Amazon Fraud Detector

Introduction to Amazon Fraud Detector

Transcript

1. © 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved | 158 © 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved Detect more online fraud faster Amazon Fraud Detector Sungmin Kim AWS Solutions Architect
2. 온라인 사기 거래
3. Hand Designed Rule Automated Rule learning from Data 사기 탐지(Fraud Detection) 어떻게 할 수 있을까?
4. ü 확장성(Scalability) ü 새로운 유형의 사기 탐지 ü Domain 전문가 사람이 직접 Fraud Detection Rules을 개발한다면, Hand Designed Rules
5. ü ML(기계 학습) 전문가의 부재 ü 반복적인 학습과 모델 평가 ü Time-consuming 작업 Automated Rule learning from Data Fraud Detection은 ML 역시 어렵다
6. Amazon Fraud Detector 기계 학습을 사용하여 온라인 사기를 대규모로 실시간으로 쉽게 감지 할 수 있는 사기 탐지 서비스 사전 구축 된 사기 탐지 모델 템플릿 맞춤형 사기 탐지 모델 자동 생성 아마존 내부 경험을 통한 다양한 패턴 Amazon SageMaker와의 통합 과거 평가 및 탐지 로직 검토 통합
7. Generating Fraud Predictions Guest Checkout: Purchase IP: 1.23.123.123 email: joe@example.com Payment: Bank123 … Fraud Detector returns: Outcome: Approved ML Score: 160 Purchase Approved Call service with: IP: 1.23.123.123 email: joe@example.com Payment: Bank123 …
8. Generating Fraud Predictions Guest Checkout: Purchase IP: 1.23.123.123 email: joe@example.com Payment: Bank123 … Fraud Detector returns: Outcome: Approved ML Score: 160 Purchase Approved Call service with: IP: 1.23.123.123 email: joe@example.com Payment: Bank123 …
9. ML template: Online Fraud Insights • Detect risky events based on an event’s attributes • Best for detecting potential fraud when historical account/user data is limited • Inspired by models and techniques used to protect Amazon.com/AWS account registration • Use cases: new account, first transaction, guest checkout • Inputs: 3 required data elements and 50+ optional
10. Data requirements (for Online Fraud Insights template) EVENT_TIMESTAMP Variable 1 Variable 2 Variable N EVENT_LABEL 4/10/2019 11:05 … … … Legit / 0 4/10/2019 19:34 … … … Legit / 0 4/10/2019 20:29 … … ... Fraud / 1 … … … … … Required Required At least 2 variables required (max 100) At least 10K total examples At least 500 fraud examples • Data must reside in S3 (same region with AFD) • Data should be in CSV format • First line of CSV file should have headers • 2 required headers: EVENT_TIMESTAMP and EVENT_LABEL (they should not have any NULL or missing values) • Maximum file size of 5GB • Minimum 6 weeks of data • Recommended: 3-6 months of data • AFD can handle NULL and missing values (for variables)
11. Minimum dataset example
12. Typical dataset example
13. • You will need to map all the event variables to a variable type • Amazon Fraud Detector can also do this automatically, when you import the dataset • For more information see Variable types . EVENT_TIMESTAMP Variable 1 Variable 2 Variable N EVENT_LABEL 4/10/2019 11:05 … … … Legit / 0 4/10/2019 19:34 … … … Legit / 0 4/10/2019 20:29 … … ... Fraud / 1 … … … … … Variable type EMAIL_ADDRESS IP_ADDRESS PHONE_NUMBER USERAGENT FINGERPRINT PAYMENT_TYPE CARD_BIN AUTH_CODE AVS BILLING_NAME BILLING_PHONE BILLING_ADDRESS_L1 BILLING_ADDRESS_L2 BILLING_CITY BILLING_STATE BILLING_COUNTRY BILLING_ZIP SHIPPING_NAME SHIPPING_PHONE SHIPPING_ADDRESS_L1 SHIPPING_ADDRESS_L2 SHIPPING_CITY SHIPPING_STATE SHIPPING_COUNTRY SHIPPING_ZIP ORDER_ID PRODUCT_CATEGORY CURRENCY_CODE PRICE NUMERIC CATEGORICAL FREE_FORM_TEXT Variables
14. ML Template: Automated model building Data Validation 1 Data Enrichment &Transformation 2 Model Training & Selection 4 Performance Metrics 5 Training data in Amazon S3 Deployment & Hosting 6 Feature Engineering 3
15. Interactive ML performance metrics • GUI for defining the optimal decision threshold for the best separation between fraud and legits • Confusion matrix • Easily control the trade- off between FP and FN Part of Fraud Detector UI
16. Detector – Associated Rules
17. Summary
18. Demo
20. Reference • Catching fraud faster by building a proof of concept in Amazon Fraud Detector • Reviewing online fraud using Amazon Fraud Detector and Amazon A2I • AWS Fraud Detector Samples