Integrating OCR, Graph Databases, and ETL in Fraud Detection: A Novel Approach |
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| © 2023 by IJCTT Journal | ||
| Volume-71 Issue-6 |
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| 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, https://doi.org/10.14445/22312803/IJCTT-V71I6P111
Abstract
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.
Keywords
Extract-Transform-Load, Fraud detection, Graph databases, Machine Learning, Optical Character Recognition.
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