AI is changing how lenders evaluate risk by turning patterns in payments, social interactions and device signals into predictive inputs that traditional scorecards miss. Asli Demirguc-Kunt at the World Bank documents how digital footprints can expand access to credit in underserved regions by providing proxies where formal records are scarce, while James Manyika at McKinsey Global Institute highlights gains in speed and operational efficiency when machine learning systems replace manual underwriting. The relevance is immediate: faster decisions and finer-grained risk differentiation can lower borrowing costs for some borrowers and bring new customers into formal credit systems across diverse territories.
Algorithmic signals and data diversity
The causes of this shift lie in widespread smartphone adoption, richer alternative data and advances in supervised and unsupervised learning that detect nonlinear relationships. Research by Jon Kleinberg at Cornell and Sendhil Mullainathan at Harvard underscores that different fairness constraints cannot always be satisfied simultaneously when algorithms use complex inputs, which makes trade-offs inevitable. In many rural and culturally distinct communities the proxies used by models — such as transaction frequency, network links or geolocation patterns — reflect local behaviors, so models trained on urban populations can misestimate risk in other territories.
Consequences for borrowers and markets
Consequences include both inclusionary and systemic outcomes. Well-calibrated models can extend credit to people excluded by conventional scores, improving livelihoods and local economic activity in places with limited formal employment. At the same time Hyun Song Shin at the Bank for International Settlements cautions that model homogeneity and shared data sources can amplify procyclical dynamics across institutions, increasing market vulnerability if many lenders act on the same signals. Opacity of complex models raises consumer protection and discrimination risks, and cultural factors such as norms around sharing financial information shape acceptance and effectiveness.
Governance, design and operational responses must address these realities. Firms need rigorous validation, interpretability tools and localized validation practices, while regulators and standard-setters work to align model governance with financial stability and fairness goals. The combination of economic, territorial and human dimensions makes AI-driven credit assessment uniquely powerful but also uniquely demanding of multidisciplinary oversight informed by evidence from researchers and international institutions.
