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

TL;DR

This paper proposes a quantum-inspired optimization method using QUBO models to improve the definition of rating scales in creditworthiness assessment, addressing complex constraints in finance.

Contribution

It introduces a QUBO formulation for rating scale definition and validates it with classical heuristics and benchmarking against brute-force methods.

Findings

QUBO model effectively solves complex rating scale optimization problems.

Classical heuristics produce solutions comparable to brute-force methods.

Framework is suitable for more complex, real-world scenarios.

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.