Algorithmic Credit Scoring Systems Optimizing Capital Access During Economic Shocks

Abstract

Economic shocks increasingly challenge conventional credit assessment systems by limiting access to capital for vulnerable borrowers and small businesses. This study aims to examine how algorithmic credit scoring systems optimize capital access during periods of economic uncertainty. Using a qualitative multiple-case study design, data were collected through interviews with 24 participants, observations of digital lending operations, and analysis of institutional documents from financial service organizations. The findings reveal three key mechanisms: adaptive scoring improves lending resilience, alternative data expands borrower evaluation, and institutional collaboration strengthens credit allocation governance. This study contributes a resilience-oriented perspective by extending algorithmic credit research beyond predictive accuracy toward adaptive financial ecosystems. The findings recommend integrating dynamic algorithms, responsible data utilization, and collaborative governance to support inclusive and sustainable lending practices.