The Challenge

A number of third-party services needed to be efficiently integrated in order to fetch and store the data needed for the credit decisioning process.

How We Solved It

Integration services were developed using Python3.6 as AWS functions. This configuration leveraged and maximized benefits such as concurrency, faster deployments using the Chalice framework (including versioning and zero-time deployments). The services were called from a decision-making service using built-in Python multithreading package which enabled faster data availability. The code was delivered as CI ready with 95-100% test coverage. Built-in packages usage kept the codebase lightweight for faster deployments and easy maintenance.

The Results

Sphere’s integrations allowed CreditNinja to improve their decision model. The result – significant financial savings and risk reduction because untrusted customers are filtered out early in the application stages. In addition, the reliability of the system became more reliable because of improved test coverage.