Nu Holdings deployed its nuFormer AI credit model to production in 2025, enabling the fintech to launch more than 100 products and features across markets while maintaining stable risk-adjusted net interest margins quarter-over-quarter. The deployment demonstrates how AI-driven underwriting allows rapid expansion without proportional increases in credit losses.
Inter & Co welcomed 7 million new clients in 2025, marking its strongest annual performance. The fintech maintains industry-leading funding costs at 65.6% of CDI, Brazil's benchmark interbank rate. Inter's newer client cohorts are transacting faster and more frequently than older cohorts, suggesting AI models improve client quality at acquisition.
Traditional credit underwriting requires manual review of documents, income verification, and subjective risk assessment. This process takes days and limits portfolio growth to the pace of human reviewers. AI models analyze hundreds of data points in seconds, including transaction patterns, payment history, and behavioral signals invisible to manual processes.
Real-time portfolio monitoring represents the second advantage. AI systems flag deteriorating credit conditions before they materialize as losses, allowing lenders to adjust terms or suspend credit lines proactively. Traditional systems rely on monthly reviews, creating lag between risk emergence and response.
The technology enables client acquisition cost optimization. Automated decisioning reduces underwriting expenses per application while improving approval accuracy. Higher approval rates for creditworthy applicants increase conversion, while better rejection of risky applicants reduces future losses. Both effects improve unit economics.
Nu's stable margins despite rapid product expansion suggest AI models maintain credit discipline under growth pressure. Manual underwriting teams often lower standards when acquisition targets rise. Algorithmic decisioning applies consistent criteria regardless of volume.
The competitive gap widens between AI-equipped fintechs and traditional banks. Incumbents carry legacy systems designed for branch-based lending and batch processing. Retrofitting AI requires replacing core infrastructure, a multi-year project. Neobanks built cloud-native architectures from inception, allowing rapid model deployment.
Inter's cohort performance data indicates AI models identify higher-quality borrowers at onboarding. Faster transaction velocity among new clients suggests they carry better credit profiles and higher engagement levels. This selection effect compounds over time as portfolio composition shifts toward AI-screened borrowers.
Brazilian fintechs now process millions of credit decisions monthly through automated systems, a scale impossible with human underwriters. The technology transforms credit from a scarce, manually rationed resource into a dynamic product that scales with demand while maintaining risk parameters.