AI-Driven Mortgage POS Modernization Using AWS and Next.js

Client Overview

A leading digital lending company is looking to modernize its Mortgage POS platform with AI-driven workflows, improve customer experience, reduce loan processing time, and build a scalable data platform for analytics and compliance.

Business Objectives

Provide a digital-first mortgage origination journey
Leverage AI for faster underwriting and verification
Enable near real-time document processing and insights
Reduce human intervention in data extraction and eligibility checks
Build a scalable, secure infrastructure on AWS

Solution Overview

A modern Mortgage POS built using Next.js, powered by an AWS Data Lake, AI-based Underwriter, and automated verification agents using Amazon SageMaker, AWS Glue, and Athena.

Tech Stack

Frontend: React.js
Backend: .NET Microservices
Data Platform: AWS S3 (Data Lake – Raw, Trusted, Curated Layers)
ETL/ELT: AWS Glue
Security: IAM, KMS, Cognito, VPC

Analytics: Amazon Athena, QuickSight (optional)
AI/ML: Amazon SageMaker (Underwriter model, OCR, document classification)
Orchestration & Automation: AWS Step Functions, Lambda
Storage: S3 (layered architecture)

Key Components & Use Cases

AI Underwriter Engine
1. Built using SageMaker
2. Trained on historical loan data (cleaned in Trusted Layer)
3. Performs:
- Risk scoring
- DTI (Debt-to-Income) validation
- Pre-qualification predictions
- Custom rule engine for loan eligibility
Algorithm: LightGBM or XGBoost (Tabular data)
Document Verification Agent
1. Handles paystubs, W-2s, and, IDs
2. Uses:
- SageMaker (OCR models)
- Textract or Comprehend (optional for NLP)
3. Extracts:
- Employer name
- Gross income
- SSN match
4. Sends verification result to the POS frontend in real time
Algorithm: Textract + Custom XGBoost model

AWS Data Lake Architecture
- Raw Layer: Stores unstructured or ingested data (e.g., documents, uploaded forms)
- Trusted Layer: Cleaned & validated data (e.g., normalized employment data)
- Curated Layer: Ready-to-consume datasets for AI training or reports
Data Pipelines with AWS Glue
1. Glue Crawlers define schema from uploaded loan packages
2. ETL scripts transform raw data into trusted datasets
3. Supports:
- Incremental loading
- Error handling
- Audit trails
Querying with Athena
1. Athena queries the Trusted and Curated layers
2. Generates insights for:
- Loan status reports
- Compliance checks
- Underwriter performance metrics

Benefits Delivered

Area
Underwriting
Document Processing
Audit & Compliance
Customer Experience
Scalability & Cost

Benefit
60% faster pre-qualification decisions
80% automation of identity/income validation
Real-time logs via Athena and Glue logs
40% increase in loan completion rate
Serverless data lake reduced infrastructure cost by 30%

Future Scope

Add an LLM-based Chatbot for borrower assistance
Integration with credit bureaus (Experian, Equifax)
Real-time fraud detection using anomaly models
Mortgage-backed securities reporting for the secondary market

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AI-Driven Mortgage POS Modernization Using AWS and Next.js

Client Overview

Business Objectives

Solution Overview

Tech Stack

Key Components & Use Cases

Benefits Delivered

Future Scope

Get in touch with us, let’s create value together.

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