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loan-document-intelligence

Free

Income and bank-statement extraction + cross-validation. Boundary: deliberately kept separate from Doc2 (commercial credit-memo drafting); this repo is the high-volume retail pipeline; extraction overlap is component-...

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Overview

loan-document-intelligence is a document-intelligence agent for retail-lending underwriting. It extracts income and bank-statement data from an applicant's documents and runs deterministic cross-validation across them, including declared income versus payslip versus bank-statement salary credits, name/address consistency, balance trend, and affordability. It produces a cited, audited, maker-checker-gated income verification. It is decision-support for underwriting, not a lending decision; the agent verifies and the underwriter decides. It handles applicant PII, so the full R1 redaction and guardrail pipeline applies. The system is built ports-and-adapters on the Gemini Enterprise Agent Platform in the asia-southeast1 region, with an offline test and lint gate that runs without the Google Cloud SDK installed. It produces artifacts including LoanApplicationCase, CrossValidationResult, and IncomeVerificationSummary. Every figure is cited to a source document and field, every interaction is written to a WORM audit log, and the LLM only normalises and explains; it never overrides a deterministic check verdict. Profiles include local, live, gcp, platform, and onprem. The local profile is a real, SDK-free laptop stack with no API key, no emulator, and no google-cloud-* package. The project is licensed Apache-2.0.

Pricing

Free

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Use Cases

  • Underwriting teams use loan-document-intelligence to extract income and bank-statement data from an applicant's documents.
  • Underwriting teams use it to run deterministic cross-validation across documents, checking declared income vs payslip vs bank-statement salary credits, name/address consistency, balance trend, and affordability.
  • Reviewers use it to produce a cited, audited, maker-checker-gated income verification.
  • Developers use the local profile to run the whole pipeline offline on a laptop with no Google Cloud SDK.
  • Teams use the CLI to validate examples/extracts.json for deterministic cross-validation.
  • Operators use the onprem profile to fail fast with exit 2 and a migration message.
  • Reviewers use the human-review-console for maker-checker routing.
  • Teams use the platform profile to connect to agent-guardrail-gateway, agent-registry, model-quality-gate, and agent-observability.

Features

AI assistant

Natural language interface

Workflow automation

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