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fintech-b2b-invoice-management-applications's Introduction

FinTech B2B Invoice Management Application

Product Requirements Specification

Idea about the problem-

The B2B world operates differently from the B2C or C2C world. Businesses work with other businesses on credit. When a buyer business orders goods from the seller business, the seller business issues an invoice for the same. This invoice for the goods contains various information like the details of the goods purchased and when it should be paid. This is known in accounting terminology as “Accounts Receivable”.

Seller business interacts with various businesses and sells goods to all of them at various times. Hence, the seller business needs to keep track of the total amount it owes from all the buyers. This involves keeping track of all invoices from all the buyers. Each invoice will have various important fields like a payment due date, invoice date, invoice amount, baseline date etc.

The buyer business needs to clear its amount due before the due date. However, in real-world scenarios, the invoices are not always cleared ie. paid in full amount by the due date. The date on which a customer clears the payment for an invoice is called the payment date.

The objective of the Web Application is:

  • To build a Full-stack Invoice Management Application using ReactJS, JDBC, Java, Servlets.
  • Build a responsive Receivables Dashboard.
  • Visualize Data in the form of grids.
  • Visualize Data in the form of graphs.
  • Perform Searching operations on the invoices.
  • Add & Edit data in the editable fields of the grid.
  • Delete data of selected rows in predefined templates.

The objectives for ML is:

  • View the invoice data from various buyers.
  • See various fields/attributes of the invoice(s) from a particular buyer.
  • Perform Data Pre-processing on the invoice data.
  • Get account-level analytics to easily visualize and interpret data- EDA and Feature Engineering.
  • Get a prediction of when the invoice is going to get paid.

Tools Used

  • Visual Studio Code: Visual Studio Code was used for developing the front end of the web application using reactJS.
  • Node.JS, create_react_app: Node.js is an open-source, cross-platform, back-end JavaScript runtime environment that runs on the V8 engine and executes JavaScript code outside a web browser. create_react_app Node.JS package was used to set up a project to run a react application.
  • ReactJS: React is a front-end JavaScript library for building user interfaces based on UI components. It was used to develop the front-end of the web application.
  • Eclipse, JAVA, JDBC - Java Database Connectivity : Eclipse was used to develop the backend of the web application using Java and JDBC.
  • SqlYOG: SQLYOG was used to set and manage the database.
  • Jupyter Notebook, Anaconda Navigator: Jupyter Notebook and Anaconda python environment were used to develop the code for ML.

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