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Basic Track
Student Track
Professional Track
German
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AI Meets Finance
About the Challenge
Data
Simulated data of transactions between thousands of clients (11k) in a mobile banking network.
Most agents behave based on statistical model of normal behavior.
But a few are committing fraud…
Task
Identify the fraudster accounts based on their transactional behavior
For the student track, an initial set of features is provided, but finding new, more informative ones, is incentivized!
The fraudster populations for the student and professional tracks are different.
Outcome
Profesional Track
Teilnehmende Teams:
23
Einreichungen:
160
Top 5 Scores
1. 99.92% —
Spider Bobs
— Ali Salehzadeh-Yazdi, Eda Cakir, Johannes Falk
2. .99.76% —
Import teamName
— Ayden Janssen, Robyn Sophie Kruse
3. 99.61% —
OldSchool
— Yale Hartmann
4. 92.14% —
Kornstante
— Fabian Wetjen, Wilhelm Jochim, Marcel Plutat
5. 76.68% —
IntroToAML
— Eike Voß, Tom Splittgerber
Student Track
Teilnehmende Teams:
51
Einreichungen:
222
Top 5 Scores
1. 99.83% —
Import teamName
— Ayden Janssen, Robyn Sophie Kruse
2. 97.05% —
Data Alchemy
— Tobias Fricke
3. 88.95% —
:^)
— Arne Winter
4. 85.64% —
DataForcer
– Abhirup Sihna, Pritilata Saha
5. 82.15% —
BitBubenBande
— Jann Eike Ackermann, Joshua, Aning, Max Lehmann
