How to build a Loan File Review Agent

TL;DR

This agent automates the review of loan and credit application documents, detects inconsistencies and fraud risks using AI, and routes high-risk cases for human review while logging low-risk cases for record-keeping.

What It Does:

• Ingests and processes uploaded loan application documents (including scanned files with OCR).

• Analyzes documents with an AI model trained to spot inconsistencies, fraud indicators, and risk factors.

• References a knowledge base of fraud indicators and performs web searches for up-to-date verification.

• Classifies applications as high-risk or low-risk using an AI routing node.

• Automatically notifies reviewers via email for high-risk applications.

• Logs low-risk applications to Google Drive for compliance and tracking.

Who It’s For:

• Loan officers and underwriters

• Credit risk teams

• Financial institutions and banks

• Compliance and fraud detection teams

Time to Value:

• Immediate: Upload documents and get a risk assessment, summary, and routing decision in minutes—no manual review required.

Output:

• For high-risk applications:

  • Detailed AI findings and recommendations

  • Automated email alert to the reviewer

• For low-risk applications:

  • AI summary and risk assessment

  • Record automatically created in Google Drive

Common Pain Points for Application Review

• Manual review is slow, error-prone, and inconsistent

• Fraud indicators are often missed due to volume or lack of expertise

• High-risk cases may not be escalated promptly

• Record-keeping for compliance is tedious

• Difficulty in keeping up with new fraud tactics and up-to-date information

What This Agent Delivers

• Automated, consistent document analysis and risk detection

• Real-time fraud indicator referencing and web verification

• Clear, actionable summaries and recommendations

• Instant routing of high-risk cases to human reviewers

• Automated record-keeping for low-risk cases

• Reduced manual workload and faster decision-making

Step-by-Step Build (StackAI Nodes)

1) Files Node (doc-0)

What it does:

• Lets the user upload underwriting summaries or Excel models.

• Automatically extracts and processes text (including OCR for scanned docs).

Goal:

• Make all relevant deal data available for AI analysis.

2) LLM Node (llm-0)

What it does:

• Analyzes the uploaded document for compliance with credit policy rules.

• Generates a compliance report (Pass/Fail for each rule, with explanations).

• If any rule fails, drafts a preview of an exception memo.

Goal:

• Instantly flag non-compliant deals and prepare escalation documentation.

Instructions

You are an AI assistant designed to evaluate loan submissions against established credit policy rules. Your responsibilities include:



- Analyzing writing summaries or Excel documents to ensure compliance with defined criteria such as LTV, DSCR, and experience.

- Instantly flagging deals that do not meet internal credit criteria, which helps save several hours in file reviews.

- Providing support to junior staff by streamlining the review process

Prompt

You are an AI assistant designed to evaluate loan submissions against established credit policy rules.

Credit Policy:

- Maximum LTV: 75%

- Minimum DSCR: 1.25X

- Minimum Borrower Experience: 2 years

- Approved Markets: CA, TX, FL, NY

- Max Loan Size: $10M

Instructions:

1. Analyze the uploaded underwriting summary or Excel model ({{doc-0.documents}}).

2. Check if the deal meets each credit policy criterion above.

3. For each category (LTV, DSCR, Experience, Market, Loan Size), state Pass/Fail and provide a brief explanation.

4. If any category fails, generate a preview of an exception memo that summarizes the failed criteria, the context, and recommended next steps for escalation to the credit committee.

5. Clearly separate the compliance report and the exception memo preview in your output.

Begin your analysis below:

3) LLM Node (Refinement & Email) (llm-1)

What it does:

• Refines the exception memo draft.

• (If configured) Sends the refined memo to a designated reviewer via email.

Goal:

• Ensure exception memos are clear, professional, and reach the right person without delay.

4) Output Node (out-0)

What it does:

• Presents the compliance report and exception memo to the user.

Goal:

• Deliver actionable results in a user-friendly format.