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Quantum for Financial Fraud Detection

Quantum machine learning approaches to anomaly detection, transaction pattern analysis, and fraud ring identification.

Full day (6 hours)

Certificate on completion

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Course Overview

Covers quantum anomaly detection, quantum machine learning for transaction classification, and quantum graph analysis for AML/KYC compliance. Includes hands-on pipeline construction, honest assessment of current hardware limits, and hybrid architecture design for fraud analytics teams.

Curriculum

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

#	Session	Topics
1	The Fraud Detection Challenge at Scale Transaction volumes, false positive costs, and evolving attack patterns
2	Quantum Anomaly Detection Algorithms Quantum one-class SVM, clustering, and Grover-based pattern search	Quantum one-class SVM for anomaly detection (Rebentrost et al.) and its application to transaction data where classical models plateau against adversarial fraud Quantum clustering algorithms for outlier identification in high-dimensional feature spaces that capture correlations classical models miss Grover-based search for suspicious patterns in large transaction graphs: quadratic speedup, practical circuit depth constraints, and input encoding costs
Break
3	QML for Transaction Classification Variational classifiers and quantum kernel methods for fraud scoring	Variational quantum classifiers for fraud/not-fraud binary classification on transaction data: circuit architecture, parameter training, and convergence behaviour Quantum kernel methods for feature extraction from transaction metadata (amount, frequency, geolocation, merchant category, timestamp patterns) Handling extreme class imbalance: quantum approaches to the 99.9% legitimate transaction problem, including cost-sensitive quantum loss functions
4	Hands-On: Building a Quantum Fraud Detection Pipeline Encoding, training, and benchmarking against classical baselines	Encoding transaction features into quantum states using amplitude and angle encoding strategies Training a variational quantum classifier on labelled fraud data using Qiskit or PennyLane on a simulator backend Measuring precision, recall, and F1 score against classical random forest and XGBoost baselines on the same synthetic dataset
Break
5	AML and KYC: Quantum Approaches to Graph Analysis Quantum walk algorithms for network analysis and entity resolution	Quantum walk algorithms for transaction network analysis: polynomial (not exponential) speedup over classical graph traversal for community detection Entity resolution in transaction graphs: matching identities across fragmented records using quantum similarity measures Beneficial ownership chain detection: tracing layered corporate structures through quantum graph search to identify concealed control relationships
6	Integration Architecture and Honest Assessment Hybrid pipeline design, current benchmarks, and where classical ML remains sufficient	Hybrid classical-quantum pipeline design: quantum batch scoring of high-risk segments alongside real-time classical transaction monitoring Current accuracy benchmarks: proof-of-concept results from the HSBC/IBM pilot (2023) and the Credit Agricole/Pasqal collaboration (2024) on synthetic fraud datasets Honest maturity assessment: false positive reduction is the most likely near-term commercial value; real-time quantum scoring is not feasible on current hardware
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.

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Quantum for Financial Fraud Detection

Course Overview

Curriculum

Designed and Delivered By

Quantum Security Defence

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