print

Scalable and imbalance-resistant machine learning models for anti-money laundering : a two-layered approach

author
Tertychnyi, Pavlo
Slobozhan, Ivan
Ollikainen, Madis
Dumas, Marlon
statement of authorship
Pavlo Tertychnyi, Ivan Slobozhan, Madis Ollikainen and Marlon Dumas
source
Enterprise Applications, Markets and Services in the Finance Industry : 10th International Workshop, FinanceCom 2020, Helsinki, Finland, August 18, 2020, Revised Selected Papers
location of publication
Cham
publisher
Springer Science and Business Media Deutschland GmbH
year of publication
2020
pages
p. 43-58
series
Lecture notes in business information processing ; 401
conference name, date
10th International Workshop on Enterprise Applications, Markets and Services in the Finance Industry, FinanceCom 2020, 18 August 2020
conference location
Helsinki, Finland
url
https://doi.org/10.1007/978-3-030-64466-6_3
subject term
tehisõpe
rahapesu
andmekogud
mudelid
Scopus
Conference Proceedings at Scopus
Article at Scopus
kvartiil
Q3
category (general)
Business, management and accounting
Decision sciences
Engineering
Mathematics
Computer science
category (sub)
Business, management and accounting. Business and international management
Decision sciences. Information systems and management
Business, management and accounting. Management information systems
Engineering. Control and systems engineering
Mathematics. Modeling and simulation
Computer science. Information systems
keyword
anti-money laundering
machine learning
ISSN
1865-1348
ISBN
978-303064465-9
notes
Bibliogr.: 20 ref
scientific publication
teaduspublikatsioon
classifier
3.1
TTÜ department
küberneetika instituut
language
inglise
Tertychnyi, P., Slobozhan, I., Ollikainen, M., Dumas, M. Scalable and imbalance-resistant machine learning models for anti-money laundering : a two-layered approach // Enterprise Applications, Markets and Services in the Finance Industry : 10th International Workshop, FinanceCom 2020, Helsinki, Finland, August 18, 2020, Revised Selected Papers. Cham : Springer Science and Business Media Deutschland GmbH, 2020. p. 43-58. (Lecture notes in business information processing ; 401). https://doi.org/10.1007/978-3-030-64466-6_3