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人工智慧雲服務與金融服務應用

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人工智慧雲服務與金融服務應用

  1. 1. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. T A I P E I 10.15.19 Incorporating AI/ML to drive innovation in financial services industry 人工智慧雲服務與金融服務應用 Young Yang, beyoung@amazon.com Machine Learning Specialist Solutions Architect
  2. 2. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Agenda • Cloud is new normal in financial services industry • Earn Customers’ Trust: first thing first security, security, and security • Machine learning for security and compliance workloads • ML/Ops is new normal in financial services industry • FinTechs aren’t disrupting banks… customer expectations are!! • Automation with increased quality and innovation • AI/ML is new normal in financial services industry • The right tools for the right job. Legacy Systems Haven’t Kept Up. • AI and ML are the next edge in digital innovation
  3. 3. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  4. 4. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Paradigm shift
  5. 5. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Representative industry view: Financial and Insurance 5 • Physical attacks against ATMs have seen a decline from their heyday of the early 2010’s. • Top 3 patterns: Web Applications, Privilege Misuse, and Miscellaneous Errors. • 36% Internal threat actors.
  6. 6. TLS Access Man-in-the-browser Client Session hijacking Malware Cross-site request forgery Abuse of functionality Man-in-the-middle DDoS Malware API attacks Injection Cross-site scripting Cross-site request forgery Certificate spoofing Protocol abuse Session hijacking Key disclosure DNS hijacking DDoS DNS spoofing DNS cache poisoning Man-in-the-middle App services DNS DDoS Eavesdropping Protocol abuse Man-in-the-middle Credential theft Credential stuffing Session hijacking Brute force Phishing Network DDoS Cross-site scripting Dictionary attacks
  7. 7. TLSCertificate spoofing Protocol abuse Session hijacking Key disclosure DDoS Man-in-the-browser Client Session hijacking Malware Cross-site request forgery Cross-site scripting DNS DNS hijacking DDoS DNS spoofing DNS cache poisoning Man-in-the-middle DDoS Eavesdropping Protocol abuse Man-in-the-middle Network Dictionary attacks Abuse of functionality Man-in-the-middle DDoS Malware API attacks Injection Cross-site scripting Cross-site request forgery App services Access Credential theft Credential stuffing Session hijacking Brute force Phishing
  8. 8. Vuln released Continuous improvement Firewall what you can’t fix Applicable? Test Apply & Retest 1.7 0.8 0.5 0.4 0.5 1.4 0.9 0.6 0.2 0.3 2014 2015 2016 2017 2018 Average Days Between Vulnerability Releases Critical High 9-12 hours
  9. 9. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. % of organizational leaders say cybersecurity teams are understaffed % of board directors and C- level execs say they have confidence in their organization’s level of cybersecurity 34 % of organizations believe that malicious attacks are on the rise y/y, but 34% confidence in their teams’ ability to address complex attacks 69 4 6Source: 2019 ISACA State of Cyber Security Report. Information Systems Audit and Control Association (ISACA) 國際電腦稽核協會
  10. 10. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Security continues to be job zero healthcare global banks
  11. 11. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Foundation Services Compute Storage Database Networking Infrastructure Regions Availability Zones Edge Locations Client-side Data Encryption Server-side Data Encryption Network Traffic Protection Platform, Applications, Identity & Access Management Operating System, Network & Firewall Configuration Customer content Shared Responsibility Model You need to handle full stacks and end-to-end scopes 之前要負責這麼大的範 圍
  12. 12. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. AWS Foundation Services Compute Storage Database Networking AWS Global Infrastructure Regions Availability Zones Edge Locations Client-side Data Encryption Server-side Data Encryption Network Traffic Protection Platform, Applications, Identity & Access Management Operating System, Network & Firewall Configuration Customer App / Content / Data Shared Responsibility Model CustomerAWS 聚焦在 創新應用 AWS 全球專業的安全 團隊 AWS 提供專業的工具 協助客戶
  13. 13. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. 站在巨人的肩膀
  14. 14. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Financial institutions across market segments are transforming on AWS
  15. 15. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Our mission Up to 135 Billion events per day Monitor 100% Equities & 70% Options in the US Run Hundreds of surveillance patterns Reconstruct Trillions of market nodes & edges Investor Protection Market Integrity
  16. 16. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. We can be far more secure in the cloud and achieve a higher level of assurance at a much lower cost, in terms of effort and dollars invested. We determined that security in AWS is superior to our on-premises data center across several dimensions, including patching, encryption, auditing and logging, entitlements, and compliance. —John Brady CISO, FINRA
  17. 17. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.  Micro-segmentation with security groups  Federated identities + granular entitlements  Visibility: AWS CloudTrail / Amazon CloudWatch Logs  Pervasive encryption: AWS KMS  Containers/serverless: Less to maintain or attack  Automate everything: DevSecOps & compliance  Resiliency: AZs, out-of-region data replication…  Security services: AWS WAF / AWS Shield / AWS Config /Amazon Macie Amazon GuardDuty, etc. More secure in the cloud Nearly everything else is better in the cloud, so it should be no surprise that security is too. The strongest cloud providers have bigger security budgets and deeper talent pools, and must be secure to survive. How?
  18. 18. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Machine learning-powered security service to discover, classify, and protect sensitive data AWS WAF - Web Application Firewall AWS Shield Machine Learning for Security and Compliance Workloads
  19. 19. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. MACHINE LEARNING FOR COMPLIANCE FOR PII-TYPES** LIKE NAMES, ADDRESSES, USER NAMES AND PASSWORDS, A REGEX- BASED APPROACH ISN’T POSSIBLE **Personally identifiable information
  20. 20. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Macie user behavior analytics We use behavioral analytics to baseline normal behavior patterns Contextualize by value of data being accessed Goals: • Go to great lengths to avoid false positives • Features, features • Compare peers • Tell a narrative
  21. 21. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. 0. Feature extraction from event data 1. Map into user time-series 2. Cluster peer groups 3. Predict user activity, update models 4.Identify anomalies 5. Attempt to explain statistically 7. Alert and narrative explanation created Normal accesses Machine learning-powered security service to discover, classify, and protect sensitive data
  22. 22. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. VPC flow logs DNS Logs CloudTrail Events HIGH MEDIUM LOW FindingsData Sources Threat Detection Types Threat intelligence Anomaly Detection (ML) Bitcoin Mining Instance Compromise Account Compromise Total of 62* detections AWS Security Hub SIEM Respond
  23. 23. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Most mature, enterprise-ready provider, with the strongest track record of customer success and the most useful partner ecosystem (Gartner, 2019) Comprehensive infrastructure Compliance standards & security Artificial intelligence & machine learning Partner networkFinancial industry services solutions
  24. 24. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  25. 25. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. FinTechs aren’t disrupting banks… customer expectations are!
  26. 26. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Paradigm shift
  27. 27. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. The traditional model is purely transactional, not “Customer Obsession” x Generic / Bad experience. Fill a lot of forms x Impersonal messages x Poorly timed engagements x Narrow audience reach x Minimal customer data and insights x Missed sales opportunities x Lengthy, frustrating forms for users. Can’t understand what it means x Disconnected legacy systems to manage x Limited touchpoints to reach users Consumers want financial institutions to know them, and expect personalized, contextual offers.
  28. 28. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. FinTechs aren’t disrupting banks… customer expectations are! AWS re:Invent 2018: Bernd Heinemann, Board Member at Allianz, Speaks at Global Partner Summit [link]
  29. 29. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Superior customer experience is a core value of our digital initiatives PayByPhone, VW The world's leading parking payment provider. In 2016, PayByPhone has already processed more than $250 million in payments and is adding approximately 7,000 users per day to their already substantial base of more than 12.5 million registered users. Example: PayByPhone at Apple‘s Developers Conference WWDC2018 in June 2018
  30. 30. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. 生存下來的物種 並不是那些最強壯,或者最聰明的,而是那些 能夠對變化做出快速反應的。 - 達爾文
  31. 31. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Automation with increased quality and innovation
  32. 32. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Put your time on Innovation Infrastructure Support Innovation Infrastructure Support Innovation Innovation Support ✅ automate automate
  33. 33. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. How AWS Help on “Automation”? Regulation DevOps & Modern Applications
  34. 34. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Customers rely on AWS’ compliance with global standards Certifications & Attestations Laws, Regulations and Privacy Alignments & Frameworks Cloud Computing Compliance Controls Catalogue (C5) 🇩🇪 CISPE 🇪🇺 CIS (Center for Internet Security) 🌐 Cyber Essentials Plus 🇬🇧 EU Model Clauses 🇪🇺 CJIS (US FBI) 🇺🇸 DoD SRG 🇺🇸 FERPA 🇺🇸 CSA (Cloud Security Alliance) 🌐 FedRAMP 🇺🇸 GLBA 🇺🇸 Esquema Nacional de Seguridad 🇪🇸 FIPS 🇺🇸 HIPAA 🇺🇸 EU-US Privacy Shield 🇪🇺 IRAP 🇦🇺 HITECH 🌐 FISC 🇯🇵 ISO 9001 🌐 IRS 1075 🇺🇸 FISMA 🇺🇸 ISO 27001 🌐 ITAR 🇺🇸 G-Cloud 🇬🇧 ISO 27017 🌐 My Number Act 🇯🇵 GxP (US FDA CFR 21 Part 11) 🇺🇸 ISO 27018 🌐 Data Protection Act – 1988 🇬🇧 ICREA 🌐 MLPS Level 3 🇨🇳 VPAT / Section 508 🇺🇸 IT Grundschutz 🇩🇪 MTCS 🇸🇬 Data Protection Directive 🇪🇺 MITA 3.0 (US Medicaid) 🇺🇸 PCI DSS Level 1 💳 Privacy Act [Australia] 🇦🇺 MPAA 🇺🇸 SEC Rule 17-a-4(f) 🇺🇸 Privacy Act [New Zealand] 🇳🇿 NIST 🇺🇸 SOC 1, SOC 2, SOC 3 🌐 PDPA - 2010 [Malaysia] 🇲🇾 Uptime Institute Tiers 🌐 PDPA - 2012 [Singapore] 🇸🇬 Cloud Security Principles 🇬🇧 PIPEDA [Canada] 🇨🇦 🌐 = industry or global standard Agencia Española de Protección de Datos 🇪🇸 26
  35. 35. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. The AWS Compliance Center features country-specific resources The AWS Compliance Center is a central location to research cloud regulations in specific countries and learn about AWS Compliance programs.
  36. 36. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. How AWS Help on “Automation”? Regulation DevOps & Modern Applications
  37. 37. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. AWS Native Services Cloud Mgmt. AWS Service Catalog AWS CloudTrail AWS Config AWS Trusted Advisor AWS X-Ray AWS OpsWorks Amazon CloudWatch AWS CloudFormation AWSSnowball AWSSMS AWS Systems Manager AWSKMS IAM AWSDMS Amazon Inspector Amazon Macie Amazon GuardDuty Service request Inventory and classification Monitoring and analytics Packaging and delivery* Provisioning and orchestration Cost management and resource optimization Cloud migration, backup, and DR Identity, security, and compliance AWSConfigAWSCodeDeploy AWSCodeCommit AWSCodePipeline AWSCodeBuild Source: Gartner May 2018 Evaluation Criteria for Cloud Management Platforms and Tools
  38. 38. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. • AWS and Standard Chartered have worked together for over 5 years • The Standard Chartered cloud journey started with a proof-of- concept grid compute running risk calculations • Has since expanded into a variety of production and non-production workloads
  39. 39. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Standard Chartered Cloud Journey 2017 Q2 - CI/CD pipeline (VX) for Cloud IaaS - 20k Concurrent vCPUs 2013 Q4 Grid PoC 2015 Q3 Production Grid Go-Live 2018 Q3 - Security monitoring with auto remediation - First Production application Go-Live via CI/CD pipeline (VX) with customer data - 1 million EC2 Instance-hours 2014 Q4 Development Grid Go-Live 2016 Q3 Grid has reached max of 7k concurrent vCPUs 2019 Q1 – NOW - 160 workloads - daily average of 250k vCPUs - 200k concurrent vCPUs - Grid cost $0.02 per vCPU hour
  40. 40. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Standard Chartered Cloud Journey
  41. 41. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  42. 42. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Applying ML/Ops Data Science Project Team Business Analyst Data Scientist Data Engineer SecurityDeveloper Operations QA Cross-Functional Project Team Domain Expertise Math/Statistical Expertise BigData & Data Pipeline Expertise Security ExpertiseApplication Code Expertise Full Stack & Operational Expertise Solution Expertise
  43. 43. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. ML/Ops Challenges: Automation with increased quality and Innovation 40% Gartner predicts that 40% of data science tasks will be automated by 2020** **Gartner Report – “Predicts 2017: Analytics Strategy and Technology”
  44. 44. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  45. 45. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. The right tools for the right job
  46. 46. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Surveillance and Fraud Detection Has Rapidly Evolved Entities and Events Known Threats and Risks Structured and Unstructured Data Machine Learning, AI, Natural Language Understanding, Behavioral Analytics Out-of-the-Box and Custom Models and Legacy Systems Haven’t Kept Up
  47. 47. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Surveillance and Fraud Detection Are Difficult and Expensive Detecting Intent Accurately Identifying Suspicious Communications Minimizing False Positives Uncovering Risky Entities / Individuals Inefficient and Costly Investigations Multiple Siloed Surveillance Platforms Random and Reactive Labor Intensive
  48. 48. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  49. 49. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Powered by machine learning Online application origination Growing issue as breaches expose more data Uses synthetic identities to create accounts— or sign up for credit cards online How do you defend against synthetic identities? Model development We reduced model development time by 60 percent—the more we practice it, the better we get! Used a variety of AWS services
  50. 50. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Surveillance and Fraud Detection Reference Technical Stack Data Gathering Analysis Decisions / Rules Investigations Device Fingerprints Browser Info IP Layer Usage History Payment Instrument Clickstream Log Files Behavioral Patterns Derived Values Relationships 3rd Party Data Real Time Binary Tree Regression Machine Learning Deep Learning Derived Values Historical Model Training Back Testing Anomaly Detection Auto Pass Auto Block Investigate Usage Limits Gather more Info Pass Gather more Info Fail Supporting Workflows Amazon NeptuneAmazon Kinesis Data Streams Amazon Simple Storage Service Amazon Redshift Amazon DynamoDB Amazon SageMaker AWS Lambda Amazon RDS Amazon Simple Notification Service Amazon Simple Queue Service Amazon EC2 AWS Step Functions Amazon Athena
  51. 51. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. AI and ML are the next edge in digital innovation. Compliance, Surveillance, and Fraud Detection Document Processing Pricing and Product Recommendation Trading Financial institutions are increasingly investing in AI/ML thanks, in part, to the availability of cost-effective, easy-to-use, and scalable cloud-based AI/ML services. Customer Experience • Credit card/account fraud detection • Sales practices/ transaction surveillance • AML/Sanctions • Investigations optimization • Regulatory mapping • Common financial instrument taxonomy • Contract ingestion and analytics • Financial information extraction • Corporate actions • Loan/Insurance underwriting • Sales/recommendations of financial products • Credit assessments • Portfolio management/ robo-advising • Algorithmic trading • Sentiment/news analysis • Image analysis • Grid computing scheduling • Enhanced customer service through chatbots • Call center optimization • Personal financial management Core processing Client facing
  52. 52. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Alexa personalizes Financial Services “Alexa, ask Fidelity to get me a market update.” “Alexa, ask Liberty Mutual for an auto insurance estimate.” “Alexa, ask Capital One how much I spent at Amazon last month.” Voice-based interactions can strengthen the relationship between consumers and their Financial Services providers.
  53. 53. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. 苹果周二新品发表会,各大研究机构、外资都推 出供应链受惠名单,促动苹概股前进上涨,使台 股即登上10800點。 Amazon Polly Turn text into lifelike speech using deep learning
  54. 54. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Text Text normalization Grapheme-to-phoneme conversion Waveform generation Speech 100% Recycled – 8 ½ x 11 inch 20 lb Office Paper – 3,000 count one hundred percent recycled, eight and a half by eleven inch… ˈwʌn ˈhʌndrəd pɚˈsɛnt riːˈsaɪkəld ˈeɪt ənd ə ˈhæf ˈɪntʃ… Text-to-speech pipeline
  55. 55. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. AI and ML are the next edge in digital innovation. Compliance, Surveillance, and Fraud Detection Document Processing Pricing and Product Recommendation Trading Financial institutions are increasingly investing in AI/ML thanks, in part, to the availability of cost-effective, easy-to-use, and scalable cloud-based AI/ML services. Customer Experience • Credit card/account fraud detection • Sales practices/ transaction surveillance • AML/Sanctions • Investigations optimization • Regulatory mapping • Common financial instrument taxonomy • Contract ingestion and analytics • Financial information extraction • Corporate actions • Loan/Insurance underwriting • Sales/recommendations of financial products • Credit assessments • Portfolio management/ robo-advising • Algorithmic trading • Sentiment/news analysis • Image analysis • Grid computing scheduling • Enhanced customer service through chatbots • Call center optimization • Personal financial management Core processing Client facing
  56. 56. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. M L F R A M E W O R K S & I N F R A S T R U C T U R E A I S E R V I C E S REKOGNITION IMAGE POLLY TRANSCRIBE TRANSLATE COMPREHEND LEXREKOGNITION VIDEO Vision Speech Language Chatbots A M A Z O N S A G E M A K E R B U I L D T R A I N FORECAST Forecasting TEXTRACT PERSONALIZE Recommendations D E P L O Y Pre-built algorithms & notebooks Data labeling (GROUND TRUTH) One-click model training & tuning Optimization (N E O ) One-click deployment & hosting M L S E R V I C E S Frameworks Interfaces Infrastructure EC2 P3 & P3dn EC2 C5 FPGAs GREENGRASS ELASTIC INFERENCE Reinforcement learning Algorithms & models ( AWS MARKETPLACE FOR MACHINE LEARNING) A W S D E E P R A C E R A W S D e e p L e n s Amazon Inferentia Optimized
  57. 57. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Machine learning is new normal in financial services industry
  58. 58. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Take Away
  59. 59. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. TLS Access Man-in-the-browser Client Session hijacking Malware Cross-site request forgery Abuse of functionality Man-in-the-middle DDoS Malware API attacks Injection Cross-site scripting Cross-site request forgery Certificate spoofing Protocol abuse Session hijacking Key disclosure DNS hijacking DDoS DNS spoofing DNS cache poisoning Man-in-the-middle App services DNS DDoS Eavesdropping Protocol abuse Man-in-the-middle Credential theft Credential stuffing Session hijacking Brute force Phishing Network DDoS Cross-site scripting Dictionary attacks © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. % of organizational leaders say cybersecurity teams are understaffed % of board directors and C-level execs say they have confidence in their organization’s level of cybersecurity34 % of organizations believe that malicious attacks are on the rise y/y, but 34% confidence in their teams’ ability to address complex attacks 69 46 Source: 2019 ISACA State of Cyber Security Report. Information Systems Audit and Control Association (ISACA) © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Agenda • Cloud is new normal in financial services industry • Earn Customers’ Trust: first thing first security, security, and security • Machine learning for security and compliance workloads • ML/ Ops is new normal in financial services industry • FinTechs aren’t disrupting banks… customer expectations are!! • Automation with increased quality and innovation • AI/ ML is new normal in financial services industry • The right tools for the right job. Legacy Systems Haven’t Kept Up. • AI and ML are the next edge in digital innovation
  60. 60. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. AWS Foundation Services Compute Storage Database Networking AWS Global Infrastructure Regions Availability Zones Edge Locations Client-side Data Encryption Server-side Data Encryption Network Traffic Protection Platform, Applications, Identity & Access Management Operating System, Network & Firewall Configuration Customer App / Content / Data CustomerAWS AWS AWS © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. We can be far more secure in the cloud and achieve a higher level of assurance at a much lower cost, in terms of effort and dollars invested. We determined that , including patching, encryption, auditing and logging, entitlements, and compliance. —John Brady CISO, FINRA © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Machine learning-powered security service to discover, classify, and protect sensitive data AWS WAF - Web Application Firewall AWS Shield Machine Learning for Security and Compliance Workloads
  61. 61. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Put your time on Innovation Infrastructure Support Innovation Infrastructure Support Innovation Innovation Support automate automate © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. FinTechsaren’t disrupting banks… customer expectations are! © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. The traditional model ispurely transactional,not “Customer Obsession” x Generic / Bad experience. Fill a lot of forms x Impersonal messages x Poorly timed engagements x Narrow audience reach x Minimal customer data and insights x Missed sales opportunities x Lengthy, frustrating forms for users. Can’t understand what it means x Disconnected legacy systems to manage x Limited touchpoints to reach users Consumers want financial institutions , and expect . © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. FinTechsaren’t disrupting banks… customer expectationsare! AWS re:Invent 2018: Bernd Heinemann, Board Member at Allianz, Speaks at Global Partner Summit [link]
  62. 62. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. How AWSHelp on “Automation”? Regulation DevOps & Modern Applications © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. The AWSCompliance Center featurescountry-specific resources The AWS Compliance Center is a central location to research cloud regulations in specific countries and learn about AWSCompliance programs. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Data Science Project Team Business Analyst Data Scientist Data Engineer SecurityDeveloper Operations QA Cross-Functional Project Team Domain Expertise Math/ Statistical Expertise BigData & Data Pipeline Expertise Security ExpertiseApplication Code Expertise Full Stack & Operational Expertise Solution Expertise © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  63. 63. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Right toolsfor right jobs © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Surveillance and Fraud Detection HasRapidly Evolved Entities and Events Known Threats and Risks Structured and Unstructured Data Machine Learning, AI, Natural Language Understanding, Behavioral Analytics Out-of-the-Box and Custom Models and Legacy Systems Haven’t Kept Up © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Surveillance and Fraud Detection Reference Technical Stack Data Gathering Analysis Decisions / Rules Investigations Device Fingerprints Browser Info IP Layer Usage History Payment Instrument Clickstream Log Files Behavioral Patterns Derived Values Relationships 3rd Party Data Real Time Binary Tree Regression Machine Learning Deep Learning Derived Values Historical Model Training Back Testing Anomaly Detection Auto Pass Auto Block Investigate Usage Limits Gather more Info Pass Gather more Info Fail Supporting Workflows Amazon NeptuneAmazon Kinesis Data Streams Amazon Simple Storage Service Amazon Redshift Amazon DynamoDB Amazon SageMaker AWS Lambda Amazon RDS Amazon Simple Notification Service Amazon Simple Queue Service Amazon EC2 AWS Step Functions Amazon Athena © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. AI and ML are the next edge in digital innovation. Compliance, Surveillance, and Fraud Detection Document Processing Pricing and Product Recommendation Trading Financial institutions are increasingly investing in AI/ ML thanks, in part, to the availability of cost-effective, easy-to-use, and scalable cloud-based AI/ ML services. Customer Experience • Credit card/ account fraud detection • Sales practices/ transaction surveillance • AML/ Sanctions • Investigations optimization • Regulatory mapping • Common financial instrument taxonomy • Contract ingestion and analytics • Financial information extraction • Corporate actions • Loan/ Insurance underwriting • Sales/ recommendations of financial products • Credit assessments • Portfolio management/ robo-advising • Algorithmic trading • Sentiment/ news analysis • Image analysis • Grid computing scheduling • Enhanced customer service through chatbots • Call center optimization • Personal financial management Core processing Client facing
  64. 64. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Machine learning isnew normal in financial servicesindustry © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. 10800 Amazon Polly Turn text into lifelike speech using deep learning
  65. 65. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. • Cloud is new normal in financial services industry • Earn Customers’ Trust: first thing first security, security, and security • Machine learning for security and compliance workloads • ML/Ops is new normal in financial services industry • FinTechs aren’t disrupting banks… customer expectations are!! • Automation with increased quality and innovation • AI/ML is new normal in financial services industry • The right tools for the right job. Legacy Systems Haven’t Kept Up. • AI and ML are the next edge in digital innovation • 上雲在金融業很常見 • 客戶對於安全性的要求越來越高 • 透過人工智能來幫助安全和合規的工作 • ML/Ops 在金融業很常見 • FinTechs 不會顛覆金融產業,但是客戶經驗會感 變整個產業 • 自動化能增進品質和創新 • AI/ML 在金融業很常見 • 使用正確的工具,做正確的事情。老舊的系統很難 跟業務的需求 • 人工智慧是下一時代的數位創新
  66. 66. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Build on AWS
  67. 67. Thank you! © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Young Yang, ML Specialist Solutions Architect beyoung@amazon.com
  68. 68. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Calibrate thresholds in transaction surveillance alert logic Refine keywords and phrases used for e-communications surveillance lexicons Extract hidden relationships and insights in the data Drive “what if” analysis on risks which are not covered by existing metrics/analysis Optimize alert output: New scenario analysis: Solution: Use machine learning to optimize surveillance logic

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