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© 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

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온라인 사기 거래

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Hand Designed Rule Automated Rule learning from Data 사기 탐지(Fraud Detection) 어떻게 할 수 있을까?

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ü 확장성(Scalability) ü 새로운 유형의 사기 탐지 ü Domain 전문가 사람이 직접 Fraud Detection Rules을 개발한다면, Hand Designed Rules

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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

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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

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Generating Fraud Predictions Guest Checkout: Purchase IP: 1.23.123.123 email: [email protected] Payment: Bank123 … Fraud Detector returns: Outcome: Approved ML Score: 160 Purchase Approved Call service with: IP: 1.23.123.123 email: [email protected] Payment: Bank123 …

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Generating Fraud Predictions Guest Checkout: Purchase IP: 1.23.123.123 email: [email protected] Payment: Bank123 … Fraud Detector returns: Outcome: Approved ML Score: 160 Purchase Approved Call service with: IP: 1.23.123.123 email: [email protected] Payment: Bank123 …

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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

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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)

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Minimum dataset example

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Typical dataset example

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• 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

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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

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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

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Detector – Associated Rules

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Summary

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Demo

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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