Journal of Advances in Developmental Research

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Role of Knowledge Graphs in Finance for Fraud Detection and Compliance

Author(s) Pavan Kumar Mantha
Country United States
Abstract The financial sector now relies on a complicated network of linked data. This complexity might help new ideas come up, but it also gives criminals a chance to set up complicated fraud rings and do illegal things. Sadly, conventional fraud detection methods and machine learning models that use siloed datasets and rigid rules often fall short, missing out on little connections and generating too many false positives. This paper looks at how knowledge graphs are being used as a powerful new way of doing things. Knowledge graphs let advanced graph analytics find hidden patterns, cut down on false alerts, and give clear and correct insights by modeling financial information as a network of nodes and edges. We will look at how this technology is being used across significant areas of the financial sector, like credit card fraud, anti-money laundering (AML), and customer risk scoring. We will use real-world examples and statistics to show why this technology is quickly becoming necessary for banks and other financial institutions.
Keywords knowledge graphs, graph databases, financial services, fraud detection, anti-money laundering, regulatory compliance, graph algorithms, data lineage.
Field Engineering
Published In Volume 14, Issue 1, January-June 2023
Published On 2023-06-07
Cite This Role of Knowledge Graphs in Finance for Fraud Detection and Compliance - Pavan Kumar Mantha - IJAIDR Volume 14, Issue 1, January-June 2023. DOI 10.71097/IJAIDR.v14.i1.1574
DOI https://doi.org/10.71097/IJAIDR.v14.i1.1574
Short DOI https://doi.org/g96275
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