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Credit Scoring and Underwriting with Quantum

Applying quantum kernel methods and variational classifiers to credit risk assessment and underwriting decisions.

Full day (6 hours)

Certificate on completion

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Course Overview

For credit risk and model validation teams. Covers quantum kernel methods for PD classification, variational quantum classifiers for LGD estimation, fair lending compliance under ECOA and the EU AI Act, and SR 11-7 model validation for quantum-enhanced credit scoring on NISQ hardware.

Curriculum

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

#	Session	Topics
1	Classical Credit Scoring Limits Where logistic regression and gradient boosting reach their ceiling
2	Quantum Kernel Methods for Credit Risk Feature spaces that classical models cannot access	Quantum kernel estimation: mapping borrower features into high-dimensional Hilbert space Quantum support vector machines for PD (probability of default) classification Benchmark-specific performance comparisons: quantum versus classical kernel methods on UCI/Kaggle credit datasets
Break
3	PD/LGD Calibration with Quantum Machine Learning Improving loss-given-default estimation accuracy	Variational quantum classifiers for LGD prediction on structured and unstructured data Alternative data enrichment: quantum feature maps for non-traditional credit signals NISQ constraints: qubit counts, circuit noise, and what current hardware can realistically process
4	Interactive Demonstration Facilitator-led credit scoring model comparison	Quantum kernel credit scoring pipeline on a sample consumer lending dataset Interpreting quantum model outputs alongside classical model baselines Assessing model explainability: SHAP-equivalent approaches for quantum credit models
Break
5	Fair Lending and Explainability Regulatory compliance for quantum credit models	ECOA and Regulation B adverse action notice requirements for quantum-derived decisions EU AI Act high-risk classification: credit scoring as a regulated AI use case Model interpretability techniques for quantum machine learning in regulated environments
6	Implementation Roadmap From pilot to production in a regulated credit environment	SR 11-7 model validation for quantum credit models: documentation and governance Integration architecture: quantum model serving alongside existing decisioning platforms Vendor landscape: Qiskit Machine Learning, PennyLane, Amazon Braket ML workflows
7	Q&A and Action 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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Credit Scoring and Underwriting with Quantum

Course Overview

Curriculum

Designed and Delivered By

Quantum Security Defence

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