Paper 2026/876

An AI-Driven Post-Quantum Cryptographically Secure Workflow for Collaborative Credit Scoring

Abstract

Credit scoring plays a critical role in the financial industry, allowing institutions to evaluate the creditworthiness of potential borrowers. Typically, a model is estimated from repositories of attributes of past borrowers linked to their loan and payments performance. The model is then used to compute an applicant's score. The training and customer data are subject to regulations that require privacy of financial records. This creates a tension between the full utilization of available data and the prevention of leakage. Recently, the tension has intensified from, on one hand, improvement in AI methods to utilize data from nontraditional sources to develop prediction models and, on the other hand, increased concern over the vulnerability of encrypted data to penetration from quantum computers. We present a credit score workflow that addresses both issues by using AI methods to estimate a credit score model in a collaborative setting, combined with post-quantum cryptographic methods to protect data. We develop a ``toy'' workflow which can form a base for more complex ``real world'' implementations. We provide links to a code-base.