13 mar, 18:00–19:30

INSTÄLLT – Open source fraud prevention using network analysis

Learn to use social network algorithms to prevent fraud. With the introduction of social network analysis (SNA), investigators are now able to detect data patterns to detect potential crime ring or group. In this session we are going showcase KNIME analytics platform and graph database OrientDB. Join us at Goto 10.

Increasingly sophisticated fraudsters are able to easily slip behind risk-score based analysis to avoid detection and, to overcome this issue, organizations need to better understand the dynamics and patterns of fraud and fraud networks. This is where the visual and analytical capabilities of social network analysis (SNA) can help the fraud prevention function to effectively detect and prevent fraud originating from web-based and other more traditional business channels. The use of networked data in fraud detection becomes increasingly important to uncover fraudulent patterns and to detect in real-time when certain processes show some characteristics of irregular activities. Although many analyses typically focus in the first place on fraud detection, the emphasis should shift towards fraud prevention, i.e. detecting fraud before it is even committed. As fraud is a time-evolving phenomenon, social network algorithms (SNA) succeed to keep ahead of new types of fraud and to adapt to changing environments and surrounding effects.

Agenda:

18:00 – 18:05: Introduction
18:05 – 18:15: KNIME intro by Johan Tornborg
18:15 – 18:45: Fraud detection use cases by Artem Ryasik
18:45 – 19:15: Snacks and Drinks and Networking

Kontakt

Johan Tornborg
johan.tornborg@redfield.se

  • Pris: Kostnadsfritt
  • Arrangör: Redfield AB
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