AzureFeeds

Scenario

Once a loan application is submitted, financial institutions must process a variety of supporting documents—including pay stubs, tax returns, credit reports, and bank statements—before a loan can be approved. This post-application phase is often fragmented and manual, involving data retrieval from multiple systems, document verification, eligibility calculations, packet compilation, and signing. Each step typically requires coordination between underwriters, compliance teams, and loan processors, which can stretch the processing time to several weeks.

This solution automates the post-application loan processing workflow using Azure services and Generative AI agents. Intelligent agents retrieve and validate applicant data, extract and summarize document contents, calculate loan eligibility, and assemble structured, compliant loan packets ready for signing. Orchestrated using Azure AI Foundry, the system ensures traceable agent actions and responsible AI evaluations. Final loan documents and metrics are stored securely for compliance and analytics, with Power BI dashboards enabling real-time visibility for underwriters and operations teams.

Architecture:

Workflow Description:

The loan processing architecture leverages a collection of specialized AI agents, each designed to perform a focused task within a coordinated, intelligent workflow. From initial document intake to final analytics, these agents interact seamlessly through an orchestrated system powered by Azure AI Foundry, GPT-4o, Azure Functions and the Semantic Kernel. The agents not only automate and accelerate individual stages of the process but also communicate through an A2A layer to share critical context—enabling efficient, accurate, and transparent decision-making across the pipeline. Below is a breakdown of each agent and its role in the system.

It all begins at the User Interaction Layer, where a Loan Processor or Underwriter interacts with the web application. This interface is designed to be simple, intuitive, and highly responsive to human input.

1. As soon as a request enters the system, it’s picked up by the Triage Agent, powered by GPT-4o or GPT-4o-mini. This agent acts like a smart assistant that can reason through the problem and break it down into smaller, manageable tasks. For example, if the user wants to assess a new applicant, the Triage Agent identifies steps like verifying documents, calculating eligibility, assembling the loan packet, and so on.


2. Next, the tasks are routed to the Coordinator Agent, which acts as the brains of the operation. Powered by Azure Functions & Sematic Kernel, this agent determines the execution order, tracks dependencies, and assigns each task to the appropriate specialized agent.


3. The very first action that the Coordinator Agent triggers is the Applicant Profile Retrieval Agent. This agent taps into Azure AI Search, querying the backend to retrieve all relevant data about the applicant — previous interactions, submitted documents, financial history, etc. This rich context sets the foundation for the steps that follow.


4. Once the applicant profile is in place, the Coordinator Agent activates a set of specialized agents, as outlined to perform specialized tasks as per the prompt received in the interaction layer. Below is the list of specialized agents:

a. Documents Verification Agent: This agent checks and verifies the authenticity and completeness of applicant-submitted documents as part of the loan process.
Powered by: GPT-4o

b. Applicant Eligibility Assessment Agent: It evaluates whether the applicant meets the criteria for loan eligibility based on predefined rules and document content.
Powered by: GPT-4o

c. Loan Calculation Agent: This agent computes loan values and terms based on the applicant’s financial data and eligibility results.
Powered by: GPT-4o

d. Loan Packet Assembly Agent: This agent compiles all verified data into a complete and compliant loan packet ready for submission or signing.
Powered by: GPT-4o

e. Loan Packet Signing Agent: It handles the digital signing process by integrating with DocuSign and ensures all necessary parties have executed the loan packet.
Powered by: GPT-4o

f. Analytics Agent: This agent connects with Power BI to update applicant status and visualize insights for underwriters and processors.
Powered by: GPT-4o

Components

Here are the key components of your Loan Processing AI Agent Architecture:

1. Azure OpenAI GPT-4o/GPT 4o mini: Advanced multimodal language model. Used to summarize, interpret, and generate insights from documents, supporting intelligent automation. Empowers agents in this architecture with contextual understanding and reasoning.


2. Azure AI Foundry Agent Service: Agent orchestration framework. Manages the creation, deployment, and lifecycle of task-specific agents—such as classifiers, retrievers, and validators—enabling modular execution across the loan processing workflow.


3. Semantic Kernel: Lightweight orchestration library. Facilitates in-agent coordination of functions and plugins. Supports memory, chaining of LLM prompts, and integration with external systems to enable complex, context-aware behavior in each agent.


4. Azure Functions: Serverless compute for handling triggers such as document uploads, user actions, or decision checkpoints. Initiates agent workflows, processes events, and maintains state transitions throughout the loan processing pipeline.


5. Azure Cosmos DB: Globally distributed NoSQL database used for agent memory and context persistence. Stores conversation history, document embeddings, applicant profile snapshots, and task progress for long running or multi-turn workflows.


6. Agentic Content Filters: Responsible AI mechanism for real-time filtering. Evaluates and blocks sensitive or non-compliant outputs generated by agents using customizable guardrails.


7. Agentic Evaluations: Evaluation framework for agent workflows. Continuously tests, scores, and improves agent outputs using both automatic and human-in-the-loop metrics.


8. Power BI: Business analytics tool that visualizes loan processing stages, agent outcomes, and applicant funnel data. Enables real-time monitoring of agent performance, SLA adherence, and operational bottlenecks for decision makers.


9. Azure ML Studio: Code-first development environment for building and training machine learning models in Python. Supports rapid iteration, experimentation, and deployment of custom models that can be invoked by agents.

Security Considerations:  

• Web App: For web applications, access control and identity management can be done using App Roles, which determine whether a user or application can sign in or request an access token for a web API. For threat detection and mitigation, Defender for App Service leverages the scale of the cloud to identify attacks targeting apps hosted on Azure App Service.


• Azure AI Foundry: Azure AI Foundry supports robust identity management using Azure Role-Based Access Control (RBAC) to assign roles within Microsoft Entra ID, and it supports Managed Identities for secure resource access. Conditional Access policies allow organizations to enforce access based on location, device, and risk level. For network security, Azure AI Foundry supports Private Link, Managed Network Isolation, and Network Security Groups (NSGs) to restrict resource access. Data is encrypted in transit and at rest using Microsoft-managed keys or optional Customer-Managed Keys (CMKs). Azure Policy enables auditing and enforcing configurations for all resources deployed in the environment. Additionally, Microsoft Entra Agent ID, which extends identity management and access capabilities to AI agents. Now, AI agents created within Microsoft Copilot Studio and Azure AI Foundry are automatically assigned identities in a Microsoft Entra directory centralizing agent and user management in one solution. AI Security Posture Management can be used to assess the security posture of AI workloads.  Purview APIs enable Azure AI Foundry and developers to integrate data security and compliance controls into custom AI apps and agents. This includes enforcing policies based on how users interact with sensitive information in AI applications. Purview Sensitive Information Types can be used to detect sensitive data in user prompts and responses when interacting with AI applications.


• Cosmos DB: Azure Cosmos DB enhances network security by supporting access restrictions via Virtual Network (VNet) integration and secure access through Private Link. Data protection is reinforced by integration with Microsoft Purview, which helps classify and label sensitive data, and Defender for Cosmos DB to detect threats and exfiltration attempts. Cosmos DB ensures all data is encrypted in transit using TLS 1.2+ (mandatory) and at rest using Microsoft-managed or customer-managed keys (CMKs).


• Power BI: Power BI leverages Microsoft Entra ID for secure identity and access management. In Power BI embedded applications, using Credential Scanner is recommended to detect hardcoded secrets and migrate them to secure storage like Azure Key Vault. All data is encrypted both at rest and during processing, with an option for organizations to use their own Customer-Managed Keys (CMKs). Power BI also integrates with Microsoft Purview sensitivity labels to manage and protect sensitive business data throughout the analytics lifecycle. For additional context, Power BI security white paper – Power BI | Microsoft Learn

Related Scenarios

1. Financial Institutions: Banks and credit unions can streamline customer onboarding by using agentic services to autofill account paperwork, verify identity, and route data to compliance systems. Similarly, signing up for credit cards and applying for personal or business loans can be orchestrated through intelligent agents that collect user input, verify eligibility, calculate offers, and securely generate submission packets—just like in the proposed loan processing model.

2. Healthcare: Healthcare providers can deploy a similar agentic architecture to simplify patient intake by pre-filling forms, validating insurance coverage in real-time, and pulling medical history from existing systems securely. Agents can reason over patient inputs and coordinate backend workflows, improving administrative efficiency and enhancing the patient experience.

3. University Financial Aid/Scholarships: Universities can benefit from agentic orchestration for managing financial aid processes—automating the intake of FAFSA or institutional forms, matching students with eligible scholarships, and guiding them through complex application workflows. This reduces manual errors and accelerates support delivery to students.

4. Car Dealerships’ Financial Departments: Agentic systems can assist car dealerships in handling non-lot inventory requests, automating the intake and validation of custom vehicle orders. Additionally, customer loan applications can be processed through AI agents that handle verification, calculation, and packet assembly—mirroring the structure in the loan workflow above.

5. Commercial Real Estate: Commercial real estate firms can adopt agentic services to streamline property research, valuations, and loan application workflows. Intelligent agents can pull property data, fill out required financial documents, and coordinate submissions, making real estate financing faster and more accurate.

6. Law: Law firms can automate client onboarding with agents that collect intake data, pre-fill compliance documentation, and manage case file preparation. By using AI Foundry to coordinate agents for documentation, verification, and assembly, legal teams can reduce overhead and increase productivity.

Contributors:

This article is maintained by Microsoft. It was originally written by the following contributors.  

Principal authors:  

1. Manasa Ramalinga| Principal Cloud Solution Architect – US Customer Success 


2. Oscar Shimabukuro Kiyan| Senior Cloud Solution Architect – US Customer Success 


3. Abed Sau | Principal Cloud Solution Architect – US Customer Success 


4. Matt Kazanowsky | Senior Cloud Solution Architect – US Customer Success