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NEVA: Visual Analytics to Identify Fraudulent Networks.

Roger A Leite1, Theresia Gschwandtner1, Silvia Miksch1

  • 1Faculty of Informatics Vienna University of Technology (TU Wien) Vienna Austria.

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|November 2, 2020
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Summary

NEVA, a visual analytics tool, helps financial institutions detect complex fraud networks by analyzing customer relationships. This approach reduces errors in fraud detection, enhancing security and trust.

Keywords:
financial fraud detectionvisual analyticsvisualization

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Area of Science:

  • Financial Technology
  • Data Science
  • Network Analysis

Background:

  • Financial institutions face challenges with trust, reputation, security, and quality.
  • Traditional analytical methods struggle with evolving, ill-defined fraudulent behaviors.
  • Detecting complex fraud networks requires analyzing more than individual customer transactions.

Purpose of the Study:

  • To introduce NEVA (Network dEtection with Visual Analytics), a visual analytics environment.
  • To support the analysis of customer networks for improved fraud detection.
  • To reduce false-negative and false-positive fraud alarms.

Main Methods:

  • Utilizing multiple coordinated views for data exploration.
  • Implementing a guidance-enriched component for network pattern generation, detection, and filtering.
  • Analyzing relationships between nodes at various complexity levels.

Main Results:

  • NEVA facilitates the exploration of complex data relations and dependencies.
  • The system supports analyzing customer networks to identify fraudulent actors.
  • Expert interviews confirmed the applicability and usability of NEVA.

Conclusions:

  • NEVA enhances the detection of complex fraud networks through visual analytics.
  • The tool effectively reduces inaccuracies in fraud detection alarms.
  • NEVA offers a robust solution for improving financial security and trust.