Exploring Modern Techniques: A Survey of Bank Statement Analysis Methodologies

Exploring Modern Techniques: A Survey of Bank Statement Analysis Methodologies

1         Satyam S. Suryawanshi, BTech, Sandip University

2         Nayan N. Mhatre, BTech, Sandip University

3         Siom P. Rajput, BTech, Sandip University

4         Hariom D. Mishra, BTech, Sandip University

5         Uday V. Tawde, BTech,

Guide: Prof. Y. R. Bhalerao , Sandip University

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1.    Abstract
Bank statement analysis is pivotal for financial decision-making, risk assessment, and fraud detection. The rise of web technologies has transformed how financial institutions approach this analysis. This survey scrutinizes web development methodologies in bank statement analysis, systematically assessing various approaches, frameworks, and tools.

We initially underscore the importance of bank statement analysis in financial institutions and elucidate challenges in traditional methods. Subsequently, we explore the impact of web technologies on enhancing analysis efficiency, accessibility, and security.

The survey offers a comprehensive examination of web development frameworks like Angular, React, and Vue.js, gauging their suitability for building robust bank statement analysis platforms.

Moreover, we investigate the integration of machine learning and data analytics techniques within web applications, enabling automated pattern recognition, anomaly detection, and predictive modelling. We also emphasize security and privacy considerations for safeguarding sensitive financial data in web-based systems.

This survey aims to illuminate current trends, challenges, and future directions in web-based bank statement analysis. Its findings provide valuable insights for developers, researchers, and financial institutions seeking to leverage web technologies for efficient and reliable analysis systems in the digital age.

Keywords:

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, Docker, AWS EC2 service for hosting