Training Course Professional

Quantum Fraud Detection and Claims Pattern Analysis

Quantum machine learning for claims fraud identification, network analysis, and anomaly detection at scale.

Full day (6 hours)

Certificate on completion

Enquire About This Course View Curriculum

Proud to recommend our expert members

Course Overview

Full-day workshop on quantum machine learning approaches to insurance fraud detection. Covers QSVM anomaly scoring, quantum kernel methods, quantum graph algorithms for organised fraud ring detection, quantum-inspired classical alternatives, and regulatory considerations under FCA Consumer Duty and GDPR Article 22.

Curriculum

Course content is structured into sessions below. Each session is configurable to your organisation's environment, threat model, and objectives.

#	Session	Topics
1	Insurance Fraud Landscape and Detection Gaps Where 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 Detection QSVM, 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
3	Quantum Graph Algorithms for Fraud Ring Detection Network 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 Pipeline Facilitator-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
5	Quantum-Inspired Classical Algorithms Near-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 Considerations Deploying 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

Training courses are designed and delivered by QSECDEF in collaboration with subject specialists. All instructors have direct experience in quantum technologies and security.

QSECDEF brings expertise in post-quantum cryptography, quantum computing strategy, and defence-grade security assessment. Our membership spans 900+ organisations and 1,500+ professionals working at the intersection of quantum technologies and critical infrastructure security.

Enquire About This Course

Get in touch to discuss scheduling, group pricing, and how this course can be configured to your organisation's needs.

Training Courses Consulting Services Training

Quantum Fraud Detection and Claims Pattern Analysis

Course Overview

Curriculum

Designed and Delivered By

Quantum Security Defence

Enquire About This Course