For many fraud teams it can feel like they’re fighting a losing battle. Fraud losses continue to rise, with the FBI’s 2025 Internet Cyber Crime Report revealing cyber-enabled crimes defrauded Americans of nearly $21 billion.
This is compounded by estimates that false positives account for about 19% of total fraud-related costs, for instance, the loss of revenue from what were actually legitimate transactions, or the resources needed to meet increased customer service demands. In comparison, genuine fraud losses represent only 7%.
Therefore, in many instances, organisations are spending more money blocking legitimate customers than they are losing to actual criminals, highlighting the need to rebalance fraud‑detection strategies toward greater accuracy and better customer experience.
Mounting fraud demands are stretching teams beyond capacity
In recent years, fraud prevention teams have leaned heavily on machine‑learning techniques, such as advanced behavioural modelling and real‑time risk scoring, to drive down false positives. But as payment volumes surge, cross‑border transactions grow more complex, and regulatory pressures under GDPR and PSD2 intensify, these systems are being pushed to their limits.
This strain doesn’t stay contained within fraud operations, in fact it reverberates across an entire organisation. For example, analysts lose hours to low‑value investigations, fraud teams are overwhelmed by avoidable alerts, and customer support teams must deal with frustrated customers wrongly flagged as suspicious.
For a mid‑sized bank processing millions of transactions each day, these pressures compound quickly. Outdated or overstretched fraud‑detection models can generate a flood of low‑risk alerts, consuming hundreds of analyst hours that add no real value. Meanwhile, the cases that do matter – genuine fraud attempts where rapid intervention is critical -risk being delayed or missed entirely. The result is a defensive posture that’s not only inefficient but is increasingly misaligned with customer expectations and the evolving complexity of modern fraud.
Quantum’s potential in fighting financial fraud
One emerging answer to help fraud teams and other departments across a business is quantum computing. From a top-level view, quantum is a type of computing that can analyse far more possibilities at once than traditional computers ever could, making it especially powerful for spotting complex patterns, rare events, or subtle anomalies.
In fraud detection, this means being able to process immense volumes of transaction data and surface suspicious behaviour that today’s systems might overlook or take too long to identify. It ultimately helps fraud teams to detect new behaviours and reduce false positives.
While quantum has a wide range of applications in fraud detection, two examples stand out in helping illustrate how the technology works in practice. The first is better detection. In the case of a highly coordinated card fraud, which slips past standard machine‑learning models because the signals are too faint, a quantum‑enhanced system ingests billions of transactions and identifies an unusual pattern. With this insight, the fraud team is immediately alerted to the coordinated activity, enabling them to freeze affected cards, flag connected merchants, and prevent escalation into a large‑scale fraud event.
The second use case is smarter alert prioritisation. In this instance, a retail bank’s hybrid quantum system is rapidly analysing thousands of alerts across multiple risk signals to identify which ones genuinely warrant human review. A seemingly low‑risk alert is then elevated after quantum analysis reveals it sits inside a hidden high‑risk merchant cluster. By directing the fraud team or investigators to alerts with the highest fraud likelihood, the bank cuts false‑positive workload and speeds up real fraud intervention.
A pragmatic quantum roadmap
Integrating quantum capabilities into existing AI and fraud‑detection systems requires a phased, tactical approach to unlock their full value. To do this effectively, organisations should follow four core components that guide how quantum is introduced seamlessly within their fraud‑detection stack.
The first step is identifying a specific pain point and reframing it as a quantum‑ready problem. This works best when selecting a high‑impact fraud scenario where classical models are hitting performance limits. Examples include the extreme data velocity of instant payments or the data‑sharing constraints of cross‑border transfers. These areas map naturally to quantum strengths such as optimisation, graph analytics and high‑dimensional pattern recognition. Clearly framing the effort as quantum‑enabled fraud detection in banking helps define both scope and success measures.
The second step is to build a small hybrid pilot that plugs into the bank’s existing data pipelines and fraud‑detection tools. The goal is to show, in a controlled way, how quantum components can enhance tasks like spotting anomalies, refreshing models, or prioritising alerts. This typically involves running quantum–classical experiments through specialist quantum providers and measuring impact with simple KPIs, like fewer false positives, faster detection times, or reduced analyst workload.
The third step is to take a proactive approach to skills and governance. Quantum experimentation works best when it sits within a clear oversight model, so establishing a dedicated working group is essential. Their role is to define how quantum‑driven results, whether from optimisation or machine‑learning models, will be tested and validated within existing model‑risk frameworks.
The fourth and final step is the transition from pilot to production, using incremental adoption only where quantum demonstrably outperforms classical methods and tracking ROI consistently through stable KPIs.
Act early with quantum
Financial fraud is now evolving faster than the classical systems built to contain it. Quantum computing offers fraud leaders a rare chance to break this cycle, through strengthening detection while finally reducing the escalating cost of false positives. The institutions that move first will not only protect themselves from mounting losses but also reshape customer experience and operational efficiency. Those that delay risk widening the gap. Quantum‑ready fraud strategies are no longer experimental, they are fast becoming a competitive necessity.
