20 May 2026
The editors describe the discipline as being at "a critical juncture," shaped by fast-moving technology, tightening regulation, and growing scrutiny of algorithmic decisions.
Automated credit scoring is now standard practice across retail and corporate lending in the financial industry, a direct application of decades of quantitative methods research. This special issue continues that tradition, advancing knowledge and application at a time when that scrutiny and regulatory pressure are continuously increasing.
Edited by Galina Andreeva (Edinburgh), Jonathan Crook (Edinburgh) and Christophe Mues (Southampton), and published in May 2026 in the Annals of Operations Research, the issue is linked to the Centre's Credit Scoring and Credit Control conferences, held since 1989.
The contributions span four areas. On stress-testing and loss modelling, papers examine how transition matrix choices shape long-run default projections, extend stress-testing methods to IFRS 9 overlays, and propose new ways to estimate loss and profitability in online lending. On machine learning and alternative data, authors draw on sources as varied as social media, ESG reports and satellite imagery to strengthen credit rating and scoring models. Further research tackles practical concerns beyond default prediction, from reject inference to fraud detection to model monitoring.
Finally, a standout paper turns to algorithmic fairness, examining how credit models can amplify inequity across intersecting characteristics such as gender, age and marital status. A question of growing importance as automated decisions play a larger role in who gets access to credit.