How to build an Application Risk 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 users upload loan application documents (PDFs, scans, etc.)

• Extracts and processes text, including OCR for scanned files

Goal:

• Provide clean, structured document content for AI analysis

2) OpenAI LLM Node (llm-0)

What it does:

• Analyzes the extracted document content using a specialized AI prompt

• Detects inconsistencies, summarizes findings, flags risks, and recommends next steps

• References a knowledge base of fraud indicators and uses web search for verification

Goal:

• Deliver a comprehensive, AI-driven risk assessment and summary

Instructions

You are a verifier for loan and credit applications. 
  Your responsibilities include reviewing all documents related to a sale application to identify inconsistencies. 
  Common inconsistencies include mismatched names, conflicting income figures, missing documents, altered pay stubs, inflated appraisals, shell company sellers, employment discrepancies, 
  unexplained large deposits, inconsistent employment dates, and discrepancies between bank statements and declared income

Prompt

You are a verifier for loan and credit applications. Review the provided documents related to a sale application, including financial statements, job letters, income statements, recommendation letters, previous bank accounts, bank statements, and pay stubs. Identify and list any inconsistencies, such as mismatched names, conflicting income figures, missing documents, altered pay stubs, inflated appraisals, shell company sellers, employment discrepancies, unexplained large deposits, inconsistent employment dates, or discrepancies between bank statements and declared income.



For each inconsistency found, summarize your findings in a table that includes:

- The inconsistency

- Recommended next steps

- Major flags and risks, with a determination of the severity for each



Flag the user as either "high risk" or "low risk" for AI routing based on your assessment.



If additional fraud indicators are needed, consult the knowledge base. For up-to-date information (such as addresses or banks), perform a web search and include relevant findings.



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3) AI Routing Node (airouting-0)

What it does:

• Reads the AI’s findings and classifies the application as “high-risk” or “low-risk”

Goal:

• Automate the decision of whether to escalate or log the application

4) Send Email Action Node (action-0)

What it does:

• If high-risk, automatically sends an email alert to the reviewer with the AI’s findings

Goal:

• Ensure high-risk cases are escalated to a human for further review

5) Create File in Google Drive Action Node (action-1)

What it does:

• If low-risk, creates a record in Google Drive (e.g., a CSV file) for compliance and tracking

Goal:

• Automate record-keeping for low-risk applications

6) Output Nodes (out-0, out-1)

What they do:

• Present the AI’s findings and routing decision to the user

Goal:

• Provide clear, actionable output for both high- and low-risk cases