We started our engagement by first conducting an elaborate discovery phase. As part of the discovery phase, we did a deep dive into the project requirements, the different third party systems that the application needed to be integrated to, and understanding of Fundation’s proprietary algorithm that determines who qualifies for a loan and on what terms.
While conducting the discovery phase, the following challenges became quite apparent to us.
Complex Third-Party Integrations - The application we developed needed to integrate with several third-party systems that helped verify the identity of the business owner and provide vital information on his/her credit worthiness. Each third-party system came with its own set of documentation, Application Programming Interfaces (APIs), and challenges.
Additionally, each system had its authentication guidelines and security protocols that we needed to adhere to, adding another measure of complexity to the project. This meant that our team had to grasp the requirements of each system fully; some integrations required SSL (Secure Socket Layer) implementation, while communication with others needed data to be encrypted to ensure that information exchange was secure.
Multi-factor Loan Approval - Fundation's proprietary algorithm, which determined which applicants qualified for the loan and on what terms was complex and analyzed multiple parameters and conditions:
At the end of discovery phase, we put together a 150-page document that had confidential project blueprint information comprising the following.
Parameters for loan approval:
Loan Terms:
At the end of discovery phase, we put together a 150-page document that had confidential project blueprint information comprising the following.
Considering that this is an application for the banking & finance domain, threshold for error is very low. However, the strong logical reasoning and problem solving skills intrinsic to our team helped implement Fundation's proprietary algorithm without a hitch.
After thoroughly going through the different APIs and system documentation, as well as the authentication/security protocol requirements for each third-party system, we successfully integrated with and gathered information necessary for approval/servicing of the loan.
We connected with
The information gathered in steps 1-4 above is fed to Fundation’s proprietary algorithm to determine if a user qualifies for the loan. Additionally, auxiliary non-numeric information (for example, customer’s reason for the loan) that played a role in the approval process required manual intervention from a credit analyst at Fundation. Loans requested towards working capital, inventory, capital improvements, equipment purchases, and expansion of facilities had a higher preference over loans requested for business acquisitions and ownership related events.