|Title / Titel||Analytical Fraud Detection|
|Abstract (PDF, 14 KB)|
|Summary / Zusammenfassung||The goal of this project is to survey data mining methods to detect traces of suspicious acts in financial institutes. More precisely, we try to detect fraud in collaboration with a bank.
The relational nature of transactions justifies the use of relational data mining methods.
As positive training examples are not at hand, a system is currently built that allows fraud experts to visualize transaction data in various ways and to find and annotate suspicious transaction patterns using their implicit and longtime know-how.
Those transaction patterns will provide a basis for research. The most promising approaches will be built into the system and search for “hot spots”, which are then delivered to human experts for closer investigations.
|Keywords / Suchbegriffe||machine learning, fraud, automatic detection|
|Project leadership and contacts /
Projektleitung und Kontakte
|Funding source(s) /
|Private Sector (e.g. Industry)
|Duration of Project / Projektdauer||Jan 2005 to Jan 2010|