Integrating OCR, Graph Databases, and ETL in Fraud Detection: A Novel Approach

© 2023 by IJCTT Journal
Volume-71 Issue-6
Year of Publication : 2023
Authors : Saikiran Subbagari
DOI :  10.14445/22312803/IJCTT-V71I6P111

How to Cite?

Saikiran Subbagari, "Integrating OCR, Graph Databases, and ETL in Fraud Detection: A Novel Approach," International Journal of Computer Trends and Technology, vol. 71, no. 6, pp. 63-68, 2023. Crossref,

Fraud detection remains an essential aspect of maintaining integrity in various sectors, especially in financial services. This paper explores the integrated use of Optical Character Recognition (OCR), Extract-Transform-Load (ETL) processes, and Graph Databases for advanced and efficient fraud detection. OCR enables data extraction from unstructured sources, while ETL processes ensure that this data is cleaned, validated, and structured for analysis. Graph Databases further enhance the system's efficiency by representing complex relationships between data entities and supporting sophisticated queries, leading to uncovering hidden fraudulent patterns. However, the system does face limitations such as potential inaccuracies from OCR, resource-intensiveness of ETL, the complexity of fraudulent patterns, and risks of false positives and negatives. To address these limitations, the paper highlights potential future research directions, including improving OCR accuracy, enhancing ETL processes, generating dynamic graph queries using machine learning, and optimizing the balance between precision and recall. The study concludes that the integration of OCR, ETL, and Graph Databases offers a promising approach in the ongoing battle against fraudulent activities, albeit necessitating continuous evolution and innovation.

Extract-Transform-Load, Fraud detection, Graph databases, Machine Learning, Optical Character Recognition.


[1] Aaisha Makkar, and Neeraj Kumar, “PROTECTOR: An Optimized Deep Learning-based Framework for Image Spam Detection and Prevention,” Future Generation Computer Systems, vol. 125, pp. 41-58, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Mahmoud Hamido, Abdallah Mohialdin, and Ayman Atia, “The Use of Background Features, Template Synthesis and Deep Neural Networks in Document Forgery Detection,” 2023 International Conference on Artificial Intelligence in Information and Communication, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Tesseract OCR. [Online]. Available:
[4] Neo4j Graph Database. [Online]. Available:
[5] Ian Robinson, Jim Webber, and Emil Eifrem, Graph Databases: New Opportunities for Connected Data, O'Reilly Media, 2015.
[Google Scholar] [Publisher Link]
[6] L. Wang et al., Fraud Detection Using Neo4j Graph Database,” Proceedings of the 2019 2nd International Conference on Industrial Artificial Intelligence, pp. 21-26, 2019.
[7] A. Bulusu, C.A. Gunter, and S. Venkataraman, “Fraud Detection using a Graph Database,” Proceedings of the 2019 IEEE 12th International Conference on Cloud Computing, 277-284, 2019.
[8] Ralph Kimball, and Margy Ross, The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, John Wiley & Sons, 2013.
[Google Scholar] [Publisher Link]
[9] W.H. Inmon, Building the Data Warehouse, John Wiley & Sons, 2005.
[Google Scholar] [Publisher Link]
[10] Athira Nambiar, and Divyansh Mundra, “An Overview of Data Warehouse and Data Lake in Modern Enterprise Data Management,” Big Data Cognitive Computing, vol. 6, no. 4, p. 132, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Tahereh Pourhabibi et al., “Fraud Detection: A Systematic Literature Review of Graph-based Anomaly Detection Approaches,” Decision Support Systems, vol. 133, p. 113303, 2020.
[CrossRef] [Google Scholar] [Publisher Link] .
[12] Abdulalem Ali et al., “Financial Fraud Detection Based on Machine Learning: A Systematic Literature Review,” Applied Sciences, vol. 12, no. 19, p. 9637, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Marina Sokolova, and Guy Lapalme, “A Systematic Analysis of Performance Measures for Classification Tasks,” Information Processing & Management, vol. 45, no. 4, pp. 427-437, 2009.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Aurelien Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, O'Reilly Media, 2019.
[Publisher Link]
[15] Michael Mannino, Sa Neung Hong, and In Jun Choi, “Efficiency Evaluation of Data Warehouse Operations,” Decision Support Systems, vol. 44, no. 4, pp. 883-898, 2008.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Peter C. Verhoef, “Understanding the Effect of Customer Relationship Management Efforts on Customer Retention and Customer Share Development,” Journal of Marketing, vol. 67, no. 4, 2003.
[CrossRef] [Google Scholar] [Publisher Link]
[17] D.E. Shasha, Database Tuning - A Principled Approach, Prentice-Hall, Hoboken, 1992.
[Google Scholar] [Publisher Link]
[18] Patricia Craja, Alisa Kim, and Stefan Lessmann, “Deep Learning for Detecting Financial Statement Fraud,” Decision Support Systems, vol. 139, p. 113421, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Saikiran Subbagari, “Leveraging Optical Character Recognition Technology for Enhanced Anti-Money Laundering (AML) Compliance,” SSRG International Journal of Computer Science and Engineering, vol. 10, no. 5, pp. 1-7, 2023.
[CrossRef] [Publisher Link]
[20] Yann LeCun, Yoshua Bengio, and Geoffrey Hinton, “Deep Learning,” Nature, vol. 521, pp. 436–444, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Ge Zhang et al., “FRAUDRE: Fraud Detection Dual-Resistant to Graph Inconsistency and Imbalance,” 2021 IEEE International Conference on Data Mining, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Roberta Galici et al., “Applying the ETL Process to Blockchain Data. Prospect and Findings,” Information, vol. 11, no. 4, p. 204, 2020.
[CrossRef] [Google Scholar] [Publisher Link]