribute to a deeper comprehension of systemic risks and enable testing
interventions at a speed and scale not achievable with classical computers.
Finally, hybrid quantum-classical models, harnessing both paradigms’
advantages, can fundamentally transform how financial intelligence agencies
and banks leverage data to prevent financial harm. In summary, quantum
computing applications in financial crime detection and prevention encompass
many capabilities. From analysing massive datasets and understanding complex
semantics to identifying vulnerabilities and mitigating risks, quantum
computing has the potential to revolutionise the way we safeguard financial
systems and combat illicit activities.
The effective detection of financial crimes relies on the utilisation of
advanced analytics, AI, and extensive financial data to uncover both familiar
and novel manipulation techniques employed by criminal organisations.
Transaction monitoring plays a vital role in identifying suspicious patterns,
including atypical fund transfers, deposits/withdrawals, and mingling of
personal and business funds, as well as structuring tactics to evade reporting
thresholds. Customer due diligence is essential during onboarding to establish
risk profiles, while ongoing monitoring ensures that these profiles remain
consistent with transactional activities. Inconsistencies and suspicious
identification documents serve as red flags. Entity resolution enables linking
interconnected accounts, entities, and individuals that may be intentionally
disguised, thereby exposing larger schemes and criminal networks.
Network analysis facilitates the mapping of connections between transacting
parties by exploring common accounts, addresses, and phone numbers, revealing
clusters and intermediaries indicative of underground banking operations. Fund
flow analysis traces the trajectory of funds over time through intricate
networks, shedding light on the funds’ ultimate source, destination, and
purpose, thereby unravelling layers of transactions designed to obfuscate
illicit activities. Behavioural analysis involves constructing models of
normal customer behaviours and establishing transaction benchmarks, enabling
the identification of significant deviations such as changes in transaction
locations or volumes that deviate from established patterns and triggering
alerts. Predictive modelling leverages financial and contextual data to
develop risk scores and prioritise alerts, empowering investigators to focus
on higher-risk potential leads. Finally, data aggregation from internal and
external sources provides valuable context concerning customers and
geopolitical risks, facilitating comprehensive risk assessments. By harnessing
the power of advanced analytics, AI, and extensive data, financial
institutions and law enforcement agencies enhance their ability to detect and
combat financial crimes, both well-known and innovative, perpetrated by
criminal organisations.
QML algorithms, such as QSVM and quantum Boltzmann machines, offer powerful
tools for analysing patterns in large databases to identify anomalies
indicative of crimes like fraud, embezzlement, and insider trading [24, 25].
These algorithms can process vast amounts of data and detect subtle deviations
more efficiently than classical methods by leveraging the computational
advantages of quantum computing. Additionally, quantum graph algorithms,
specifically quantum walks applied to networks of entities and transactions,
can potentially uncover hidden connections between perpetrators of complex
financial crimes at an accelerated pace [26]. It enables the detection of
intricate relationships and networks that may not be readily apparent using
conventional approaches [7]. Quantum clustering algorithms [27] provide an
automated means of grouping related financial crime activities involving
different entities and accounts across various locations and time frames. By
identifying common patterns and associations, these algorithms contribute a
more comprehensive understanding of criminal activities.
Quantum Order Finding (QOF) and period detection algorithms, such as Shor’s
algorithm, analyse periodic transaction behaviours, allowing for the
identification of activities such as Ponzi schemes and pump-and-dump
operations. It enables timely detection and intervention in fraudulent
practices. Quantum dimension reduction techniques, such as PCA and Singular
Value Decomposition (SVD), extract meaningful financial features that can be
used to detect outliers and flag suspicious activities for further
investigation. These techniques improve anomaly detection capabilities and
enhance the accuracy of crime identification. Quantum state and process
tomography enable rigorous testing of controls and the identification of
weaknesses that financial cybercriminals may exploit [28]. By conducting
simulations and analysing the behaviour of financial systems, these techniques
provide valuable insights for strengthening security measures.
Hybrid quantum-classical optimisation approaches can accelerate risk modelling
of large portfolios and databases, enabling the detection of abnormal flows
that may indicate financial crimes. By leveraging both quantum and classical
computing capabilities, these models improve the efficiency and accuracy of
risk assessment. Quantum generative models offer the ability to expand limited
financial crime datasets synthetically. By generating realistic data, these
models facilitate the robust training of classical and QML algorithms,
improving their performance in identifying and preventing financial crimes.
Furthermore, quantum analogue and digital simulation techniques aid in
modelling the dynamics of financial systems at scale. It provides a deeper
understanding of macro patterns exploited by organised crime syndicates,
aiding in developing more effective prevention and intervention strategies. In
summary, quantum algorithms’ speed, scale, and parallelism significantly
enhance the capabilities of detecting diverse financial crimes. By leveraging
quantum machine learning, graph algorithms, dimension reduction techniques,
order finding, state tomography, hybrid optimisation, generative models, and
simulation methods, financial institutions can strengthen their defences
against criminal activities and protect the integrity of financial systems.

##  4 Quantum Computing for Malicious Finance Detection: Benefits and
Evaluation Metrics

Quantum computing offers significant benefits for the detection of malicious
finance activities. First, quantum algorithms provide exponential speedups,
enabling near real-time analysis of huge datasets impractical for classical
computers [29]. Additionally, quantum computing efficiently handles datasets
and systems of a scale that exceeds the capabilities of classical approaches
[30]. Furthermore, quantum approaches can potentially expose complex
correlations and patterns hidden in classical methods, enhancing detection
accuracy [31]. The speed of quantum computing also enables interactive
exploration of edge cases and facilitates what-if analysis, offering insights
that are not feasible to obtain classically. Moreover, hybrid quantum-
classical tools may provide valuable insights into the origins and mechanics
of detected activities, contributing to their explainability.
Various metrics can be considered to evaluate the effectiveness of quantum-
based malicious finance detection properly. One important metric is the time
to detection, which quantifies the reduction in the time taken to identify
malicious activities using quantum analysis compared to classical methods.
Another metric is the detection rate, which measures the percentage of
simulated or historical attempts that quantum models successfully flag
compared to classical approaches. Striking a balance between detection
coverage and minimising unnecessary alerts is crucial, and this can be
assessed through the false positive rate metric. Additionally, evaluating the
impact of additional or refined data fields and features on detection
performance, known as feature sensitivity, helps optimise the detection
process. The robustness of the models against adversarial manipulation of data
or strategies aimed at avoiding detection is another important metric to
consider.
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 supporting informed decision-
making regarding the adoption of quantum solutions in this domain.

##  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 to ensure
consistency with customer risk profiles. 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 identities that may be intentionally
disguised. 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].
Quantum clustering algorithms are invaluable in automatically grouping related
money laundering activities based on transaction attributes, revealing
connections that may not be apparent through traditional means. Quantum
dimensionality reduction techniques, such as QPCA, extract meaningful features
from high-dimensional financial data. It facilitates the detection of
anomalies and the flagging of suspicious transactions for further
investigation. Furthermore, the quantum order finding algorithm can analyse
periodic or cyclical patterns in financial activities, allowing for the
identification of behaviour typical of specific money laundering techniques
like round-tripping or smurfing. Quantum state tomography, applied to the
output of quantum simulations of financial networks or processes, enables
stress testing of controls and the identification of vulnerabilities exploited
by money launderers.
Hybrid quantum-classical optimisation algorithms can potentially accelerate
risk modelling of large and complex portfolios, enabling the detection of
abnormal flows that may indicate money laundering activities. Additionally,
quantum sampling techniques can aid in generating realistic synthetic money
laundering datasets, augmenting the training of machine learning models and
improving their effectiveness. Indeed, the speed and scalability of quantum
algorithms significantly enhance money laundering detection capabilities. By
leveraging quantum machine learning, graph algorithms, dimensionality
reduction techniques, order finding algorithms, state tomography, hybrid
optimisation algorithms, and sampling methods, financial institutions can more
effectively combat money laundering and safeguard their systems against
illicit activities.

##  6 Enhancing Cryptocurrency Crime Detection with Quantum Computing

The detection and prevention of financial crimes employ advanced algorithms
and leveraging expanding financial crime databases to triangulate structural
and behavioural analyses. Screening transactions against watchlists of known
criminals, sanctioned entities, and politically exposed persons is a
significant measure to identify potentially corrupted interactions. Analysing
transaction metadata, including payee names, memo fields, and account
descriptors, provides valuable context for categorising transactions and
uncovering illicit schemes.
Geotagging techniques play a crucial role in identifying abnormal transaction
locations that may indicate fraudulent activity while monitoring cross-border
flows, which assists in detecting potential movements of illicit funds.
Structural analysis delves into examining relationships, communication
patterns, and fund movements within networks to expose hierarchies,
connections, and the division of labour within criminal organisations.
Link and cluster analysis identifies associations between accounts and
entities over time, drawing upon attributes such as overlapping addresses,
phone numbers, and IP addresses, exposing hidden relationships. Machine
learning-based anomaly and outlier detection utilise historical databases of
past criminal and non-criminal transactions and patterns to identify
statistical deviations that necessitate further investigation automatically. A
enhances and expedites these techniques by continuously learning from
investigators, adapting models, and recommending new monitoring patterns as
criminal methods evolve.Effective coordination and information sharing between
the public and private sectors are also essential in combating financial
criminal networks. This collaborative approach ensures the efficient exchange
of intelligence and maximises the collective effort to combat financial
crimes.
As mentioned above, QML algorithms, particularly QSVM, offer powerful tools
for analysing patterns in graphs of cryptocurrency transactions and addresses
[18]. These algorithms can swiftly flag suspicious entities and activities
indicative of theft, scamming, and money laundering. By leveraging the
computational advantages of quantum computing, these algorithms outperform
classical methods, enabling faster detection of illicit activities in the
crypto space [33].
Quantum period finding algorithms contribute to the identification of
repetitive patterns in cryptocurrency transfers, which often signify pump-and-
dump schemes or round-tripping activities associated with market manipulation
crimes. This capability assists in the timely detection and prevention of
fraudulent practices [34]. Quantum clustering algorithms excel in grouping
crypto transactions and entities involved in complex crimes that span multiple
accounts and currencies, overcoming the limitations of classical clustering
techniques. This comprehensive approach enhances understanding of
interconnected criminal networks within the cryptocurrency ecosystem. Quantum
signature detection techniques analyse various attributes of crypto
transactions, such as coin amounts and velocities, to identify anomalies and
outlier transactions associated with theft and fraud crimes [25]. By
leveraging quantum computing’s computational power, these algorithms can
efficiently detect suspicious activities that may go unnoticed by traditional
methods.
Quantum dimension reduction methods, such as PCA and SVD, extract meaningful
financial features from high-volume and complex cryptocurrency transaction
data. It enables the identification of outliers and flags transactions for
further investigation, enhancing anomaly detection capabilities.
Cryptocurrency network analysis, utilising quantum walk algorithms applied to
networks of entities and transactions, uncover hidden connections between
players involved in syndicated crypto crimes. By revealing intricate
relationships, these algorithms provide valuable insights for combating
organised criminal activities within the crypto space. Quantum simulation
techniques allow for modelling the dynamics of large cryptocurrency markets
and systems at a scale that classical computers cannot match. It provides a
deeper understanding of macro patterns exploited by organised criminal
networks, aiding in developing effective prevention and intervention
strategies.
Hybrid quantum-classical tools integrate the results obtained from quantum
analytics with traditional forensics, enabling improved detection of
cryptocurrency crimes. By combining the strengths of both paradigms, these
tools enhance the accuracy and efficiency of crime detection and investigation
in the crypto domain. In summary, the parallelism and scalability of quantum
computing significantly enhance the capabilities of detecting and combating
cryptocurrency crimes. Through the utilisation of quantum machine learning,
period finding, clustering, signature detection, dimension reduction, network
analysis, simulation, and hybrid quantum-classical tools, the cryptocurrency
ecosystem can be safeguarded against illicit activities, protecting investors
and maintaining the integrity of the market.

##  7 Enhancing Detection of Market Manipulation with Quantum Computing

Unravelling manipulative schemes that are often intentionally designed to
evade detection requires the implementation of sophisticated algorithms,
pattern-matching techniques, and entity resolution. Manipulative practices
such as identifying wash trades must be addressed, which involve fake
transactions to create misleading activity or liquidity in a stock without
changing beneficial ownership. Monitoring for spoofing is essential, which
entails entering large orders and cancelling them to artificially influence
price movements before executing trades. Additionally, detecting pump-and-dump
schemes is crucial, whereby coordinated buying artificially inflates the price
of a stock, followed by selling to dispose of shares on unsuspecting retail
investors.
Analysing the concentration of trades is necessary to identify instances where
one entity dominates the trading volume in a stock, particularly around news
announcements or filing periods. Comparing trade volumes to the public float
is important, as abnormally high volumes could indicate the trading of fake
shares. Benchmarking price moves against news events is also crucial, as
drastic price changes without corresponding news raise concerns. The
behavioural analysis of traders plays a significant role in identifying
coordinated patterns among entities and using the same brokerages or accounts.
Assessing order-to-trade ratios is essential, as high proportions of cancelled
orders compared to completed trades could signal spoofing. Network analysis of
trading relationships helps identify dense clusters of entities repeatedly
engaging in trades with each other. Machine learning techniques can be
employed, using past cases to train models to detect newer manipulation
techniques autonomously. Ongoing monitoring and the adaptability of models are
vital to keep pace with the evolving tactics employed by manipulators. By
leveraging these sophisticated tools and techniques, it becomes possible to
detect and mitigate manipulative activities in financial markets effectively.
We have already mentioned that QML algorithms, such as QSVM, offer powerful
tools for analysing patterns in large datasets of stock trades, orders, and
prices [35, 24]. By leveraging these algorithms, market manipulation
strategies like pump-and-dump and spoofing can be automatically identified
faster. The computational advantages of quantum computing enable efficient
processing of vast amounts of data, improving the detection of unfair
practices in the stock market. Quantum graph analysis techniques, particularly
quantum walks applied to trading entities and transaction networks, can
uncover complex insider trading rings and hidden coordination between actors.
By examining the relationships and interactions within the market, these
algorithms provide valuable insights into illicit activities that may not be
easily detectable using traditional methods [36].
Quantum dimension reduction methods, such as Quantum Principal component
analysis (QPCA) and Qunatum SVD (QSVD), extract meaningful features from high-
dimensional market data. It enables the identification of anomalous trades,
prices, or volatility patterns that may indicate market manipulation. Through
quantum computing’s computational power, these techniques enhance the accuracy
of identifying suspicious activities.
Quantum signal processing techniques focus on detecting engineered price or
volume signals that could indicate front-running or insider trading
activities. By analysing stock market signals in the quantum domain, these
algorithms can identify patterns that may not be apparent using classical
methods, contributing to detecting unfair practices in real-time. Quantum
order-finding algorithms analyse repetitive or cyclical trading periodicities,
which may wash trading or other strategies designed to influence markets
artificially. By leveraging quantum computing’s capabilities, these algorithms
provide insights into trading patterns that can assist in exposing market
manipulation techniques.
Quantum portfolio optimisation techniques allow stress tests on large and
complex portfolio holdings using quantum solvers. It enables verifying
compliance with regulations, particularly regarding conflicts of interest,
enhancing transparency and accountability in the financial industry. Quantum
simulation techniques enable the modelling of stock exchange dynamics and the
co-movement of related firms’ prices at scale. By gaining insights into
potential market manipulation techniques, these simulations contribute to a
deeper understanding of unfair practices in financial markets.

##  8 Quantum Circuit Born Machines in Finance: Enhancing Generative Modeling
and Quantitative Applications

A Quantum Circuit Born Machine (QCBM) is a QML model based on Born machines
and offers several key advantages [37]. Born machines are variational hybrid
quantum-classical models that leverage Born’s rule from quantum mechanics to
evaluate classical data distributions. In the case of QCBMs, the Born rule
distribution is implemented using a parameterised quantum circuit, enabling
the model to harness the power of quantum hardware [38].
The quantum circuit encodes a quantum state representing the data
distribution, and measurements in this state provide samples from the
distribution. The circuit parameters are optimised variationally using a
classical optimiser to minimise the difference between the generated and
target distributions. This approach offers a quantum-enhanced generative
modelling capability that can efficiently learn certain distributions compared
to classical methods. QCBMs have demonstrated their potential in various
tasks, including density estimation, molecular simulations, graph neural
networks, and generative adversarial networks. One of the advantages of QCBMs
is that they have relatively weaker resource requirements than other QML
models, making them implementable on near-term quantum devices. However, it is
important to note that their practical implementation is still constrained by
the size of circuits that can be realised.
In finance, QCBMs hold promise for various applications [39]. For instance, in
option pricing and hedging, QCBMs can model complex underlying asset
distributions more accurately than classical methods, enabling the pricing of
exotic options and effective risk management of large option portfolios.
QCBMs can also aid in portfolio optimisation by capturing the intricate
covariances between assets and providing recommendations for optimal asset
allocations. Risk modelling becomes more refined with QCBMs as they can
simulate extreme but plausible scenarios from the learned distribution,
assisting in estimating portfolio Value at Risk and Expected Shortfall.
Furthermore, QCBMs can be utilised for anomaly detection in finance, helping
to identify outliers and anomalies in time series data, such as stock prices,
by learning normal distributions and flagging deviations. In fraud detection,
QCBMs can model complex patterns in large financial transaction graphs,
enabling the identification of traces of money laundering, spoofing, and pump-
and-dump schemes. Macroeconomic forecasting can benefit from QCBMs as they can
capture complex non-linear relationships in economic indicators, facilitating
predictions of recessionary environments and commodity prices. Stress testing
of banks’ resilience to market crashes and liquidity crises can be enhanced
through QCBMs by simulating distributions of risk factor shocks.
In trading strategies, QCBMs can aid in developing and evaluating strategies
like market making by modelling order books and limiting order placements.
Additionally, QCBMs can be used for synthetic data generation, producing
realistic training datasets by sampling from QCBM distributions to augment
classical models. The unique modelling abilities offered by QCBMs have the
potential to improve various quantitative finance applications significantly.
By leveraging the power of quantum computing, QCBMs can provide more accurate
and efficient solutions for pricing, risk management, anomaly detection, fraud
detection, forecasting, stress testing, trading strategies, and synthetic data
generation in the financial industry.

##  9 Advancing Risk Management in Finance: Quantum Computing for Enhanced
Analysis

Quantum computing holds immense potential for revolutionising risk management
in the financial industry. By leveraging the speed, scale, and parallelism
inherent in quantum systems, various applications can be developed to enhance
risk analysis and decision-making. Here are some key areas quantum computing
can significantly impact [40, 41].
Quantum portfolio optimisation allows using quantum optimisation techniques,
such as VQE [42], to efficiently determine optimal asset allocations for large
and complex portfolios while considering risk constraints. This approach has
the potential to outperform classical methods and deliver superior portfolio
performance. Quantum scenario analysis introduces quantum simulations to
rigorously assess risks under extreme market conditions that are challenging
to model using classical approaches. By leveraging the computational power of
quantum systems, financial institutions can gain deeper insights into the
potential impact of rare and catastrophic events on their portfolios.
Quantum Value-at-Risk (VaR) estimation involves leveraging QML algorithms,
like QSVMs, to estimate VaR and Expected Shortfall (ES) risk measures using
vast historical market data. Quantum algorithms can process and analyse this
data more efficiently, enabling more accurate and timely risk assessments.
Quantum risk factor modelling employs QPCA techniques to extract risk factors
from high-dimensional market time series data. Financial institutions can
improve risk modelling and management strategies by identifying the underlying
factors driving portfolio risk.
Quantum correlation analysis offers the ability to analyse the joint
interactions of multiple risk factors exponentially faster through quantum
algorithms. This enhanced capability enables a more comprehensive and accurate
portfolio risk modelling, particularly in complex and interconnected markets.
Quantum counterparty risk analysis involves applying quantum graph algorithms
to map connections between financial entities, identifying potential systemic
risks and vulnerabilities within the financial system.
By leveraging quantum computing’s ability to handle and analyse large-scale
networks, financial institutions and regulators can gain deeper insights into
counterparty risk and systemic stability. Quantum stress testing utilises
hybrid quantum-classical optimisation approaches to test the resilience of
risk models under diverse assumption shocks at a large scale. By combining the
computational power of quantum systems with classical optimisation techniques,
financial institutions can assess the robustness of their risk management
frameworks and identify areas for improvement.
Quantum anomaly detection leverages QML techniques to flag outlier market
moves within massive datasets. By harnessing the power of quantum systems,
financial institutions can enhance their risk investigation capabilities and
promptly address potential anomalies and irregularities. Overall, the speed,
scale, and parallelism offered by quantum computing have the potential to
advance risk management capabilities for financial institutions and regulators
significantly. By embracing quantum technologies and developing innovative
applications, the financial industry can enhance risk analysis, improve
decision-making processes, and strengthen overall financial stability.

##  10 Comparing Quantum Algorithms and Classical Approaches: Key Findings

In comparing classical and quantum computing approaches for detecting
financial crimes, the distinctions largely revolve around speed, data
processing, and pattern detection accuracy. Classical computing, the
foundation for fighting financial crime, processes information in binary,
limiting its speed and efficiency as data volumes grow. As can be seen in
Figure 1, quantum computing, leveraging principles like superposition and
entanglement, promises significant improvements in these areas but remains in
the early development stages for practical application.

Figure 1: Quantum Context: Advantages, Opportunities, and Challenges in
Finance.

  * -

Processing Speed: Classical methods are bound by algorithms’ linear or
polynomial time complexities, which can slow searching through massive
datasets. Using algorithms like Grover’s, Quantum computing could dramatically
reduce search times, offering a quadratic speedup. Indeed, quantum computers
could potentially complete tasks in hours or days that would take classical
computers much longer.

  * -

Data Processing Capabilities: Classical computing has advanced with big data
technologies and ML, enabling the processing of large datasets to identify
fraud patterns. However, the increasing volume and complexity of data present
challenges. Using techniques like QPCA, Quantum computing could theoretically
manage high-dimensional data more efficiently, taking advantage of quantum
computing’s ability to operate in high-dimensional spaces.

  * -

Accuracy in Detecting Fraud Patterns: The accuracy of classical AI and ML
models in identifying fraud can be affected by the complexity of financial
systems. Quantum computing’s approach to processing multiple possibilities
simultaneously could theoretically improve model accuracy by uncovering subtle
correlations missed by classical methods. However, the practical benefits of
quantum computing in enhancing detection accuracy are yet to be fully
realised. The transition from classical to quantum computing in financial
crime detection is expected to bring significant advancements but is
contingent on overcoming technological challenges and developing new
algorithms and security measures. The potential for quantum computing to
transform the field is clear, yet much work remains to harness these
capabilities fully.

Based on the comparison between classical and quantum computing in detecting
financial crimes, several findings emerge, leading to recommendations for
future research, development, and policy formulation:

  1. a)

Technological Maturity, given that quantum computing is nascent, especially in
practical applications for financial crime detection. While promising, the
technology requires further development to realise its full potential;

  2. b)

Quantum Advantage: The theoretical advantages of quantum computing—such as
processing speed and the ability to handle complex, high-dimensional data—are
clear. However, practical demonstrations of these advantages in real-world
financial crime detection scenarios are limited;

  3. c)

Integration Challenges: Integrating quantum computing into financial crime
detection frameworks poses significant challenges, including the need for new
algorithms, data security measures, and possibly regulatory changes;

  4. d)

Resource Intensity: Quantum computing currently requires substantial
resources, including specialised knowledge and quantum computing hardware,
which are not widely accessible.

The above analyses provided some useful recommendations:

  1. a)

Investment in Research and Development: Governments, academia, and the private
sector should invest in R&D to advance quantum computing technology, focusing
on developing practical applications for financial crime detection;

  2. b)

Develop Quantum-Ready Workforce: Educational institutions and industry players
should work together to develop training programs that prepare a new
generation of professionals skilled in quantum computing and its applications
in finance;

  3. c)

Pilot Projects: In collaboration with quantum computing firms, financial
institutions should initiate pilot projects to explore the application of
quantum computing in detecting financial crimes. These projects can provide
valuable insights into the technology’s practical benefits and limitations;

  4. d)

Quantum-Secure Cryptography: With the advancement of quantum computing,
existing encryption methods could become vulnerable. Developing quantum-secure
cryptography is essential to protect financial data against potential quantum
computing threats;

  5. e)

Regulatory Frameworks: Regulatory bodies should begin considering the
implications of quantum computing on financial systems. Developing guidelines
and standards that address the use of quantum computing in finance will be
crucial to ensuring security, privacy, and fair use;

  6. f)

Cross-Sector Collaboration: Encourage collaboration across financial
institutions, technology companies, regulatory bodies, and academic
researchers to share knowledge, resources, and best practices in applying
quantum computing;

  7. g)

Monitor Developments in Quantum Computing: Financial institutions should
continuously monitor advancements in quantum computing to stay ahead of
potential threats and opportunities. It includes keeping abreast of
developments in quantum algorithms that could revolutionise financial crime
detection.

##  11 Conclusions

The development of quantum computing introduces significant advancements in
detecting and countering financial crimes. This technology, leveraging the
unique capabilities of quantum mechanics, offers a new set of tools for
analysing data and identifying illicit activities within vast datasets, doing
so at a pace and level of complexity that classical computing can not match.
The examples of quantum algorithms explored in this article, from QSVM for
anomaly detection to quantum clustering for identifying hidden networks
involved in money laundering, showcase the potential applications across
various financial crime categories. Quantum computing’s edge lies in its
ability to process information through mechanisms such as superposition and
entanglement, providing a leap in the speed and efficiency of data analysis.
This ability is critical in the financial sector, where detecting fraudulent
patterns swiftly can significantly disrupt criminal operations. By integrating
quantum technologies with existing frameworks and strategies for financial
crime prevention, there is a clear path towards enhancing the effectiveness of
these measures. The alignment of financial crime classifications with
recognised standards from global entities, such as the FATF and the UNODC,
emphasises the need for a unified strategy incorporating quantum computing.
This approach improves existing methods and introduces new capabilities for
uncovering and combating financial crimes. The financial sector faces an
opportunity to strengthen defences against criminal activities. It includes
investment in research, development, and collaborative efforts to explore and
apply quantum technologies. The adoption of quantum computing in financial
crime detection signals a move towards more secure, transparent, and resilient
financial systems equipped to counter the challenges posed by sophisticated
criminal schemes. This article encourages the financial community to seriously
consider the potential of quantum computing. We can expect a significant
enhancement in the mechanisms for preventing and detecting financial crimes
safeguarding the financial system against emerging threats by adopting this
technology.

## References

  * [1] Darío Gil.  Institute for Business Value (2023) The Quantum Decade: A Playbook for Achieving Awareness, Readiness, and Advantage, 4th edn. IBM.  https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/quantum-decade, 2023.  [Online; accessed 22-March-2024].
  * [2] Stefano Mangini, Francesco Tacchino, Dario Gerace, Daniele Bajoni, and Chiara Macchiavello.  Quantum computing models for artificial neural networks.  Europhysics Letters, 134(1):10002, 2021.
  * [3] Sri Amit Ray.  Quantum Machine Learning with Quantum Cheshire Cat Generative AI Model: Quantum Mirage Data.  Compassionate AI Lab, 2024.
  * [4] Vedran Dunjko and Hans J Briegel.  Machine learning & artificial intelligence in the quantum domain: a review of recent progress.  Reports on Progress in Physics, 81(7):074001, 2018.
  * [5] Sukhpal Singh Gill, Minxian Xu, Carlo Ottaviani, Panos Patros, Rami Bahsoon, Arash Shaghaghi, Muhammed Golec, Vlado Stankovski, Huaming Wu, Ajith Abraham, et al.  Ai for next generation computing: Emerging trends and future directions.  Internet of Things, 19:100514, 2022.
  * [6] Lidong Wang and Cheryl Ann Alexander.  Quantum science and quantum technology: Progress and challenges.  Am. J. Electr. Electron. Eng., 8(2):43–50, 2020.
  * [7] Nouhaila Innan, Muhammad Al-Zafar Khan, and Mohamed Bennai.  Financial fraud detection: a comparative study of quantum machine learning models.  International Journal of Quantum Information, page 2350044, 2023.
  * [8] Raffaele Guarasci, Giuseppe De Pietro, and Massimo Esposito.  Quantum natural language processing: Challenges and opportunities.  Applied Sciences, 12(11):5651, 2022.
  * [9] Smita Roy Trivedi, Dipali Krishnakumar, and Richa Verma Bajaj.  Loan frauds and bad boy billionaires: A new approach of loan fraud prevention using natural language processing (nlp).  NIBM Working Paper Series, 2021.
  * [10] Murphy Yuezhen Niu, Sergio Boixo, Vadim N Smelyanskiy, and Hartmut Neven.  Universal quantum control through deep reinforcement learning.  npj Quantum Information, 5(1):33, 2019.
  * [11] Nico Meyer, Christian Ufrecht, Maniraman Periyasamy, Daniel D Scherer, Axel Plinge, and Christopher Mutschler.  A survey on quantum reinforcement learning.  arXiv preprint arXiv:2211.03464, 2022.
  * [12] Iordanis Kerenidis and Anupam Prakash.  Quantum recommendation systems.  arXiv preprint arXiv:1603.08675, 2016.
  * [13] Iris Cong and Luming Duan.  Quantum discriminant analysis for dimensionality reduction and classification.  New Journal of Physics, 18(7):073011, 2016.
  * [14] Esma Aïmeur, Gilles Brassard, and Sébastien Gambs.  Quantum clustering algorithms.  In Proceedings of the 24th international conference on machine learning, pages 1–8, 2007.
  * [15] Andrew J Daley, Immanuel Bloch, Christian Kokail, Stuart Flannigan, Natalie Pearson, Matthias Troyer, and Peter Zoller.  Practical quantum advantage in quantum simulation.  Nature, 607(7920):667–676, 2022.
  * [16] Gennaro De Luca.  A survey of nisq era hybrid quantum-classical machine learning research.  Journal of Artificial Intelligence and Technology, 2(1):9–15, 2022.
  * [17] Juraj Nosál.  Crime in the digital age: A new frontier.  In The Implications of Emerging Technologies in the Euro-Atlantic Space: Views from the Younger Generation Leaders Network, pages 177–193. Springer, 2023.
  * [18] Rosario Girasa and Gino J Scalabrini.  Regulation of Innovative Technologies: Blockchain, Artificial Intelligence and Quantum Computing.  Springer Nature, 2022.
  * [19] Andrew M Childs and Jason M Eisenberg.  Quantum algorithms for subset finding.  arXiv preprint quant-ph/0311038, 2003.
  * [20] Angad Kalra, Faisal Qureshi, and Michael Tisi.  Portfolio asset identification using graph algorithms on a quantum annealer.  Available at SSRN 3333537, 2018.
  * [21] Alexander M Dalzell, Sam McArdle, Mario Berta, Przemyslaw Bienias, Chi-Fang Chen, András Gilyén, Connor T Hann, Michael J Kastoryano, Emil T Khabiboulline, Aleksander Kubica, et al.  Quantum algorithms: A survey of applications and end-to-end complexities.  arXiv preprint arXiv:2310.03011, 2023.
  * [22] Michele Grossi, Noelle Ibrahim, Voica Radescu, Robert Loredo, Kirsten Voigt, Constantin Von Altrock, and Andreas Rudnik.  Mixed quantum–classical method for fraud detection with quantum feature selection.  IEEE Transactions on Quantum Engineering, 3:1–12, 2022.
  * [23] Abiodun Esther Omolara, Aman Jantan, Oludare Isaac Abiodun, Manmeet Mahinderjit Singh, Mohammed Anbar, and DV Kemi.  State-of-the-art in big data application techniques to financial crime: a survey.  International Journal of Computer Science and Network Security, 18(7):6–16, 2018.
  * [24] Daniel J Egger, Claudio Gambella, Jakub Marecek, Scott McFaddin, Martin Mevissen, Rudy Raymond, Andrea Simonetto, Stefan Woerner, and Elena Yndurain.  Quantum computing for finance: State-of-the-art and future prospects.  IEEE Transactions on Quantum Engineering, 1:1–24, 2020.
  * [25] Mingchao Guo, Hailing Liu, Yongmei Li, Wenmin Li, Fei Gao, Sujuan Qin, and Qiaoyan Wen.  Quantum algorithms for anomaly detection using amplitude estimation.  Physica A: Statistical Mechanics and its Applications, 604:127936, 2022.
  * [26] Karuna Kadian, Sunita Garhwal, and Ajay Kumar.  Quantum walk and its application domains: A systematic review.  Computer Science Review, 41:100419, 2021.
  * [27] Somayeh Bakhtiari Ramezani, Alexander Sommers, Harish Kumar Manchukonda, Shahram Rahimi, and Amin Amirlatifi.  Machine learning algorithms in quantum computing: A survey.  In 2020 International joint conference on neural networks (IJCNN), pages 1–8. IEEE, 2020.
  * [28] Gregory AL White, Felix A Pollock, Lloyd CL Hollenberg, Kavan Modi, and Charles D Hill.  Non-markovian quantum process tomography.  PRX Quantum, 3(2):020344, 2022.
  * [29] Abha Naik, Esra Yeniaras, Gerhard Hellstern, Grishma Prasad, and Sanjay Kumar Lalta Prasad Vishwakarma.  From portfolio optimization to quantum blockchain and security: A systematic review of quantum computing in finance.  arXiv preprint arXiv:2307.01155, 2023.
  * [30] Maryann Njorbuenwu, Bobby Swar, and Pavol Zavarsky.  A survey on the impacts of quantum computers on information security.  In 2019 2nd International conference on data intelligence and security (ICDIS), pages 212–218. IEEE, 2019.
  * [31] Bilgehan Arslan, Mehtap Ulker, Sedat Akleylek, and Seref Sagiroglu.  A study on the use of quantum computers, risk assessment and security problems.  In 2018 6th International Symposium on Digital Forensic and Security (ISDFS), pages 1–6. IEEE, 2018.
  * [32] Soumya Ranjan Mishra, Hitesh Mohapatra, et al.  Enhancing money laundering detection through machine learning: A comparative study of algorithms and feature selection techniques.  In AI and Blockchain Applications in Industrial Robotics, pages 300–321. IGI Global, 2024.
  * [33] Apoorva Ganapathy.  Quantum computing in high frequency trading and fraud detection.  Engineering International, 9(2):61–72, 2021.
  * [34] Jorão Gomes, Sajjad Khan, and Davor Svetinovic.  Fortifying the blockchain: A systematic review and classification of post-quantum consensus solutions for enhanced security and resilience.  IEEE Access, 2023.
  * [35] Anshul Saxena, Javier Mancilla, Iraitz Montalban, and Christophe Pere.  Financial Modeling Using Quantum Computing: Design and manage quantum machine learning solutions for financial analysis and decision making.  Packt Publishing Ltd, 2023.
  * [36] Mritunjay Shall Peelam, Anjaney Asreet Rout, and Vinay Chamola.  Quantum computing applications for internet of things.  IET Quantum Communication, 2023.
  * [37] Javier Alcazar, Vicente Leyton-Ortega, and Alejandro Perdomo-Ortiz.  Classical versus quantum models in machine learning: insights from a finance application.  Machine Learning: Science and Technology, 1(3):035003, 2020.
  * [38] Santanu Ganguly.  Implementing quantum generative adversarial network (qgan) and qcbm in finance.  arXiv preprint arXiv:2308.08448, 2023.
  * [39] Brian Coyle, Maxwell Henderson, Justin Chan Jin Le, Niraj Kumar, Marco Paini, and Elham Kashefi.  Quantum versus classical generative modelling in finance.  Quantum Science and Technology, 6(2):024013, 2021.
  * [40] Meng-Leong How and Sin-Mei Cheah.  Business renaissance: Opportunities and challenges at the dawn of the quantum computing era.  Businesses, 3(4):585–605, 2023.
  * [41] Stefan Woerner and Daniel J Egger.  Quantum risk analysis.  npj Quantum Information, 5(1):15, 2019.
  * [42] Jules Tilly, Hongxiang Chen, Shuxiang Cao, Dario Picozzi, Kanav Setia, Ying Li, Edward Grant, Leonard Wossnig, Ivan Rungger, George H Booth, et al.  The variational quantum eigensolver: a review of methods and best practices.  Physics Reports, 986:1–128, 2022.