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Automated Financial Data Analysis Tool Using VueJS, Laravel, and Python

This project involves the development of a data analysis tool using VueJS, Laravel, and a Python backend to analyze and identify inconsistencies in financial data received from various institutions. The tool aims to automate the process of detecting and reporting discrepancies, saving time and resources for financial institutions.

Technical Architecture

The project employs a layered architecture, with VueJS serving as the front-end interface, Laravel acting as the back-end API framework, and Python handling the data analysis tasks.

VueJS Front-End:

• Provides a user-friendly interface for uploading and submitting financial data files.
• Displays processing information and status updates to the user.
• Presents the generated inconsistency reports to the user upon completion.

Laravel Back-End:

• Receives data file uploads from the VueJS front-end.
• Handles authentication and authorization for user access.
• Communicates with the Python backend to initiate data analysis tasks.
• Retrieves and formats the generated inconsistency reports from the Python backend.

Python Data Analysis:

• Extracts and processes data from uploaded files.
• Applies predefined rules to identify and flag inconsistencies within the data.
• Generates detailed reports outlining the identified inconsistencies.

Project Methodology

The project initially adopted a top-down approach, focusing on creating the VueJS front-end interface first. As the project progressed and larger data sets were tested, the need to optimize memory consumption and processing time arose. This prompted a shift in focus to optimize the Python data analysis pipeline.

Data Security and Confidentiality

To adhere to confidentiality and privacy regulations, the tool employs a strict data minimization approach. No data is stored on the server, ensuring that no personal or sensitive information is retained. Files are uploaded, processed, and reports are generated directly, without any intermediate data storage. Upon completion, all traces of processed data are securely deleted.

Project Benefits

The developed data analysis tool offers several benefits to financial institutions:

• • Automated Inconsistency Detection:

-The tool simplifies and streamlines the process of identifying inconsistencies in financial data, saving time and resources for analysts.

• • Real-Time Insights:

-The tool provides real-time feedback on the processing status and generated reports, enabling timely identification and resolution of issues.

• • Enhanced Data Quality:

-By detecting and addressing inconsistencies early on, the tool contributes to maintaining data integrity and improving overall data quality.

• • Compliance with Data Privacy Regulations:

-The tool’s strict data minimization approach ensures compliance with privacy regulations and protects sensitive customer data.