Mining insights with ease: Artificial Intelligence and Machine Learning for Financial Services
•
3 gefällt mir•285 views
Artificial Intelligence in Financial Services • Challenges • How AWS Helps FSI Customers • Use Cases Speakers: Dickson Yue, Solutions Architect, AWS & Sebastien Linsolas, Solutions Architect, AWS
Empfohlen
Empfohlen
Weitere ähnliche Inhalte
Was ist angesagt?
Was ist angesagt? (20)
Ähnlich wie Mining insights with ease: Artificial Intelligence and Machine Learning for Financial Services
Ähnlich wie Mining insights with ease: Artificial Intelligence and Machine Learning for Financial Services (20)
Mehr von Amazon Web Services
Mehr von Amazon Web Services (20)
Mining insights with ease: Artificial Intelligence and Machine Learning for Financial Services
- 1. Mining Insights with Ease Artificial Intelligence & Machine Learning for Financial Services Sebastien Linsolas, Solutions Architect Dickson Yue, Solutions Architect Amazon Web Services
- 2. Agenda • Artificial Intelligence in Financial Services • Challenges • How AWS Helps FSI Customers • Use Cases
- 3. Artificial Intelligence A system or service that can perform tasks that usually require human intelligence AA Hardware + learning algorithms Fit a network structure to the data Fit a function to the data AI ML DL
- 4. Why is there a “Race”? • Evolution of Technology • Evolution of Financial Services • Profitability • Competition • Regulation
- 5. The Impact of AI/ML AI/ML use cases are gaining traction in Financial Services Compliance, Surveillance and Fraud Detection Pricing and Product Recommendation Document Processing Trading Customer Experience
- 6. Slow Investment Source: McKinsey Global Institute, Artificial Intelligence The Next Digital Frontier? An ambivalent response to AI • Strong overall appetite for adopting AI • History of digital investment and strong foundation for integrating AI technologies • Large volumes of data to support model training and development • Comparatively low investment in AI
- 7. Challenges AI/ML expertise is rare Building and scaling AI/ML technology is hard Deploying and operating models in production is time- consuming and expensive A lack of cost-effective, easy-to-use, and scalable AI/ML services
- 8. Usability/Simplicity: Leverages AWS AI/ML expertise Greater control: Customer-specific models How AWS Helps
- 9. Enhancing Customer Experience and Uncovering Insights Polly Turn text into lifelike speech using deep learning Rekognition Deep learning-based image and video analysis Lex Conversational interfaces through natural language understanding Comprehend Discover insights and relationships in text Customization of offerings at scale More personal and efficient customer interactions Operational efficiencies Novel investment/ trading opportunities Benefits for Financial Services Institutions
- 10. Helping FIs Manage Risk By Recognizing Patterns Amazon Machine Learning Easy-to-use, managed machine learning service built for developers with existing data Benefits for Financial Services Institutions Compliance: • Know Your Customer: Client-type Identification • Alert risk-scoring to optimize investigation Trading/Risk Management: • Predictive Grid Computing Capacity Management Surveillance: • Credit-card/Account Fraud Detection • Sales Practices Monitoring
- 11. Helping FIs Identify Hidden Trends and Take Action Benefits for Financial Services Institutions Pricing and Product Recommendation: • Next-best Offer/Customer based Predictive Analytics • Loan/Insurance Underwriting Trading: • Sentiment Analysis • Algorithmic Trading • Portfolio Management & Optimization Surveillance: • Anti-Money Laundering • Market Manipulation Amazon SageMaker Easily build, train, and deploy machine learning models Amazon Deep Learning AMI Pre-installed Deep Learning Frameworks to train sophisticated AI/ML Models
- 12. Use case: credit card & loan voice, self service, automated
- 13. Voice services are personalizing financial services “Alexa, ask Capital One when my auto loan is due.” “Alexa, ask Fidelity to get me a market update.”
- 14. Alexa skill
- 15. Time0 – voice banking Customer Alexa “Alexa, ask Cloud Bank when my credit card bill is due.” “ok, it is on April 23 2018. The amount is $423,987. “Yes” “with a monthly interest as low as ….” We have a personal loan offer you might be interested, would you like to know?” Lambda Binary classification Customer-Will-Apply = 1 Multiclass classification Potential-Product = Ploan123 Amazon Machine learning
- 16. Time1 – multi channel engagement SMS: Would you like to apply the personal loan we discussed earlier? Call us at 3018 2298 . Serverless notification system Lambda PinpointAmazon Machine learning Binary classification Customer-Will-Apply = 1 Multiclass classification Potential-Product = Ploan123
- 17. Ok, the loan amount is $400,000, repayment period is 12 month. The monthly payment will be $34,666. Your application has been summited for review. Yes, $400,000 for 12 months. Great. Thank you!Data Dip CRM content Hi, Mr.Terry Chan, Are you calling to apply for the personal loan? What is the loan amount and repayment period? Incoming customer call Time3 - AI enabled contact center Lambda Amazon Connect LexPolly
- 18. Time4 – Application submission Fn:Save Application Database State Machine
- 19. Time4 – Customer onboarding Rekognition Fn:Save Fn:ValidateBio State Machine Application
- 20. Time4 – Fraud check Anti Fraud System Fn:CheckFraud Fn:Save Fn:ValidateBio State Machine Application
- 21. Time4 – Fraud check Fn:CheckFraud Fn:Save Fn:ValidateBio State Machine Application Amazon Neptune Graph Database
- 22. Time4 – Fraud check Amazon Neptune Graph Database Fn:CheckFraud Fn:Save Fn:ValidateBio State Machine Application
- 23. Fraud ring gremlin> g.V(). ......1> hasLabel('Applicant'). ......2> has('first_name','Terry'). ......3> has('last_name','Wilder'). ......4> out('CREDIT'). ......5> out('IDENTITY'). ......6> in('IDENTITY'). ......7> in('CREDIT').dedup().as('ring'). ......8> project('ring', 'identity'). ......9> by(select('ring').values().fold()). .....10> by(select('ring').out('CREDIT').out('IDENTITY').dedup().valueMap().fold()) ==>{ring=[Terry, Wilder], {phone_number=[0208 674 5742], type=[Phone Number]}, {type=[Social Security Number], ssn=[224-23-1221]}]} ==>{ring=[Bill, Darrow], {phone_number=[0208 674 5742], type=[Phone Number]}, {type=[Social Security Number], ssn=[555-23-4545]}]} ==>{ring=[Lucy, Phillips], {phone_number=[0208 674 5742], type=[Phone Number]}, {type=[Social Security Number], ssn=[224-23-1221]}]} ==>{ring=[Colin, Smith], {phone_number=[07074 633 7654], type=[Phone Number]}, {type=[Social Security Number], ssn=[224-23-1221]}]} Amazon Neptune Graph Database
- 24. Time4 – Fraud check with ML Fn:CheckFraud Fn:Save Fn:ValidateBio Application Fn:CheckFraud State Machine Train SageMaker Machine learning
- 25. Logical Architecture CRM content Voice Enquiry Prediction & Engagement Binary classification Multi channel Engagement Machine learning Pinpoint Lex Polly Engagement through AI Lambda DynamoDB Amazon Connect Contact Center Data Dip NLU Speech Lambda Lambda Lambda Lambda Lambda Lambda Rekognition Neptune Sage maker State machine Facial Auth Fraud Ring detect Fraud detect with ML Customer onboarding Fraud check
- 26. Ready to Start Building? Devising an implementation plan Building your use case Choosing the right tools An AI/ML discovery session with AWS Financial Services specialists can help with:
- 27. Thank You