Data Science Economy 2019: Dawn of AI
2+ keynotes / 30+ talks
delivered from experts & practitioners

16th & 17th of May 2019, @KRAŠ Auditorium, Zagreb, Croatia
IT Auditor, Banca Intesa Beograd, Serbia

Vladimir Marković


Vladimir has comprehensive experience in DW/BI design and development primarily based on Microsoft and SAS BI platforms and products. This experience is further bolstered by years of working in implementation and development different kinds of DW/BI solutions and products.
During his work in the bank, he’s gained broad business background in different fields of BI application especially in analytical customer intelligence, credit risk scoring, credit risk portfolio management and accounting.
He is an experienced trainer and presenter. He enjoys sharing enthusiasm by presenting and promoting DW/BI at courses, user groups, technical events and conferences.
Vladimir holds a MSc in Math and Computer Science from Faculty of Mathematics, University of Belgrade. Areas of his interest are dimensional modeling and data mining. He plays chess in his free time.


Can unsupervised machine learning help in fraud detection?

Fraud is a „profitable“ business and it is increasing every year. Traditional techniques of fraud detection are complex, time-consuming and request domain knowledge like business practice, finance, economics, low etc.

Well-designed applications have readable application logs, and well-described business processes in terms of data. Usually, we use all available in the development of a mathematical model that identifies anomalies in client/employee behavior
The presentation will show UML techniques such as path analysis and various types of segmentation that can help in detecting anomalies in a client/employee behavior.


How unsupervised machine learning help in fraud detection?

  • Introduction to internal fraud – some cases
  • SAS Visual Investigator demo
  • How to use advanced analytics in reducing sample of suspicious
    • About anomalies
    • Assumptions – choice of the method
    • Some techniques in detecting anomalies
  • Steps in detecting collective anomalies using application logs in practice
    • Data preparation
    • Find behavioral patterns
    • Make „fingerprint“ for each user
    • Analyze the fingerprints and detect collective anomalies
  • Visualization of results
  • Some tips
  • Using results in SAS Fraud Solution