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 address that gap. Particle Swarm Optimization (PSO) handles feature selection, after which transaction features are encoded into quantum-inspired Hilbert spaces using Angle and Amplitude Encoding. Two quantum models, a Quantum Kernel SVM (QK-SVM) and a Variational Quantum Circuit (VQC), process these representations, and their outputs are fused with scores from Random Forest, XGBoost, and One-Class SVM through a weighted scoring scheme. On the Credit Card Fraud Detection benchmark, the hybrid framework outperforms classical-only baselines on rare event detection across Precision, Recall, F1-Score, and ROC-AUC, without needing actual quantum hardware.
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 does not.
Quantum machine learning (QML) has surged as a prominent area of research with the objective to go beyond the capabilities of classical machine learning models. A critical aspect of any learning task is the process of data embedding, which directly impacts model performance. Poorly designed data-embedding strategies can significantly impact the success of a learning task. Despite its importance, rigorous analyses of data-embedding effects are limited, leaving many cases without effective assessment methods. In this work, we introduce a metric for binary classification tasks, the
class margin
, by merging the concepts of average randomness and classification margin. This metric analytically connects data-induced randomness with classification accuracy for a given data-embedding map. We benchmark a range of data-embedding strategies through
class margin
, demonstrating its ability to identify data-induced randomness that hinders classification performance. We expect this work to provide a new approach to evaluate QML models by their data-embedding processes, addressing gaps left by existing analytical tools
The growing need for accurate, consistent, and reliable loan approval systems The use of machine learning in credit decision-making is increasingly important for financial institutions, but comparative research still often focuses on Accuracy or a limited number of classification metrics, so the trade-off between predictive performance and computational efficiency is not fully described. This study aims to develop a Standardized Multi-Metric Evaluation Framework (MMEF) to support the selection of more objective and reproducible machine learning models in the case of loan approval. The research method uses a standardized experimental pipeline with consistent preprocessing, class balancing using the Synthetic Minority Over-sampling Technique (SMOTE), identical data sharing, model optimization, and multi-metric evaluation. Five algorithms, namely Logistic Regression, Support Vector Machine (SVM), Random Forest, XGBoost, and CatBoost, are compared using a loan approval dataset consisting of 45,000 records and 13 predictor features. The evaluation includes Accuracy, Precision, Recall, F1-score, ROC-AUC, training time, and Overall Score. The results show that XGBoost provides the best overall performance with Accuracy 87.86%, Precision 66.77%, Recall 90.30%, F1-score 76.77%, ROC-AUC 96.27%, and Overall Score 0.827503. CatBoost has the fastest training time of 1.01 seconds, while SVM obtained the highest Recall of 92.40% with a much longer training time. These results indicate that model selection is not sufficient based on a single metric. MMEF provides a more systematic evaluation basis 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
Credit card fraud detection presents unique challenges for machine learning due to extreme class imbalance, evolving fraud patterns, and asymmetric misclassification costs. While numerous algorithms have been proposed for this domain, their evaluation typically relies on simplistic metrics that fail to capture the multifaceted requirements of operational fraud detection systems. This study introduces a sophisticated multi‐criteria ranking framework that extends beyond traditional performance measures to incorporate temporal stability, computational efficiency, and robustness considerations. We develop an anisometric penalty structure that quantifies deficiencies across multiple dimensions with differential weighting, and we apply this framework to evaluate several leading machine learning approaches with various sampling strategies, XGBoost, Random Forest, and logistic regression—using a large dataset of European credit card transactions. Our findings reveal that an active learning approach with combined uncertainty–diversity sampling achieves superior performance across multiple evaluation criteria, outperforming both XGBoost and traditional classification algorithms. Statistical significance testing and sensitivity analysis confirm the robustness of these results across different weight configurations and operational scenarios. This study advances both the theoretical understanding of machine learning evaluation in fraud detection and provides practical guidance for financial institutions seeking to implement or enhance their fraud detection systems.
M. Z. Yüzügüldü, H. Altún, Ali ÇoŞkun et al.· International Transactions i...· 0 citations
Accurate credit risk assessment underpins modern financial risk management. Meanwhile, advanced machine learning algorithms have largely replaced traditional linear models in default prediction. However, real-world consumer credit data is scattered across complex relational tables and exhibits extreme class imbalance. As a result, conventional coarse-grained aggregations often fail to capture these nuances, leading to the loss of crucial micro-level default signals. Accordingly, this paper conducts a comparative analysis of the machine learning workflows that performed the best in the Home Credit default risk challenge. In particular, through qualitative deconstruction of pipeline architectures, three representative high-level parallel processing pipelines are analyzed and compared: one based on domain knowledge, one based on heterogeneous stacking framework, and one based on microscopic target aggregation scheme. Furthermore, the key differences among them in terms of feature engineering, handling of imbalanced data, and integration architecture are compared and evaluated. The results indicate that different architectures can effectively extract sparse default signals via their inherent mechanisms, yielding significant gains on imbalanced credit data. It further demonstrate the high effectiveness of feature dimension reconstruction and target dimension reduction in handling extremely imbalanced credit data, providing certain references for industrial credit risk modeling.
Sisi Xia· Advances in Economics, Manag...· 0 citations
