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A More General Quantum Credit Risk Analysis Framework.

Emanuele Dri1, Antonello Aita2, Edoardo Giusto1

  • 1Dipartimento di Automatica e Informatica (DAUIN), Politecnico di Torino, 10129 Torino, Italy.

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Summary
This summary is machine-generated.

This study introduces an enhanced quantum algorithm for credit risk analysis (CRA), improving asset risk models with multiple factors and flexible loss-given-default inputs. The new approach offers a more realistic financial solution, tested on quantum hardware.

Keywords:
algorithmscredit risk analysisquantum computingquantum financescalability

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Area of Science:

  • Quantum Computing
  • Financial Mathematics
  • Computational Finance

Background:

  • Classical credit risk analysis (CRA) methods face limitations in accuracy and realism.
  • Existing quantum algorithms for CRA offer speedups but require further refinement for practical application.
  • Business domain experts have identified key areas for improvement in current CRA quantum approaches.

Purpose of the Study:

  • To propose and evaluate a novel variant of the CRA quantum algorithm.
  • To address identified limitations in existing quantum CRA methodologies.
  • To enhance the realism and applicability of quantum algorithms in financial risk assessment.

Main Methods:

  • Developed a new CRA quantum algorithm variant.
  • Enhanced the asset risk model to incorporate multiple systemic risk factors.
  • Modified the loss-given-default input to accept non-integer values.
  • Tested the algorithm using classical simulations and IBM Quantum Experience QPUs.

Main Results:

  • The enhanced CRA quantum algorithm provides a more realistic model for asset default probability by considering multiple systemic risk factors.
  • The algorithm accommodates flexible loss-given-default inputs, enabling the use of real financial data for benchmarking.
  • Testing on QPUs confirmed the algorithm's feasibility and provided a baseline for future research.
  • The proposed enhancements increase circuit depth and width requirements.

Conclusions:

  • The enhanced CRA quantum algorithm offers a more realistic and flexible solution for financial risk analysis compared to existing methods.
  • The improvements pave the way for more meaningful and useful applications of quantum computing in the financial sector.
  • Further research and development are needed to optimize the algorithm for large-scale quantum hardware.