Bank Statement Analyser

Overview

This application analyses bank statements and provides analytical reports on the account expenses. This is a personal project to get an idea about my expenses and sharpen my knowledge on the following set of technologies :

• Python Flask framework
• Computer Vision (Google's Tesseract OCR)
• Pdf to Image python package (Pdf2image)
• Machine Learning (Scikit Learn) to predict transaction categories
• Azure SQL Database to store transaction data and user login details
• PowerBI visualisations

Behind the scenes, the app extracts transaction details from documents, predicts a category for each transaction, and upload the data to a SQL database linked to interactive PowerBI visualisations.

Demo

https://bsa-demo.azurewebsites.net/
Username : admin
Password : password123

Visualisations are generated from ~3000 transaction samples which can be viewed in transaction.db

There are three main screens to the application :

• Transaction details
• Dashboard views
• Statements upload

Transaction details

Dashboard selection screen

Dashboard view (Total spending)

Filtered Dashboard by Year and Category

Read further to create your own App

Initial Setup

The following steps are required to link your own data to the displayed visualisations

• Create a SQL Database with the tables suggested below *
• Replace the database connection variables in parameters.py
• In each PowerBI file (.pbix), set up a DirectQuery to the database
• Upload .pbix files to PowerBI Service and create sharable links (Publish to Web)
• Insert each link in the corresponding html template

* SQL Tables :

A transaction table containing transaction records

CREATE TABLE transactions (
    ID varchar(255) NOT NULL PRIMARY KEY,
    Date datetime NOT NULL,
    Value float,
    Category varchar(255),
	Reference varchar(255)
);

A users table containing login details

CREATE TABLE users (
    id INTEGER NOT NULL PRIMARY KEY Identity(1, 1),
    username VARCHAR(15) UNIQUE,
	email VARCHAR(50) UNIQUE,
	password VARCHAR(80)
);

Run the app locally

In the command prompt, run the following :

1. Install Docker

   $ pip install docker
   

2. Verify Docker installation

   $ docker version
   

3. cd into the bsa-app folder

   $ cd full/path/to/bsa-app
   

4. Build docker image

   $ docker build -t bsa_image:latest .
   

5. Generate and Run a container

   $ docker run -p 5000:5000 bsa_image:latest
   

6. Visit http://localhost:5000/