Asset Based Lending (ABL)

Intelligent and predictive ABL solution

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Our inspiration

Many of our clients in the ABL business use multiple software to capture, manage and analyze their asset data. They use manual means to maintain and monitor their borrowers. Providing a solution that could integrate with Loan IQ and make it easy for the clients to maintain their business seemed like a great opportunity and would mean a win-win situation for both Finastra and the Financial institutions that are Clients of Loan IQ.

ABL application helps the Collateral Analyst to capture the details of the various assets that are pledged with the institution and regularly calculate the Borrowing Base value of the Borrower's Facility. This helps in determining the "available to draw" amount.

How we built it

We started building this application keeping in mind the time to market. The intention was to release a Minimum Viable Product (MVP) in the least time possible so that we could solicit the opinions of the existing ABL users. Hence we had to select a good stack to develop and easily deploy. The result was that we opted to build it in such a way that it could be hosted on FusionFabric.Cloud and be made available all our clients. We used OpenAPI services to handle the maintenance and calculations through an attractive UI built using Angular8. Using the templates suggested by Finastra UXG helped a lot in achieving a standard look and feel to the UI.

Challenges we ran into

This being the first of its kind for Loan IQ, we had to traverse the learning curve very fast in order to produce the proposed MVP. Understanding the functionality and providing the appropriate flows was a bit challenging initially but was mitigated with the help of the Product Architect who guided us in the right direction with his timely inputs.

Accomplishments that we're proud of

Since each of use were working on different layers of the application, we had to set ourselves small, achievable milestones to integrate and verify that we were going in the right direction. In spite of all these challenges and the daily tasks on hand, our three-member team could come out with a good MVP providing most of the basic functionalities that were envisaged. Our two-month long effort was worth it when we got the appreciation of our Engineering leadership forum after we demonstrated the MVP.

What we learned

The development of this application taught us to collaborate well and continuously integrate our work to keep the MVP in a working condition constantly.

What's next for Asset Based Lending (ABL)

The future of the project aims to use Machine Learning to enhance the analytical capabilities of the application. This will help the Collateral Analyst to predict and monitor the trend of his/her borrowers better.

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