A new approach to rating scale definition with quantum-inspired optimization

2026-03-27

AI Summary

This study addresses the challenge of constructing credit scoring scales that satisfy regulatory constraints—a complex constrained combinatorial optimization problem in financial credit rating. For the first time, this work formulates the problem as a Quadratic Unconstrained Binary Optimization (QUBO) model, making it compatible with quantum computing frameworks while enabling efficient solution via classical heuristic algorithms. The proposed approach achieves solution quality equivalent to exhaustive search but with significantly improved scalability, thereby supporting applications involving more intricate regulatory constraints. By bridging regulatory compliance and computational efficiency, this method establishes a novel paradigm for designing credit scoring systems that are both compliant and scalable.

Abstract

In finance, assessing the creditworthiness of loan applicants requires lenders to cluster borrowers using rating scales. Financial institutions must define the scales in compliance with strict institutional constraints, resulting in solving a complex combinatorial constrained optimization problem. This contribution studies how to solve this problem using a Quadratic Unconstrained Binary Optimization (QUBO) model, a formulation suitable for quantum hardware. We validate this approach by testing the proposed formulation with classical heuristics. We then benchmark the results against a brute-force method to demonstrate consistent solution quality and highlight the framework's suitability for more complex scenarios.

Problem

• rating scale
• creditworthiness
• constrained optimization
• combinatorial optimization
• financial institutions

Innovation

• QUBO
• quantum-inspired optimization
• rating scale
• constrained clustering
• credit risk assessment