MongoDB and Hasura for Modern Fintech Services

Solution Overview

In the rapidly transforming fintech sector, financial services providers require several application features that address evolving customer needs and meet the following requirements:

• Process high-volume real-time transactions

• Map complex data relationships across customer profiles, financial instruments, and privacy requirements

• Meet regulatory compliance requirements with geographical data residency constraints

• Adapt to volume spikes and new product offerings with scalable systems

• Provide real-time analytics for risk assessment and fraud detection

To address these challenges, this solution uses Hasura and MongoDB to build a modern fintech application with the following tools:

• Hasura Data Delivery Network: Hasura DDN provides a data access layer for advanced applications and AI. You can use Hasura DDN to build and deploy a secure, federated API layer on your data.

• MongoDB Atlas: MongoDB Atlas' document model, horizontal scalability through sharding, and performance optimizations provide security and reliability in your financial applications.

Reference Architectures

This reference architecture enables you to build fintech applications that handle high transaction volumes while maintaining data integrity, security, and regulatory compliance.

The following diagram shows the multi-regional deployment of Hasura DDN with MongoDB clusters:

Figure 1. Hasura DDN with MongoDB architecture diagram

In this diagram, client applications connect through a global load balancer to multiple Hasura DDN regions, which interact with MongoDB Atlas clusters across multiple geographic locations. The architecture also incorporates centralized authentication and access control, AI and analytics capabilities, global compliance, security measures, and integration with external financial services.

The following layers make up the key components of this solution's architecture:

• Data layer: MongoDB Atlas clusters for primary data storage

• API and access layer: Hasura DDN for data access and real-time subscriptions

• Application layer: Fintech services and applications

• Security layer: Authentication and authorization services

• Analytics layer: Data processing and ML services

Data Model Approach

MongoDB's document-based structure addresses the unique requirements of financial applications. The flexible schema design allows organizations to adapt quickly to new financial products or regulatory requirements without disruptive schema migrations.

The following example shows a customer document with embedded account information:

{
  "_id": ObjectId("5f8a7b2b9d3b2e5a7c8b4567"),
  "customerId": "C10045678",
  "customerInfo": {
    "name": "Jane Smith",
    "contactDetails": {
      "email": "jane.smith@example.com",
      "phone": "+1-555-123-4567",
      // PII fields can utilize MongoDB's field-level encryption
    },
    "kycStatus": "verified",
    "riskProfile": "moderate"
  },
  "accounts": [
    {
      "accountId": "A200387645",
      "accountType": "savings",
      "balance": 45678.92,
      "currency": "USD",
      "status": "active",
      "createdDate": ISODate("2022-03-15T10:30:00Z")
    },
    {
      "accountId": "A200456789",
      "accountType": "investment",
      "balance": 125000.00,
      "currency": "USD",
      "status": "active",
      "createdDate": ISODate("2022-08-22T14:45:00Z")
    }
  ]
}

This example document demonstrates the following MongoDB capabilities:

• Support sharding strategies for high-volume data by customer ID or date ranges for time-series transaction data

• Provide storage optimization for performance

• Grant role-based access control and field-level security

Build the Solution

This architecture focuses on security, performance, and scalability. The MongoDB data layer interacts with the Hasura DDN API layer while addressing authentication, security policies, and compliance requirements.

Key Learnings

• Distributed architecture improves performance: MongoDB's distributed clusters with Hasura DDN's regional deployment achieve microsecond-level latency for trading platforms and high-frequency transactions while maintaining data consistency globally.

• Multi-layered security protects applications: A multi-layered strategy combines centralized and decentralized security. Hasura's permission systems and MongoDB's field-level security provide protection while maintaining flexibility for regulatory requirements like GDPR, PSD2, and MiFID II.

• Flexible data models enable innovation: MongoDB's schema flexibility with Hasura's GraphQL API generation allows fintech organizations to deploy new offerings without extensive redevelopment.

• Real-time capabilities enhance customer experiences: This architecture supports real-time data subscriptions and complex relationship mapping for personalized banking, fraud detection, and risk assessment using complete customer data views.

• Incremental modernization reduces risk: The API-first approach allows organizations to transition from legacy systems by creating a modern data access layer while maintaining existing data sources.

Authors

• Jon Mills, Hasura

• Aditi Phadke, Hasura

• Asawari Samant, Hasura

• Adam Malone, Hasura

• Kenneth Stott, Hasura

• Sebastian Rojas Arbulu, MongoDB

Learn More

• Fraud Detection Accelerator Using AWS SageMaker

• Fraud Prevention and AML Using Atlas Vector Search

• Open Finance Data Store