Interest Rate Web Loader

The Interest Rate Web Loader was a solution to a current problem of only being able to enter Interest Rate Trades into our system through an Excel uploader. The user would have to fill out an extremely large Excel template, then upload it. This application allowed the user to do this process directly on the web. We were able to add functionality such as copying previously added trades, information blurbs, uploading confirmation, building and editing a trade schedule, and much more into this application which could not be handled using the previous method.

The basic process a user will perform is they will take a trade confirmation (pdf that has trade details), enter those details into the system, adjust the built schedule as needed, attach the confirm and save.

Step 1: Gather information, trade confirmations and talk to subject matter experts.

Step 2: Whiteboard out potential workflows for the process.

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Step 3: Wireframe workflows.

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Step 4: Build Axure prototypes. We went down two different paths. The first was looking at using natural language processing where the user could upload the confirm and using NLP, it would find the needed inputs from the upload. The second path was a more manual approach, where the user would fill out a series of forms. We ended up going with the ladder due to timing (release in 6, 2 week sprints.

NLP Prototype

Stepped Workflow Prototype: Landing page

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Stepped Workflow Prototype: Step 2

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Stepped Workflow Prototype: Step 4

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Stepped Workflow Prototype: Step 1

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Stepped Workflow Prototype: Step 3

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Stepped Workflow Prototype: Step 5

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Stepped Workflow Prototype: Trade Entered

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Step 5: Test prototype with internal users, then review and update the stepped workflow prototype.

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Step 6: Test with external users. We worked with the business side to line up 5 clients that were willing to perform testing on the prototypes. A separate prototype was built for each client which used their actual data for a more personal experience.

Step 7: Made any necessary changes to the prototype based on feedback.

Step 8: Created Google analytics callouts.

Step 9: Worked with developers to get everything into production.

Step 10: Gathered user engagement. I built a data studio report for the project to monitor analytics and we added a Hotjar survey to the application.

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