Migration to Thought Machine's Value CoreMigration to Thought Machine's Value Core

Fintech

BUSINESS CHALLENGE

The client’s business was functioning on a semi-digital legacy system and needed to be transformed into a fully digital banking service provider.

• The legacy system required to be upgraded to Thought Machine’s Vault core, a microservice-based, fully cloud-native solution.
• The existing system, constructed using PL/SQL and hosted to on-prem servers, posed challenges for scalability and required increased maintenance efforts.
• The transactions between the merchants and the customers were complex and fragmented.
• The client wanted to provide their customers with a BNPL (Buy Now Pay Later) feature and easy tracking for previous loan applications.
• Tracking the applications of the existing customers who applied for BNPL and providing spending limit insights according to their historical limits.
• The affiliated partners used the BNPL via the client's platform and wanted to simplify the process of assigning loan limits and disbursement.

We harnessed event-driven microservice-based architecture to migrate the data from their legacy system to an AWS database.

• Our efforts using Kafka and AWS S3 resulted in secure and encrypted data migration to the new system.
• We divided the legacy system into several smaller sections to make the process seamless.
• To maintain a smooth transition, we decided to retain a few features from the legacy system.
• We successfully integrated a one-platform solution for the client to make partner transactions simpler.
• There were multiple analyses on different datasets added to the BNPL product.
• For testing purposes, data was migrated between lower environments.

Amazon Web Services | Python | Apache Kafka | Apache Spark

Migrating to a microservice-based architecture enabled us to implement a more flexible and agile development approach in deploying and managing the system.

• This ensured that the system remained responsive and scalable, even as the number of users and transactions increased.
• The system now responds more quickly and efficiently to large sets of data and user activity changes.
• By meticulously devising and implementing our data migration strategy, we ensured flawless and uninterrupted migration of sensitive data of millions of users.
• The microservice-based architecture by driving independent service updates, incremental deployment, and scalability minimized downtime since changes to one service do not disrupt the entire system.