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

Ayushi Patil1, Shreya Mahajan1, Jinal Menpara1

  • 1Artificial Intelligence & Machine Learning Department, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, Maharashtra 412115, India.

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This study introduces a graph-based machine learning model for detecting banking fraud. The model effectively identifies fraudulent transactions across various banking operations, enhancing financial security in the digital age.

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Area of Science:

  • Computer Science
  • Data Science
  • Financial Technology

Background:

  • The banking sector's digital transformation increases fraud risks.
  • Traditional fraud detection methods struggle with evolving cyber threats.
  • There is a critical need for advanced fraud detection and prevention systems.

Purpose of the Study:

  • To implement a state-of-the-art Graph Database approach for detecting fraudulent transactions.
  • To develop and evaluate a graph-based machine learning model for real-time fraud detection.
  • To enhance the security of banking operations against sophisticated fraud tactics.

Main Methods:

  • Modeling relational features using Neo4j graph database.
  • Applying graph-based machine learning for anomaly and pattern detection.
  • Evaluating system performance using Accuracy, Recall, False Positive Rate, and ROC curves.

Main Results:

  • The proposed graph-based model demonstrates effectiveness in detecting fraudulent activities.
  • The approach provides immediate analysis of transactions and user behavior.
  • The methodology offers a reliable framework for evaluating fraud detection systems.

Conclusions:

  • Innovative technologies like graph databases are crucial for modern fraud detection.
  • The study validates the effectiveness and reliability of the proposed graph-based machine learning model.
  • Financial institutions can leverage this approach to strengthen their fraud prevention strategies.