Think of this as a tireless junior credit underwriter that can log into systems, pull documents, read them, cross-check rules, and draft decisions on loan applications—then hand them to humans for final approval.
Traditional underwriting in fintech is slow, manual, and expensive because analysts must gather data from many systems, interpret unstructured documents, and apply complex policies. Agentic AI automates much of this data collection, analysis, and decision support, speeding up approvals and reducing human effort while improving consistency.
Deep integration into underwriting workflows, access to proprietary applicant and performance data, and fine-tuned policy/decision logic that becomes more valuable over time as models are calibrated to portfolio outcomes.
Hybrid
Vector Search
High (Custom Models/Infra)
Context window cost and latency for complex, multi-step underwriting workflows that require retrieving and reasoning over large volumes of applicant and third-party data, combined with strict data privacy and regulatory constraints.
Early Adopters
Positioned specifically around agentic, task-performing AI for underwriting rather than just scorecards or simple RAG chat; focuses on orchestrating multi-step workflows (data gathering, document analysis, rule application, and decision explanation) across fintech systems, which is more aligned with the end-to-end underwriting process than many legacy ML-based risk scoring vendors.