Mortgage processing is a complex, document-heavy workflow that demands accuracy, efficiency, and compliance. Traditional mortgage operations rely on manual review, rule-based automation, and disparate systems, often leading to delays, errors, and a poor customer experience. Recent industry surveys indicate that only about half of borrowers express satisfaction with the mortgage process, with traditional banks trailing non-bank lenders in borrower satisfaction. This gap in satisfaction level is largely attributed to the manual, error-prone nature of traditional mortgage processing, where delays, inconsistencies, and fragmented workflows create frustration for borrowers and impact overall experience.
In this post, we introduce agentic automatic mortgage approval, a next-generation sample solution that uses autonomous AI agents powered by Amazon Bedrock Agents and Amazon Bedrock Data Automation. These agents orchestrate the entire mortgage approval process—intelligently verifying documents, assessing risk, and making data-driven decisions with minimal human intervention. By automating complex workflows, businesses can accelerate approvals, accelerate approvals, minimize errors, and provide consistency while enhancing scalability and compliance.
The following video shows this agentic automation in action—enabling smarter, faster, and more reliable mortgage processing at scale.
Why agentic IDP?
Agentic intelligent document processing (IDP) revolutionizes document workflows by driving efficiency and autonomy. It automates tasks with precision, enabling systems to extract, classify, and process information while identifying and correcting errors in real time.
Agentic IDP goes beyond simple extraction by grasping context and intent, adding deeper insights to documents that fuel smarter decision-making. Powered by Amazon Bedrock Data Automation, it adapts to changing document formats and data sources, further reducing manual work.
Built for speed and scale, agentic IDP processes high volumes of documents quickly, reducing delays and optimizing critical business operations. Seamlessly integrating with AI agents and enterprise systems, it automates complex workflows, cutting operational costs and freeing teams to focus on high-value strategic initiatives.
IDP in mortgage processing
Mortgage processing involves multiple steps, including loan origination, document verification, underwriting, and closing; with each step requiring significant manual effort. These steps are often disjointed, leading to slow processing times (weeks instead of minutes), high operational costs (manual document reviews), and an increased risk of human errors and fraud. Organizations face numerous technical challenges when manually managing document-intensive workflows, as depicted in the following diagram.
These challenges include:
Document overload – Mortgage applications require verification of extensive documentation, including tax records, income statements, property appraisals, and legal agreements. For example, a single mortgage application might require manual review and cross-validation of hundreds of pages of tax returns, pay stubs, bank statements, and legal documents, consuming significant time and resources.
Data entry errors – Manual processing introduces inconsistencies, inaccuracies, and missing information during data entry. Incorrect transcription of applicant income from W-2 forms or misinterpreting property appraisal data can lead to miscalculated loan eligibility, requiring costly corrections and rework.
Delays in decision-making – Backlogs resulting from manual review processes extend processing times and negatively affect borrower satisfaction. A lender manually reviewing income verification and credit documentation might take several weeks to work through their backlog, causing delays that result in lost opportunities or frustrated applicants who turn to competitors.
Regulatory compliance complexity – Evolving mortgage industry regulations introduce complexity into underwriting and verification procedures. Changes in lending regulations, such as new mandatory disclosures or updated income verification guidelines, can require extensive manual updates to processes, leading to increased processing times, higher operational costs, and elevated error rates from manual data entry.
These challenges underscore the need for automation to enhance efficiency, speed, and accuracy for both lenders and mortgage borrowers.
Solution: Agentic workflows in mortgage processing
The following solution is self-contained and the applicant only interacts with the mortgage applicant supervisor agent to upload documents and check or retrieve application status. The following diagram illustrates the workflow.
The workflow consists of the following steps:
Applicant uploads documents to apply for a mortgage.
The supervisor agent confirms receipt of documents. Applicant can view and retrieve application status.
The underwriter updates the status of the application and sends approval documents to applicant.
At the core of the agentic mortgage processing workflow is a supervisor agent that orchestrates the entire workflow, manages sub-agents, and makes final decisions. Amazon Bedrock Agents is a capability within Amazon Bedrock that lets developers create AI-powered assistants capable of understanding user requests and executing complex tasks. These agents can break down requests into logical steps, interact with external tools and data sources, and use AI models to reason and take actions. They maintain conversation context while securely connecting to various APIs and AWS services, making them ideal for tasks like customer service automation, data analysis, and business process automation.
The supervisor agent intelligently delegates tasks to specialized sub-agents while maintaining the right balance between automated processing and human supervision. By aggregating insights and data from various sub-agents, the supervisor agent applies established business rules and risk criteria to either automatically approve qualifying loans or flag complex cases for human review, improving both efficiency and accuracy in the mortgage underwriting process.
In the following sections, we explore the sub-agents in more detail.
Data extraction agent
The data extraction agent uses Amazon Bedrock Data Automation to extract critical insights from mortgage application packages, including pay stubs, W-2 forms, bank statements, and identity documents. Amazon Bedrock Data Automation is a generative AI-powered capability of Amazon Bedrock that streamlines the development of generative AI applications and automates workflows involving documents, images, audio, and videos. The data extraction agent helps make sure that the validation, compliance, and decision-making agent receives accurate and structured data, enabling efficient validation, regulatory compliance, and informed decision-making. The following diagram illustrates the workflow.
The extraction workflow is designed to automate the process of extracting data from application packages efficiently. The workflow includes the following steps:
The supervisor agent assigns the extraction task to the data extraction agent.
The data extraction agent invokes Amazon Bedrock Data Automation to parse and extract applicant details from the application packages.
The extracted application information is stored in the extracted documents Amazon Simple Storage Service (Amazon S3) bucket.
The Amazon Bedrock Data Automation invocation response is sent back to the extraction agent.
Validation agent
The validation agent cross-checks extracted data with external resources such as IRS tax records and credit reports, flagging discrepancies for review. It flags inconsistencies such as doctored PDFs, low credit score, and also calculates debt-to-income (DTI) ratio, loan-to-value (LTV) limit, and an employment stability check. The following diagram illustrates the workflow.
The process consists of the following steps:
The supervisor agent assigns the validation task to the validation agent.
The validation agent retrieves the applicant details stored in the extracted documents S3 bucket.
The applicant details are cross-checked against third-party resources, such as tax records and credit reports, to validate the applicant’s information.
The third-party validated details are used by the validation agent to generate a status.
The validation agent sends the validation status to the supervisor agent.
Compliance agent
The compliance agent verifies that the extracted and validated data adheres to regulatory requirements, reducing the risk of compliance violations. It validates against lending rules. For example, loans are approved only if the borrower’s DTI ratio is below 43%, making sure they can manage monthly payments, or applications with a credit score below 620 are declined, whereas higher scores qualify for better interest rates. The following diagram illustrates the compliance agent workflow.
The workflow includes the following steps:
The supervisor agent assigns the compliance validation task to the compliance agent.
The compliance agent retrieves the applicant details stored in the extracted documents S3 bucket.
The applicant details are validated against mortgage processing rules.
The compliance agent calculates the applicant’s DTI ratio, applying corporate policy and lending rules to the application.
The compliance agent uses the validated details to generate a status.
The compliance agent sends the compliance status to the supervisor agent.
Underwriting agent
The underwriting agent generates an underwriting document for the underwriter to review. The underwriting agent workflow streamlines the process of reviewing and finalizing underwriting documents, as shown in the following diagram.
The workflow consists of the following steps:
The supervisor agent assigns the underwriting task to the underwriting agent.
The underwriting agent verifies the information and creates a draft of the underwriting document.
The draft document is sent to an underwriter for review.
Updates from the underwriter are sent back to the underwriting agent.
RACI matrix
The collaboration between intelligent agents and human professionals is key to efficiency and accountability. To illustrate this, we’ve crafted a RACI (Responsible, Accountable, Consulted, and Informed) matrix that maps out how responsibilities might be shared between AI-driven agents and human roles, such as compliance officers and the underwriting officer. This mapping serves as a conceptual guide, offering a glimpse into how agentic automation can enhance human expertise, optimize workflows, and provide clear accountability. Real-world implementations will differ based on an organization’s unique structure and operational needs.
The matrix components are as follows:
R: Responsible (executes the work)
A: Accountable (owns approval authority and outcomes)
C: Consulted (provides input)
I: Informed (kept informed of progress/status)
End-to-end IDP automation architecture for mortgage processing
The following architecture diagram illustrates the AWS services powering the solution and outlines the end-to-end user journey, showcasing how each component interacts within the workflow.
In Steps 1 and 2, the process begins when a user accesses the web UI in their browser, with Amazon CloudFront maintaining low-latency content delivery worldwide. In Step 3, Amazon Cognito handles user authentication, and AWS WAF provides security against malicious threats. Steps 4 and 5 show authenticated users interacting with the web application to upload required documentation to Amazon S3. The uploaded documents in Amazon S3 trigger Amazon EventBridge, which initiates the Amazon Bedrock Data Automation workflow for document processing and information extraction.
In Step 6, AWS AppSync manages user interactions, enabling real-time communication with AWS Lambda and Amazon DynamoDB for data storage and retrieval. Steps 7, 8, and 9 demonstrate how the Amazon Bedrock multi-agent collaboration framework comes into play, where the supervisor agent orchestrates the workflow between specialized AI agents. The verification agent verifies uploaded documents, manages data collection, and uses action groups to compute DTI ratios and generate an application summary, which is stored in Amazon S3.
Step 10 shows how the validation agent (broker assistant) evaluates the application based on predefined business criteria and automatically generates a pre-approval letter, streamlining loan processing with minimal human intervention. Throughout the workflow in Step 11, Amazon CloudWatch provides comprehensive monitoring, logging, and real-time visibility into all system components, maintaining operational reliability and performance tracking.
This fully agentic and automated architecture enhances mortgage processing by improving efficiency, reducing errors, and accelerating approvals, ultimately delivering a faster, smarter, and more scalable lending experience.
Prerequisites
You need to have an AWS account and an AWS Identity and Access Management (IAM) role and user with permissions to create and manage the necessary resources and components for this solution. If you don’t have an AWS account, see How do I create and activate a new Amazon Web Services account?
Deploy the solution
To get started, clone the GitHub repository and follow the instructions in the README to deploy the solution using AWS CloudFormation. The deployment steps offer clear guidance on how to build and deploy the solution. After the solution is deployed, you can proceed with the following instructions:
After you provision all the stacks, navigate to the stack AutoLoanAPPwebsitewafstackXXXXX on the AWS CloudFormation console.
On the Outputs tab, locate the CloudFront endpoint for the application UI.
You can also get the endpoint using the AWS Command Line Interface (AWS CLI) and the following command:
aws cloudformation describe-stacks
–stack-name $(aws cloudformation list-stacks
–stack-status-filter CREATE_COMPLETE UPDATE_COMPLETE | jq -r ‘.StackSummaries[] | select(.StackName | startswith(“AutoLoanAPPwebsitewafstack”)) | .StackName’)
–query ‘Stacks[0].Outputs[?OutputKey==`configwebsitedistributiondomain`].OutputValue’
–output text
Open the (https://.cloudfront.net) in a new browser.
You should see the application login page.
Create an Amazon Cognito user in the user pool to access the application.
Sign in using your Amazon Cognito email and password credentials to access the application.
Monitoring and troubleshooting
Consider the following best practices:
Monitor stack creation and update status using the AWS CloudFormation console or AWS CLI
Monitor Amazon Bedrock model invocation metrics using CloudWatch:
InvokeModel requests and latency
Throttling exceptions
4xx and 5xx errors
Check Amazon CloudTrail for API invocations and errors
Check CloudWatch for solution-specific errors and logs:
aws cloudformation describe-stacks —stack-name
Clean up
To avoid incurring additional costs after testing this solution, complete the following steps:
Delete the relevant stacks from the AWS CloudFormation console.
Verify the S3 buckets are empty before deleting them.
Conclusion
The sample automated loan application sample solution demonstrates how you can use Amazon Bedrock Agents and Amazon Bedrock Data Automation to transform mortgage loan processing workflows. Beyond mortgage processing, you can adapt this solution to streamline claims processing or address other complex document-processing scenarios. By using intelligent automation, this solution significantly reduces manual effort, shortens processing times, and accelerates decision-making. Automating these intricate workflows helps organizations achieve greater operational efficiency, maintain consistent compliance with evolving regulations, and deliver exceptional customer experiences.
The sample solution is provided as open source—use it as a starting point for your own solution, and help us make it better by contributing back fixes and features using GitHub pull requests. Browse to the GitHub repository to explore the code, click watch to be notified of new releases, and check the README for the latest documentation updates.
As next steps, we recommend assessing your current document processing workflows to identify areas suitable for automation using Amazon Bedrock Agents and Amazon Bedrock Data Automation.
For expert assistance, AWS Professional Services and other AWS Partners are here to help.
We’d love to hear from you. Let us know what you think in the comments section, or use the issues forum in the repository.
About the Authors
Wrick Talukdar is a Tech Lead – Generative AI Specialist focused on Intelligent Document Processing. He leads machine learning initiatives and projects across business domains, leveraging multimodal AI, generative models, computer vision, and natural language processing. He speaks at conferences such as AWS re:Invent, IEEE, Consumer Technology Society(CTSoc), YouTube webinars, and other industry conferences like CERAWEEK and ADIPEC. In his free time, he enjoys writing and birding photography.
Jady Liu is a Senior AI/ML Solutions Architect on the AWS GenAI Labs team based in Los Angeles, CA. With over a decade of experience in the technology sector, she has worked across diverse technologies and held multiple roles. Passionate about generative AI, she collaborates with major clients across industries to achieve their business goals by developing scalable, resilient, and cost-effective generative AI solutions on AWS. Outside of work, she enjoys traveling to explore wineries and distilleries.
Farshad Bidanjiri is a Solutions Architect focused on helping startups build scalable, cloud-native solutions. With over a decade of IT experience, he specializes in container orchestration and Kubernetes implementations. As a passionate advocate for generative AI, he helps emerging companies leverage cutting-edge AI technologies to drive innovation and growth.
Keith Mascarenhas leads worldwide GTM strategy for Generative AI at AWS, developing enterprise use cases and adoption frameworks for Amazon Bedrock. Prior to this, he drove AI/ML solutions and product growth at AWS, and held key roles in Business Development, Solution Consulting and Architecture across Analytics, CX and Information Security.
Jessie-Lee Fry is a Product and Go-to Market (GTM) Strategy executive specializing in Generative AI and Machine Learning, with over 15 years of global leadership experience in Strategy, Product, Customer success, Business Development, Business Transformation and Strategic Partnerships. Jessie has defined and delivered a broad range of products and cross-industry go- to-market strategies driving business growth, while maneuvering market complexities and C-Suite customer groups. In her current role, Jessie and her team focus on helping AWS customers adopt Amazon Bedrock at scale enterprise use cases and adoption frameworks, meeting customers where they are in their Generative AI Journey.
Raj Jayaraman is a Senior Generative AI Solutions Architect at AWS, bringing over a decade of experience in helping customers extract valuable insights from data. Specializing in AWS AI and generative AI solutions, Raj’s expertise lies in transforming business solutions through the strategic application of AWS’s AI capabilities, ensuring customers can harness the full potential of generative AI in their unique contexts. With a strong background in guiding customers across industries in adopting AWS Analytics and Business Intelligence services, Raj now focuses on assisting organizations in their generative AI journey—from initial demonstrations to proof of concepts and ultimately to production implementations.
