AML 자료 모음
AML 분석 모델
- 자금세탁 의심거래 탐지 방안 - 소셜 네트워크 분석 기법을 중심으로 : link
- AML model risk management and validation: Introduction to Best Pratices : link
- Flushing out the money launderers with better customer risk-rating models : link
이론과 메뉴얼
- How does money laundering work? Youtube : link
- Korea: Anti-Money Laundering Laws and Regulations 2021 : link
- [책] 쉽게 이해하는 블록체인 : 암호화폐 자금세탁 방지 : link
- 차세대 AML - SAS 분석 자료 : link
AML solutions
- SAS anti-money laundering : link
- SAS support - learning course : link
- FICO : FICO: Fraud Detection and Anti-Money Laundering with AWS Lambda and AWS Step Functions : link
AML learning
- ACAMS - AML 교육의 명가 : link
- Anti-Money Laundering & Counter-Terrorist Financing Course : link
- AML eLearning Course : link
현업 데이터 사이언티스트들의 업무
- NetBoost (XgBoost+Network) for Suspicious Transaction Report
- Designed and implemented XgBoost model with network-based features to improve Transaction Monitoring System
- Utilized the relationships between neighbors in the graph to discover potential suspicious transactions and succeed to report unprecedented types of suspicious transactions to Korea Financial Intelligence Unit (KoFIU)
- Risk Assessment System (RA)
- Developed real-time Risk Assessment System to define customer’s risk level and provide appropriate KYC procedure.
- Leveraged tree-based M/L algorithm to select appropriate features for RA, and defined accuracy evaluation for different risk level
- Paper Company Detection Model
- Implemented automated paper company detection process using web crawling(w/python), and reduced monitoring time by 90%
- Designed a quantified detection model using crawling source and internal transaction data. Utilized point biserial correlation to define feature’s weight
국제 기준 및 정부 기관
- 금융정보분석원 : link
- 2021.02에 발표된 금융정보분석원(FIU)의 자금세탁방지 역량 강화 방안 및 2021년 중점 추진과제 : link
- 자금세탁방지 국제기구(FATF) : link