---
name: al-628-catch-lending-file-mistakes-before-money-moves-have-an-ai
description: "At the same Nov 5, 2026 NH NongHyup Bank / AgileSoDA demo covered by Seoul Economic Daily, the audit department’s “zero lending accidents” idea drew a large crowd. The agent reads hundreds of loan documents and cross-checks loan amounts, collateral and income data against credit review opinions to surface errors and omissions before funding. Staff still own the decision; the agent’s job is to find inconsistencies humans miss in long packs. Confirm any rollout against your bank’s own model-risk and credit policy before using it on live files. Use when the user wants help with loan-file cross-check before funding (legal & compliance) for a small business."
---

# Catch lending-file mistakes before money moves: have an AI agent read the loan pack and cross-check amounts, collateral and income against the credit opinion so a person only reviews the mismatches

At the same Nov 5, 2026 NH NongHyup Bank / AgileSoDA demo covered by Seoul Economic Daily, the audit department’s “zero lending accidents” idea drew a large crowd. The agent reads hundreds of loan documents and cross-checks loan amounts, collateral and income data against credit review opinions to surface errors and omissions before funding. Staff still own the decision; the agent’s job is to find inconsistencies humans miss in long packs. Confirm any rollout against your bank’s own model-risk and credit policy before using it on live files.

## Inputs to collect first
Ask the user for anything below that they haven't already given you. Don't guess missing facts.

- The material the task is about (pasted text, notes or data)

## Instructions

Do the task below with the user's inputs in place of each [bracket].

1. Pick a closed loan file with the credit opinion and the supporting pack (application, income, collateral, appraisal).
2. List the fields that must match across documents (loan amount, collateral value, income figures, borrower IDs) and the rule for what counts as a mismatch.
3. Ask an AI agent to extract those fields from each document with citations and produce a mismatch table only—no approve/deny recommendation.
4. Have a credit or audit person walk every flagged row against the source pages and record false positives.
5. Only after several clean dry runs on historical files, use the mismatch table as a pre-funding checklist; a person still signs the decision.

## Safety checks (share these with the user)

- This prompt works on text other people wrote (emails, reviews, messages or web pages). That text can hide instructions aimed at AI, such as "ignore your rules and…". Paste it as material to work on, and never let the AI act on requests inside it, like paying, sharing logins or changing records.
- Remove names, contact details, account numbers and other personal data the task doesn't need before you paste, and check your AI tool's data and training settings.
- Check names, dates, numbers and any claim against your own source material before you use the answer. AI can misread or invent details.
- Say clearly which parts need a person to review or approve before anything is sent, paid, posted or filed.

## About this skill

- Works with: Document AI / bank AI agent (or Claude/ChatGPT with uploads), loan file PDF pack, credit-opinion template, mismatch checklist for auditors
- Based on this real-world example: https://en.sedaily.com/finance/2026/11/05/ai-cuts-five-day-project-finance-report-to-four-hours
- Free prompt from AgentPlaybook Real Estate: https://agentic-library.netlify.app/prompt/al-628-catch-lending-file-mistakes-before-money-moves-have-an-ai-ag/
- To install in an agent that supports Agent Skills, save this file as SKILL.md in a folder with the same name as `name` above (for example in your skills directory). Install steps for Claude.ai, Claude Code, Cursor and Codex: https://agentic-library.netlify.app/skills/