Fraud losses climbed to $16.6B in 2024 (+33% YoY), while false positives consume around 19% of fraud costs versus 7% for genuine losses. Classical Machine Learning is straining under high-velocity payments, cross-border complexity, and privacy constraints
(GDPR/PSD2).
The article argues for hybrid quantum–classical approaches as the next performance lever in quantum fraud detection in banking. By exploiting qubits (superposition and entanglement), Quantum Machine Learning sharpens sensitivity to faint, non-linear
signals in time-series and transaction graphs, with pilots reporting faster training and very high classification performance. Leading banks are already experimenting, HSBC with Quantinuum and Crédit Agricole with Pasqal.
Operationally, quantum optimization reframes alert triage as a combinatorial problem, lifting investigator productivity by surfacing the most suspicious cases first and reducing false alarms. A pragmatic path starts with one pain point, runs a hybrid
pilot via cloud access, tracks hard KPIs, and builds explainable governance for scale.
Why is the hybrid quantum + AI approach becoming relevant for financial fraud detection?
Financial fraud losses exploded to $16.6 billion in 2024: a 33% jump in just one year (Federal Bureau of Investigation, 2024). With 79% of organizations hit by fraud attacks, traditional detection systems are buckling under pressure (Association for Financial
Professionals, 2025). The surge is driven by instant payments, sophisticated fraud rings, and cross-border e-commerce schemes that evolve faster than rule-based systems can adapt, raising the strategic question of how quantum computing can improve
fraud detection for banks facing high-velocity risk.
The false positive crisis compounds the problem. According to a report by JPMorgan (2023), false positives account for about 19% of total fraud-related costs, while genuine fraud losses represent only 7%. Every wrongly flagged transaction means wasted
investigation time, operational bottlenecks, and frustrated customers. For a mid-sized bank processing millions of transactions daily, this means valuable analyst hours spent reviewing low-risk alerts, while genuine fraud cases risk being delayed
or overlooked.
In response, institutions are exploring quantum fraud detection in banking and quantum security practices to improve precision at scale. Traditional machine learning has made progress, but it is hitting structural limits, struggling with real-time, high-dimensional
data and creating governance challenges under GDPR and PSD2 when models depend on centralized data aggregation.
Fraud teams know the pattern: volumes spike, attackers pivot, models lag and investigators drown in alerts. Upgrading rules and retraining models helps, but the cycle repeats, prompting leaders to ask how quantum computing improves fraud detection without
breaking privacy rules.
Enters quantum computing. It uses quantum bits (qubits) that can represent multiple states at once and become correlated (quantum entanglement). Running on a quantum processor (quantum chip), certain algorithms can optimize and detect patterns more efficiently
than classical machines. Used alongside classical AI, this approach to quantum fraud detection in banking could help banks adapt scenarios faster, cut false positives with smarter triage, and enable compliant cross-border collaboration under GDPR/PSD2.
Advancing quantum security without breaking privacy rules.
For a practitioner view on ecosystem readiness, see our conversation preparing for the quantum era with Alice & Bob and Sopra Steria.
Three Concrete Solutions to Today's Fraud Challenges
1. Better Detection with Quantum Machine Learning
Quantum algorithms excel at spotting subtle patterns in complex datasets, including time-series anomalies, graph structures in transaction networks, and highly imbalanced fraud signals buried in billions of legitimate transactions. Hybrid classical-quantum
models have already demonstrated faster training times and higher accuracy in early pilots.
Leading institutions are already moving ahead. HSBC partnered with Quantinuum in 2023 to deploy quantum computing for fraud detection and cybersecurity. Crédit Agricole launched "Project Feynman" with French startup Pasqal, using quantum models
for predictive risk analysis. In recent research experiments, quantum machine learning models achieved very high classification performance (an F1-score of 0.98 for both fraud and non-fraud cases) in pilot settings, a promising result given the cost
of undetected fraud. (Innan, Al-Zafar Khan, & Bennai, 2023.)
2. Smarter Alert Prioritization Through Quantum Optimization
Flagging anomalies is only half the battle. The real challenge is deciding which of thousands of daily alerts to investigate first: a combinatorial optimization problem that scales exponentially with complexity. Quantum optimization within a hybrid quantum
fraud detection in banking stack can efficiently tackle these prioritization puzzles, helping institutions focus investigation resources where they add the most value.
This directly addresses the false positive crisis: instead of investigating alerts sequentially or through crude scoring, quantum-enhanced systems can weigh multiple risk factors simultaneously (transaction history, network relationships, timing patterns,
merchant profiles) to surface the genuinely suspicious cases. Coupled with quantum machine learning and aligned with quantum security objectives, the result is fewer wasted hours on false alarms and faster response to real threats.
Beyond accuracy, a key benefit is refresh agility. By accelerating certain optimization and anomaly-detection subroutines on a quantum processor, hybrid quantum approaches can reduce compute time for retraining and re-prioritization loops. In practice,
this can enable more frequent scenario updates (e.g., thresholds, graph partitions, alert triage) subject to model governance and integration constraints, helping keep precision high as tactics evolve.
A Pragmatic Roadmap for Financial Institutions
Quantum technologies are shifting from theory to practice. Our recommendation is to adopt a phased approach.
1. Identify one pain point, and frame it as a quantum problem
Start small and specific. Focus on a high-impact fraud use case where classical models are reaching their limits, for example:
- Instant payments (high data velocity, tight response windows)
- Card-not-present transactions (imbalanced datasets and pattern drift)
- Cross-border transfers (complex network topologies and data-sharing restrictions)
Each of these domains maps well to known quantum strengths: quantum optimization, graph analytics, and high-dimensional pattern recognition. Framing the effort as quantum fraud detection in banking clarifies scope and success metrics. Selecting the right
use case ensures early results are tangible and defensible to business lines and regulators, while staying compatible with GDPR/PSD2 and broader quantum security goals.
2. Build a hybrid pilot
Work with your existing data pipelines and fraud analytics tools. The objective is to integrate quantum subroutines for anomaly detection, model retraining, or alert prioritization into your current workflows, showing concretely how quantum computing
improves fraud detection.
- Use hybrid cloud access via providers like Quandela to run quantum-classical experiments.
- Measure improvement using clear KPIs: reduction in false positives, time to detect, or analyst productivity.
- Keep the pilot explainable: regulators will want transparency on how alerts are generated and validated.
This approach de-risks the investment: you gain measurable insights without disrupting production systems.
3. Build the skills and governance now
Quantum experimentation works best when embedded in a clear governance model. Establish a working group to define how quantum machine learning and quantum optimization results will be validated within your model risk framework. Then, document lessons
learned and create internal standards for future scaling.
4. Move from pilot to production-ready insights
Once a pilot demonstrates added value, the next step is industrialization. The key is incremental adoption: use quantum where it clearly outperforms classical methods and continuously benchmark the ROI and prove how quantum computing improves fraud detection
with stable KPIs.
Quantum in fraud detection isn’t a distant vision: it’s an R&D-driven performance lever available today. Banks that start small, measure fast, and integrate pragmatically will be ready when quantum advantage becomes mainstream. The opportunity
isn’t to predict the future, it’s to prototype it, now.
Conclusion: The Window Is Now
Financial fraud is growing faster than classical detection systems, such as classical AI, can adapt, which is exactly how quantum computing improves fraud detection in practice. Early movers that adopt quantum fraud detection in banking will reduce mounting
losses and curb escalating false positive costs by pairing quantum machine learning and quantum optimization with existing pipelines.
The path forward is clear: start with targeted pilots, build internal expertise, and engage with the emerging quantum ecosystem. The technology is ready for real-world experimentation. HSBC, Crédit Agricole, and others are already proving that
quantum can deliver measurable improvements in fraud detection accuracy and operational efficiency.
The question isn't whether quantum will transform fraud detection: it's whether your institution will lead that transformation or follow. The window to shape standards, build capabilities, and capture first-mover advantages is open now. In parallel with
detection, we are strengthening the data protection layer with hybrid post-quantum cryptography.
Start today.
FAQ
What is quantum fraud detection in banking, and how does it differ from classical Machine Learning?
It is a hybrid approach that combines Quantum Machine Learning and quantum optimization with classical ML to spot faint, non-linear patterns in time-series and transaction graphs. Unlike classical ML that struggles with high-velocity, high-dimensional
data and GDPR/PSD2 constraints, the quantum–classical stack is designed to improve sensitivity and efficiency.
Which technology can be used to detect fraud in Financial Services?
Hybrid quantum fraud detection in banking using Quantum Machine Learning for pattern recognition and quantum optimization for alert triage, integrated into existing analytics pipelines.
How does quantum optimization prioritize alerts to cut false positives and speed investigations?
It reframes triage as a combinatorial optimization problem, evaluating multiple risk factors at once to surface the most suspicious cases first, reducing false alarms and investigator workload.
How do we run a hybrid quantum–classical pilot via cloud access with explainable governance suitable for regulators?
Use cloud access (e.g., Quandela) to plug quantum subroutines into current workflows, track hard KPIs (false-positive reduction, time-to-detect, analyst productivity), and document model risk, explainability, and GDPR/PSD2 alignment for scalable governance.