AI-Powered Credit Scoring & Underwriting
Alternative data-driven decisioning that expands credit access without increasing risk.
The Challenge
Over 1.4 billion adults globally remain unbanked or underserved by traditional credit systems. FICO-based models exclude thin-file and no-file borrowers — leaving significant market opportunity on the table. Meanwhile, manual underwriting processes are slow (days to weeks), inconsistent across underwriters, and expensive to scale — constraining growth in a competitive lending market.
The Quapt Agentic AI Solution
QUAPT engineers intelligent underwriting agents that ingest and reason over alternative data signals — transaction history, cash flow patterns, utility payments, rent behaviour, and business operational data — alongside traditional bureau data. An LLM reasoning layer produces explainable, compliance-ready credit decisions with confidence scores and risk narratives, enabling human underwriters to review edge cases efficiently.
Key AI Capabilities
- Multi-source alternative data ingestion and normalisation pipeline
- LLM-powered credit narrative generation for human review
- Explainable AI scoring with adverse action reason codes (FCRA compliant)
- Dynamic risk banding and pricing recommendation engine
- Portfolio stress-testing and scenario modelling agents
- Continuous model monitoring for drift and performance degradation
- Seamless integration with LOS (Loan Origination Systems)
35%
Increase in Approval Rates
28%
Reduction in Default Rates
80%
Faster Decision Time
60%
Lower Underwriting Cost
Business Outcomes
- Expanded addressable market by serving previously excluded borrower segments
- Improved portfolio quality through superior risk signal utilisation
- Dramatic reduction in time-to-decision — from days to minutes
- Consistent, bias-tested underwriting decisions across all applications
- Full regulatory compliance with explainability requirements
- Significant cost reduction in underwriting operations at scale
