Scalable and Imbalance-Resistant Machine Learning Models for Anti-money Laundering: A Two-Layered Approach

Author:

Tertychnyi Pavlo,Slobozhan Ivan,Ollikainen Madis,Dumas Marlon

Publisher

Springer International Publishing

Reference20 articles.

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3. Kotsiantis, S., Koumanakos, E., Tzelepis, D., Tampakas, V.: Forecasting fraudulent financial statements using data mining. Int. J. Comput. Intell. 3(2), 104–110 (2006)

4. Jayasree, V., Siva Balan, R.V.: Money laundering regulatory risk evaluation using bitmap index-based decision tree. J. Assoc. Arab Univ. Basic Appl. Sci. 23(1), 96–102 (2017)

5. Nielsen, D.: Tree boosting with XGBoost - why does XGBoost win “every” machine learning competition? Master’s Thesis, NTNU (2016)

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