Workshops Insurance Quantum Fraud Detection

Insurance Full Day Workshop

Quantum Fraud Detection and Claims Pattern Analysis

Insurance fraud costs the UK market an estimated GBP 3-4 billion annually. Classical machine learning detects roughly a third of it. This workshop examines where quantum algorithms offer genuine improvement in fraud scoring and network analysis, where they fall short on current hardware, and what quantum-inspired classical methods deliver today.

Full day (6 hours + Q&A)

In person or online

Max 30 delegates

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Workshop Description

Classical fraud detection pipelines in insurance rely on rules engines, supervised ML classifiers (typically gradient-boosted trees), and manual investigation queues. These work well for known fraud patterns but struggle with organised rings that distribute activity across multiple policies and claimants. Network graph analysis can identify ring structures but becomes computationally prohibitive as the graph grows beyond hundreds of thousands of nodes. Quantum computing offers two potential improvements: quantum kernel methods for higher-dimensional anomaly detection, and quantum graph algorithms for subgraph matching at scale.

Current NISQ hardware constrains both approaches to problem sizes well below production scale. A QSVM can process roughly 50-100 claims records on today's devices before noise degrades accuracy below classical baselines. Quantum graph algorithms handle approximately 20-50 node subgraphs. This workshop is honest about these limitations. It also covers quantum-inspired classical algorithms, a category that is production-ready now, delivering measurable uplift on existing GPU infrastructure without waiting for fault-tolerant quantum hardware. Participants leave with a clear decision framework: what to pilot on quantum hardware, what to deploy classically today, and what to wait for.

What participants cover

QSVM anomaly detection: encoding claims feature vectors, quantum kernel matrices, and accuracy comparison against classical XGBoost on matched datasets
Quantum Kernel Estimation (QKE) for high-dimensional claims data: identifying non-linear fraud patterns in feature spaces too large for classical kernel methods
Quantum graph algorithms for fraud ring detection: Grover-accelerated subgraph matching, quantum walks for community detection
NISQ hardware constraints: realistic problem sizes (50-100 claims for QSVM, 20-50 nodes for graph), noise impact on classification accuracy
Quantum-inspired classical methods: tensor networks, simulated quantum annealing, and dequantisation results that deliver near-term production value
Regulatory compliance: FCA Consumer Duty explainability, GDPR Article 22 automated decision rights, Solvency II model validation for quantum ML

Preliminary Agenda

Full day workshop structure with scheduled breaks. Content is configurable to your organisation's claims data environment and existing fraud detection pipeline.

#	Session	Topics
1	Insurance Fraud Landscape and Detection GapsWhere classical detection fails and quantum approaches may help	UK insurance fraud: ABI estimates GBP 1.1 billion detected annually, with estimated undetected fraud 2-3x higher Classical ML pipeline limitations: feature engineering bottlenecks, high false positive rates in anomaly detection, graph analysis scaling walls Organised fraud rings: why network graph analysis is computationally expensive at scale and where quantum graph algorithms offer theoretical advantage
2	Quantum Machine Learning for Anomaly DetectionQSVM, quantum kernel methods, and variational classifiers on claims data	Quantum Support Vector Machines (QSVM): encoding claims feature vectors into quantum states for anomaly scoring Quantum Kernel Estimation (QKE): identifying non-linear fraud patterns that classical kernels miss in high-dimensional claims datasets Variational Quantum Classifiers (VQC): parameterised circuits for binary fraud/legitimate classification Barren plateau problem: why deep variational circuits fail to train and what this means for practical deployment timelines
Break, after 55 min
3	Quantum Graph Algorithms for Fraud Ring DetectionNetwork analysis at scale for organised insurance fraud	Grover-accelerated subgraph matching: searching for known fraud ring topologies in policyholder/claimant networks Quantum walk algorithms for community detection: identifying clusters of connected claims that suggest organised fraud Hybrid classical-quantum pipelines: classical pre-filtering to reduce problem size, quantum processing on the combinatorial core Honest NISQ assessment: current hardware handles graphs of approximately 20-50 nodes, well below production fraud networks
4	Interactive Demonstration: Quantum Fraud Scoring PipelineFacilitator-led walkthrough of a hybrid classical-quantum fraud detection pipeline	Data preparation: encoding claims records as quantum-compatible feature vectors using amplitude encoding Running a QSVM classifier on a 20-claim sample dataset: interpreting kernel matrix output and anomaly scores Comparing quantum anomaly scores against classical XGBoost baseline on the same dataset
Break, after 60 min
5	Quantum-Inspired Classical AlgorithmsNear-term uplift without quantum hardware	Tensor network methods for claims pattern recognition: classical algorithms inspired by quantum circuit structure Simulated quantum annealing for claims triage optimisation: D-Wave-inspired approaches running on classical GPUs Dequantisation results: Tang (2019) and subsequent work showing classical algorithms that match quantum ML speed-ups for specific problems Practical recommendation: quantum-inspired classical as the production path for 2025-2028, true quantum for 2029+
6	Regulatory and Governance ConsiderationsDeploying ML (quantum or classical) in regulated claims decisions	FCA Consumer Duty: explainability requirements for automated claims decisions GDPR Article 22: automated individual decision-making and the right to human review Model validation under Solvency II Pillar 2: how quantum ML models fit into existing model risk management frameworks
7	Q&A and Pilot Planning

Designed and Delivered By

Workshops are designed and delivered by QSECDEF in collaboration with sector specialists. All facilitators have direct experience in both quantum technologies and insurance systems.

QSECDEF brings world-leading expertise in post-quantum cryptography, quantum computing strategy, and defence-grade security assessment. Our advisory membership spans 600+ organisations and 1,200+ professionals working at the intersection of quantum technologies and critical infrastructure security.

Insurance workshops are co-delivered with sector specialists who bring direct operational experience in claims analytics, fraud investigation, and data science within Lloyd's syndicates, composite insurers, and specialty lines.

Quantum technologies are evolving quickly and new developments emerge regularly. This page was last updated on 15/03/2026. For the most current information about course content and suitability for your organisation, we recommend contacting us directly.

Commission This Workshop

Sessions are configured around your claims data environment, existing fraud detection pipeline, and regulatory obligations. Get in touch to discuss requirements and schedule a date.

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