Empowering Credit Scoring Systems with Quantum-Enhanced Machine Learning

ArXiv ID: 2404.00015 “View on arXiv”

Authors: Unknown

Abstract

Quantum Kernels are projected to provide early-stage usefulness for quantum machine learning. However, highly sophisticated classical models are hard to surpass without losing interpretability, particularly when vast datasets can be exploited. Nonetheless, classical models struggle once data is scarce and skewed. Quantum feature spaces are projected to find better links between data features and the target class to be predicted even in such challenging scenarios and most importantly, enhanced generalization capabilities. In this work, we propose a novel approach called Systemic Quantum Score (SQS) and provide preliminary results indicating potential advantage over purely classical models in a production grade use case for the Finance sector. SQS shows in our specific study an increased capacity to extract patterns out of fewer data points as well as improved performance over data-hungry algorithms such as XGBoost, providing advantage in a competitive market as it is the FinTech and Neobank regime.

Keywords: Quantum Machine Learning, Quantum Kernels, Systemic Quantum Score (SQS), Feature Extraction, Generalization, Fintech

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 4.0/10
• Quadrant: Lab Rats
• Why: The paper introduces advanced quantum machine learning concepts like quantum kernels, Hilbert space mappings, and evolutionary algorithms for feature map optimization, requiring sophisticated mathematical background, but the empirical validation is limited to preliminary results on a single production dataset without reported statistical significance or backtest metrics.

  flowchart TD
    A["Research Goal: Enhance Credit Scoring<br>in FinTech with QML"] --> B["Methodology: Systemic Quantum Score SQS"]
    C["Data: Scarce/Skewed Financial Datasets"] --> B
    B --> D["Computational Process:<br>Quantum Kernels for Feature Space"]
    D --> E{"Classical Models vs SQS"}
    E -- Classical (e.g., XGBoost) --> F["Struggles with Data Scarcity"]
    E -- SQS Proposed --> G["Key Outcomes:<br>Increased Pattern Extraction<br>Improved Generalization"]