Case study

We Integrated Our ML-Based Solutions Into One of Our Fintech Client’s Systems, Enabling Faster Response Time and Ease of Maintenance

SG Analytics DA Fintech Case Study We integrated our ML-based solutions


The client engaged us to set up and integrate ML-based solutions into its system.


  • We proposed multiple end-to-end architectures with a feasibility study and pricing details to provide options to client targeting to cater to the specific needs of low cost and maintenance alongside high-security standards.
  • Apart from access and high-security standards, the major challenge we faced was providing real-time model results for streaming data. To resolve this, we divided the process into chunks of serverless micro-services and event services, building the bridge between need, speed, and security. 
  • We automated all the workflows via code templates to strictly minimize external efforts and avoid any manual interference in a production environment.
  • We simplified heavy technical architectures and handy, detailed documentation to ease business communication.


We established a bi-weekly sprint framework where we utilized Jira boards provided by the client, which allowed us to effectively manage and prioritize tasks, ensuring the timely completion of deliverables.


  • We streamlined end-to-end architecture with fault tolerance in place. 
  • We implemented the recommendation engine for new and existing customers by increasing potential customer experience. 
  • We reduced efforts for the client’s Engineering team.


  • With buzz for recommendation engines, recommendations for investment options is the need of the hour. Personalized investment recommendations cater to audience experience leading to higher user retention.
  • Rather than a monolithic approach, micro-services provide better reliability to incorporate versioning changes.
  • Financial data needs to be highly secured; following high-security standards and encrypting sensitive data are the keys.

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