20.500.12556/DKUM-93187 Uporaba napredne analitike pri odkrivanju in preprečevanju pranja denarja The use of advanced analytics in detecting and preventing money laundering Pranje denarja predstavlja globalni izziv, ki ogroža stabilnost in integriteto finančnega sistema ter spodkopava zaupanje v finančne institucije. Tradicionalni pristopi za preprečevanje pranja denarja, ki temeljijo na vnaprej določenih pravilih, pogosto vodijo do velikega števila lažno pozitivnih opozoril, kar zmanjšuje učinkovitost in povečuje stroške delovanja. Poleg tega vse strožja zakonodaja in regulativne zahteve od finančnih institucij zahtevajo nenehno prilagajanje in izboljševanje nadzornih mehanizmov. Diplomsko delo se uvodoma osredotoča na predstavitev temeljnih pojmov pranja denarja, njegovih značilnih faz in pojava profesionalnega pranja denarja. Predstavljena je tudi vloga ključnih mednarodnih in nacionalnih organizacij, ki delujejo na področju preprečevanja pranja denarja. V nadaljevanju je opredeljena napredna analitika, pri čemer so predstavljeni različni pristopi, kot so podatkovno rudarjenje, analiza sentimenta, klastrska analiza, strojno učenje, vizualizacija podatkov, analiza časovnih serij in analitika velikih podatkov. Poudarjena je njihova uporaba pri zbiranju podatkov, segmentaciji strank, zaznavanju anomalij, prioritetni obravnavi opozoril in vizualni predstavitvi rezultatov. Posebna pozornost je namenjena strukturi in delovanju sistemov za preprečevanje pranja denarja. Predstavljeni so ključni izzivi, kot so visoka stopnja lažno pozitivnih zaznav, potreba po kakovostnih podatkih ter zahteve glede skladnosti z zakonodajo. Obravnavane so tudi ovire pri vključevanju napredne analitike v finančne institucije. V drugem delu je predstavljen trg tehnoloških rešitev za preprečevanje pranja denarja, s posebnim poudarkom na podjetju NICE Actimize kot vodilnem ponudniku. Podrobneje je opisana rešitev NICE Actimize Suspicious Activity Monitoring. Money laundering represents a global challenge that threatens the stability and integrity of the financial system and undermines trust in financial institutions. Traditional approaches to anti-money laundering, based on predefined rules, often result in a high number of false positive alerts, which reduces efficiency and increases operational costs. Moreover, increasingly strict legislation and regulatory requirements demand that financial institutions continuously adapt and improve their monitoring mechanisms. The thesis begins by introducing the fundamental concepts of money laundering, its characteristic stages, and the phenomenon of professional money laundering. It also presents the role of key international and national organizations involved in anti-money laundering efforts. The following section defines advanced analytics, presenting various approaches such as data mining, sentiment analysis, cluster analysis, machine learning, data visualization, time series analysis, and big data analytics. Their applications are highlighted in data collection, customer segmentation, anomaly detection, alert prioritization, and visual representation of results. Special attention is given to the structure and operation of anti-money laundering systems. Key challenges such as the high rate of false positives, the need for high-quality data, and compliance requirements are discussed. Obstacles to the integration of advanced analytics in financial institutions are also addressed. The second part presents the market for technological anti-money laundering solutions, with a special focus on the company NICE Actimize as a leading provider. The NICE Actimize Suspicious Activity Monitoring solution is described in more detail. Preprečevanje pranja denarja napredna analitika NICE Actimize Suspicious Activity Monitoring NICE Actimize Anti-Money Laundering advanced analytics NICE Actimize Suspicious Activity Monitoring NICE Actimize true true false A. Lutar Slovenski jezik Angleški jezik Diplomsko delo/naloga 2025-06-11 20:11:54 2025-10-10 10:29:50 2025-10-11 03:15:04 0000-00-00 00:00:00 2025 0 Maribor 0 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004.9:343.9.024 246142211 UN_Lutar_Ajsa_2025.pdf UN_Lutar_Ajsa_2025.pdf 1 B52B5CB10BC2EB7F8C987B8DA39E7D91 6be87b1b9063f0d7e93c4e1ba8ddc991863e2707861aaefff1a95ba3468727d3 880a697d-58ca-11f0-8567-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=193259 Ekonomsko-poslovna fakulteta 0 0 0