Aug 2026· Risk Management Magazine· 0 citations· 15 references
Abstract
The objective of this paper is to demonstrate that traditional statistical problems in credit scoring can be solved efficiently by implementing a quantum-enhanced version of the traditional Support Vector Machines algorithm. Three significant case studies are presented, involving regression, dichotomous and multi-class classification problems in the field of credit risk management. The analysis highlights the promise of the quantum kernel method as a competitive solution especially for multiclass problems with strongly structured features. Quantum-enhanced models tend to outperform classical approaches because they can capture more intricate non-linear relationships between variables, partly due to their ability to leverage entanglement. This advantage is particularly evident in credit risk assessment, where it can improve the credit evaluation process and help reduce credit losses for financial and insurance institutions.
Classical ML models for fraud detection tend to break down on rare events, which are the cases that matter most. In imbalanced transaction datasets, new fraud patterns often fall outside what these models were trained to recognize. This paper presents a Hybrid Quantum-Classical Anomaly Detection Framework built to addr...
V. Maral, Aryan Dongare, Vidya Gandhale et al.· 2026 International Conferenc...· 0 citations
It is shown that ordinary hyperparameter choices move performance by considerably more than the quantum kernel does, that additional qubits degrade rather than improve performance through kernel concentration, and that the clustering framing itself fails at realistic class imbalance though kernel-based anomaly scoring...
A metric for binary classification tasks, the class margin, is introduced, which analytically connects data-induced randomness with classification accuracy for a given data-embedding map, demonstrating its ability to identify data-induced randomness that hinders classification performance.
A Standardized Multi-Metric Evaluation Framework is developed to support the selection of more objective and reproducible machine learning models in the case of loan approval to identify models that have a balance of performance and efficiency in loan approval experiments.
Trihartono Agus, Agus Ilyas Ilyas, S. Sattriedi et al.· Jurnal Informatika: Jurnal P...· 0 citations
An anisometric penalty structure is developed that quantifies deficiencies across multiple dimensions with differential weighting, and is applied to evaluate several leading machine learning approaches with various sampling strategies, XGBoost, Random Forest, and logistic regression—using a large dataset of European cr...
M. Zahid Yüzügüldü, H. Altún, Ali Coşkun et al.· International Transactions i...· 0 citations
A comparative analysis of the machine learning workflows that performed the best in the Home Credit default risk challenge indicates that different architectures can effectively extract sparse default signals via their inherent mechanisms, yielding significant gains on imbalanced credit data.
Sisi Xia· Advances in Economics, Manag...· 0 citations
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