Enhancing fraud detection in banking by integration of graph databases with machine learning

Abstract

The banking sector's shift from traditional physical locations to digital channels has offered customers unprecedented convenience and increased the risk of fraud for customers and institutions alike. In this study, we discuss the pressing need for robust fraud detection & prevention systems in the context of evolving technological environments. We introduce a graph-based machine learning model that is specifically designed to detect fraudulent activity in various types of banking operations, such as credit card transactions, debit card transactions, and online banking transactions. This model uses advanced methods for anomalies, behaviors, and patterns to analyze past transactions and user behavior almost immediately. We provide an in-depth methodology for evaluating fraud detection systems based on parameters such as Accuracy Recall rate and False positive rate ROC curves. The findings can be used by financial institutions to develop and enhance fraud detection strategies as they demonstrate the effectiveness and reliability of the proposed approach. This study emphasizes the critical role that innovative technologies play in safeguarding the financial sector from the ever-changing strategies of fraudsters while also enhancing banking security. • This paper aims to implement the detection of fraudulent transactions using a state-of-the-art Graph Database approach. • The relational graph of features in the dataset used is modelled using Neo4J as a graph database. • Applying JSON features from the exported graph to various Machine Learning models, giving effective outcomes.

Keywords

• Fintech
• Bank transactions
• Machine learning algorithms
• Fraud detection
• Online banking