We built AI agents that reduce mortgage processing from 18 days to 3–5
Most mortgage processing delays aren’t due to risk — they’re due to manual workflows. We’ve been working on SimplAI, an AI-driven system designed for banking and financial services, starting with mortgage operations. The problem we kept seeing: 15–22 day processing timelines Heavy manual document handling (500+ pages per loan) Repetitive data entry + verification loops Underwriters spending hours on non-decision work So we built a set of AI agents that handle the operational layer: Document AI (IDP) → classifies + extracts data from loan docs in minutes Income analysis models → parse tax returns, payslips, and variable income Verification integrations → real-time employment + financial checks AI-assisted underwriting → pre-validates files and generates conditions Compliance engine → continuously checks against regulatory rules What we’re seeing in production: End-to-end processing: ~18 days → 3–5 days Data extraction accuracy: 97%+ Underwriting review time: 3–4 hrs → <45 mins Cost per loan: reduced by ~40–50% We’re not replacing underwriters — we’re removing the operational bottlenecks around them. Still early, but we’re exploring: Agent-based workflows across lending lifecycle Better handling of edge cases (self-employed borrowers, non-QM loans) Explainability in underwriting decisions Would love feedback from folks in fintech, lending, or anyone building AI systems in regulated environments.
✨ AI 摘要
Most mortgage processing delays aren’t due to risk — they’re due to manual workflows. We’ve been working on SimplAI, an AI-driven system designed for banking and financial services, starting with mortgage operations. The problem we kept seeing: 15–22 day processing timelines Heavy manual document handling (500+ pages per loan) Repetitive data entry + verification loops Underwriters spending hours on non-decision work So we built a set of AI agents that handle the operational layer: Document AI (IDP) → classifies + extracts data from loan docs in minutes Income analysis models → parse tax returns, payslips, and variable income Verification integrations → real-time employment + financial checks AI-assisted underwriting → pre-validates files and generates conditions Compliance engine → continuously checks against regulatory rules What we’re seeing in production: End-to-end processing: ~18 days → 3–5 days Data extraction accuracy: 97%+ Underwriting review time: 3–4 hrs → <45 mins Cost per loan: reduced by ~40–50% We’re not replacing underwriters — we’re removing the operational bottlenecks around them. Still early, but we’re exploring: Agent-based workflows across lending lifecycle Better handling of edge cases (self-employed borrowers, non-QM loans) Explainability in underwriting decisions Would love feedback from folks in fintech, lending, or anyone building AI systems in regulated environments.
適合誰
開發者、產品團隊與技術型創辦人。
為何值得關注
Most mortgage processing delays aren’t due to risk — they’re due to manual workflows. We’ve been working on SimplAI, an AI-driven system designed for banking and financial services, starting with mortgage operations. The problem we kept seeing: 15–22 day processing timelines Heavy manual document handling (500+ pages per loan) Repetitive data entry + verification loops Underwriters spending hours on non-decision work So we built a set of AI agents that handle the operational layer: Document AI (IDP) → classifies + extracts data from loan docs in minutes Income analysis models → parse tax returns, payslips, and variable income Verification integrations → real-time employment + financial checks AI-assisted underwriting → pre-validates files and generates conditions Compliance engine → continuously checks against regulatory rules What we’re seeing in production: End-to-end processing: ~18 days → 3–5 days Data extraction accuracy: 97%+ Underwriting review time: 3–4 hrs → <45 mins Cost per loan: reduced by ~40–50% We’re not replacing underwriters — we’re removing the operational bottlenecks around them. Still early, but we’re exploring: Agent-based workflows across lending lifecycle Better handling of edge cases (self-employed borrowers, non-QM loans) Explainability in underwriting decisions Would love feedback from folks in fintech, lending, or anyone building AI systems in regulated environments.
核心功能
- Most mortgage processing delays aren’t due to risk — they’re due to manual workflows.
- We’ve been working on SimplAI, an AI-driven system designed for banking and financial services, starting with mortgage operations.
- The problem we kept seeing: 15–22 day processing timelines Heavy manual document handling (500+ pages per loan) Repetitive data entry + verification loops Underwriters spending hours on non-decision work So we built a set of AI agents that handle the operational layer: Document AI (IDP) → classifies + extracts data from loan docs in minutes Income analysis models → parse tax returns, payslips, and variable income Verification integrations → real-time employment + financial checks AI-assisted underwriting → pre-validates files and generates conditions Compliance engine → continuously checks against regulatory rules What we’re seeing in production: End-to-end processing: ~18 days → 3–5 days Data extraction accuracy: 97%+ Underwriting review time: 3–4 hrs → <45 mins Cost per loan: reduced by ~40–50% We’re not replacing underwriters — we’re removing the operational bottlenecks around them.
- Still early, but we’re exploring: Agent-based workflows across lending lifecycle Better handling of edge cases (self-employed borrowers, non-QM loans) Explainability in underwriting decisions Would love feedback from folks in fintech, lending, or anyone building AI systems in regulated environments.
使用場景
- Review original launch sources before making adoption decisions.
- Track community momentum from Product Hunt, GitHub, and Hacker News.