AI e ML para Serviços Financeiros
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Assista a este webinar e entenda como empresas de serviços financeiros podem usar inteligência artificial para combater fraudes por meio de reconhecimento facial e também desenvolver uma solução de análise de documentos com revisão humana. Principais tópicos apresentados: - Os principais casos de uso de inteligência artificial em serviços financeiros; - Aproveite o Amazon Rekognition para realizar liveness detection e evitar atividades fraudulentas; - Como usar serviços como Amazon Textract, Amazon Comprehend e Amazon Augmented AI para analisar documentos físicos automaticamente.
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AI e ML para Serviços Financeiros
- 1. © 2020, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Alexandre Bicas Solutions Architect Manager Amazon Web Services Inteligência Artificial e Machine Learning para Serviços Financeiros
- 2. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. O que éInteligênciaArtificial? Inteligência Artificial Qualquer técnica que abilita máquinas (computadores) a imitar a inteligência human usando lógica, loops if-then-else, e aprendizado de máquina (incluindo Deep Learning) Subconjunto de IA que utiliza máquinas para procurer padrões em conjuntos dae dados e constrói modelos lógicos automaticamente Subconjunto de Aprendizado de Máquina composto por redes neurais de múltiplas camadas que executam tarefas como reconhecimento de voz e imagem
- 3. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 4. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. $50bi+ a previsão dos gastos mundiais em tecnologias de AI e cognitivas —IDC 2018 Inovação Tomada de decisão Experiência do cliente Peça central para a transformação digital Operação de negócios Vantagem competitiva
- 5. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Instituições financeiras estão investindo cada vez mais emAI/ML porque tem melhor custo/benefício, fácil de usar, e é mais escalável usando computação em nuvem Conformidade, vigilância, e detecção de fraude • Deteção de fraude em conta e cartão de crédito • Vigilância em transações / práticas de vendas • AML/Sanções • Otimização de investigação • Mapeamento regulatório Processamento de documentos • Taxonomia de instrumentos financeiros comuns • Ingestão e análise de contratos • Extração de informações financeiras • Ações corporativas Recomendação de preços e produtos • Recomendação de empréstimos • Recomendação/venda de produtos financeiros • Assessoria de crédito • Balanço de conta bancária Trading e análises • Gerenciamento de portfolio / recomendação de robôs • Trading quântico • Análise de sentiment / notícias • Análise de imagens • Agendamento de grid computing • Empréstimos de Securities • Revisão NAV • Precificação de opções Experiência do cliente • Melhorar experiência do cliente através de aplicativos mobile e chatbots • Otimização de call center • Gerenciamento de finanças pessoais • Atender clientes a partir de canais prediletos (ex: Alexa) Processamento central Contato com o usuário AI/MLhabilitandoapróximaondadasorganizaçõesfinanceiras
- 6. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. FALTA DE CONHECIMENTO DADOS CASO DE USO APLICADO AO NEGÓCIO CONSTRUÇÃO DE MODELO DE ML Desafios comuns Falta força de trabalho capacitada a construir bons modelos de ML Achar o caso de uso adequado que possa se beneficiar de ML É um processo complexo e que consume muito tempo Ter uma estratégia de dados bem definida
- 7. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Nossa missão na AWS Tornar machine learning acessível à todos os desenvolvedores
- 8. © 2020, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Instituições financeiras de todos os segmentos estão usando a AWS
- 9. © 2020, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Alavancando a AWS, o Nubank lançou um cartão de crédito sem taxas, gerenciado completamente através de um aplicativo móvel, para redefinir o relacionamento dos clientes com seu dinheiro e com as instituições financeiras. A startup brasileira cresceu rapidamente escalando seus serviços com a AWS para gerenciar 23+ milhões de clientes, fornecendo cartão de crédito e contas digitais na América Latina. Através de sua inovação, a AWS está ajudando o Nubank a ganhar parte do mercado dos bancos tradicionais com uma Plataforma flexível, escalável, permitindo eles a testarem novos negócios e idéias ao mesmo tempo que estão crescendo. We have achieved these goals largely because of the structure and technology that AWS offers us. Renan Capaverde, Infrastructure Leader at Nubank “ ” NubanksetornaomaiorbancoindependentenaAWS
- 10. © 2020, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Machine Learning na AWS
- 11. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. VISION SPEECH TEXT SEARCH CHATBOTS PERSONALIZATION FORECASTING FRAUD CONTACT CENTERS Deep Learning AMIs & Containers GPUs & CPUs Elastic Inference Trainium Inferentia FPGA AI SERVICES ML SERVICES FRAMEWORKS & INFRASTRUCTURE DeepGraphLibrary Amazon Rekognition Amazon Polly Amazon Transcribe +Medical Amazon Lex Amazon Personalize Amazon Forecast Amazon Comprehend +Medical Amazon Textract Amazon Kendra Amazon CodeGuru Amazon Fraud Detector Amazon Translate INDUSTRIAL AI CODE AND DEVOPS NEW Amazon DevOps Guru Voice ID For Amazon Connect Contact Lens NEW Amazon Monitron NEW AWS Panorama + Appliance NEW Amazon Lookout for Vision NEW Amazon Lookout for Equipment O stack de ML na AWS NEW Amazon HealthLake HEALTH AI NEW Amazon Lookout for Metrics ANOMALY DETECTION Amazon Transcribe Medical Amazon Comprehend Medical Amazon SageMaker Label data NEW Aggregate & prepare data NEW Store & share features Auto ML Spark/R NEW Detect bias Visualize in notebooks Pick algorithm Train models Tune parameters NEW Debug & profile Deploy in production Manage & monitor NEW CI/CD Human review NEW: Model management for edge devices NEW: SageMaker JumpStart SAGEMAKER STUDIO IDE
- 12. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. VISION SPEECH TEXT SEARCH CHATBOTS PERSONALIZATION FORECASTING FRAUD CONTACT CENTERS Deep Learning AMIs & Containers GPUs & CPUs Elastic Inference Trainium Inferentia FPGA AI SERVICES ML SERVICES FRAMEWORKS & INFRASTRUCTURE DeepGraphLibrary Amazon Rekognition Amazon Polly Amazon Transcribe +Medical Amazon Lex Amazon Personalize Amazon Forecast Amazon Comprehend +Medical Amazon Textract Amazon Kendra Amazon CodeGuru Amazon Fraud Detector Amazon Translate INDUSTRIAL AI CODE AND DEVOPS NEW Amazon DevOps Guru Voice ID For Amazon Connect Contact Lens NEW Amazon Monitron NEW AWS Panorama + Appliance NEW Amazon Lookout for Vision NEW Amazon Lookout for Equipment O stack de ML na AWS NEW Amazon HealthLake HEALTH AI NEW Amazon Lookout for Metrics ANOMALY DETECTION Amazon Transcribe Medical Amazon Comprehend Medical Amazon SageMaker Label data NEW Aggregate & prepare data NEW Store & share features Auto ML Spark/R NEW Detect bias Visualize in notebooks Pick algorithm Train models Tune parameters NEW Debug & profile Deploy in production Manage & monitor NEW CI/CD Human review NEW: Model management for edge devices NEW: SageMaker JumpStart SAGEMAKER STUDIO IDE
- 13. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Processos de empréstimos • Origem do financiamento • Serviços de financiamneto • Processamento de aplicação de cartão de crédito • Aquisição de dados • Deteção de fraude • Estado do processamento • Processamento do empréstimo Gerenciamento de bens e ativos • “Onboarding” de clientes • Gerenciamento de portfolio • Relatórios de performance • Confirmação de gerenciamento • Acordos • Automação de pagamentos & transferências Serviço ao cliente • Abertura e término de contas • Verificação de endereço • Atualização e consolidação de dados • Extratos de contas • Processamento de reclamações • Call Center Support Conformidade • Fluxos de Know Your Customer (KYC) • Anti-Money Laundering (AML) • Verificações e sanções OFAC • Rastreio de zonas proibidas • Relatórios regulatórios Gerenciamento de dados • Mineração de dados • Validação de dados • Sistemas legados • Migração de dados • Conformidade & auditoria de dados internos Finanças • Reconcilizações • Entradas manuais de diários • Operações de tesouraria • Gerenciamento de dados de impostos • Processamento de invoice • Contas recebíveis Download automático de invoices a partir do seu email e extração de dados relevantes usando computação visional e tecnologia de IA. Robôs automáticos então inserem estes dados no ERP. Robotic ProcessAutomation (RPA)
- 14. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Transformando a experiência dos clientes Análise de “Voz do Cliente” | Agentes de serviço automatizados | Suporte multi-idiomas Canal predileto de comunicação do cliente | Recomendações personalizadas POLLY TRANSCRIBE TRANSLATE COMPREHEND LEX PERSONALIZE CONTACT LENS For Amazon Connect
- 15. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Simplificando a conformidade e autenticação REKOGNITION COMPREHEND Verificação de identidade | Reconhecimento de entidade | Classificador de textos TEXTRACT
- 16. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. AWSSDKpara Python (boto3) • Suporte a Python 2 e 3 • API’s de cliente e recursos, além de waiters • Expõe API HTTPS • Permitem acessar atributos e executar ações • Ajuda no controle das ações https://boto3.amazonaws.com/v1/documentation/api/lates t/index.html
- 17. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Amazon Transcribe
- 18. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Amazon Transcribe Um serviço totalmente gerenciado e continuamente melhorado de automatic speech recognition (ASR) que recebe audio e gera automaticamente transcrições apuradas
- 19. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Identificação do canal Vocabulário customizad o Identificação do locutor Pontuação, capitalização Marcação de horário mundial Alta confiança na inferência Transcrições alternativas Rasura de conteúdo PCI/PII Filtro de vocabulári o Modelos customizados de idiomas AmazonTranscribe – Funcionalidades
- 20. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Stream de aúdio Stream de texto Conexão bi-directional com HTTP2 ou WebSocket Streaming Transcrição em tempo real Processamento em lote de grandes quantidades Arquivo de aúdio Arquivo da transcrição * 31 idiomas suportados * 6 idiomas suportados Amazon Transcribe Transcrição em lote AmazonTranscribe
- 21. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Build a call recording solution (Contact Center QA) - Call recording storage in Amazon S3 - Playback in live or on-demand mode - Durable, searchable storage - Secure and fully managed Build automated compliance auditing (disclosures) - Use with Amazon Transcribe and AWS Lambda - Transcribe call data and check for compliance Amazon Chime Voice Connector streams audio from phone calls in real time for playback, storage, and analytics VoiceTelephonyIntegrationtoTranscribewith AmazonChimeVoiceConnector Amazon Transcribe Amazon Kinesis Video Streams Voice Connector SIP Trunks Voice Connector SIPREC
- 22. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Amazon Transcribe DEMO
- 24. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Amazon Textract
- 25. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Extrai dados rapidamente, precisamente , e com flexibilidade (extrai a partir de diferentes tipos de documentos) Reduz ou elimina o esforço manual Reduz o custo de processamento dos documentos Não requer experiência com ML AmazonTextract:funcionalidades
- 26. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Principais características Extração de texto Extração de formulários Extração de tabelas
- 27. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Relacionamentos
- 28. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Relacionamentos
- 29. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Relacionamentos
- 30. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Respostadas chamadas deAPI
- 31. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Amazon Textract DEMO
- 32. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 33. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Amazon Comprehend
- 34. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Um serviçototalmentetreinado e gerenciado que descobre insightse relacionamentos no texto
- 35. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Principais características
- 36. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Suporte aentidades customizadas • Analise documentos específicos de um segmento usando os jargões únicos deste domínio • Traga o seu próprio schema para analisar textos não estruturados Olá, boa tarde, meu nome é John Doe. Obrigado por chamar a AnyCompany. Eu entendo que você está ligando para falar sobre a peça de número XT2457. Nós estamos sem estoque desta peça, porém já fizemos o pedido e vamos enviar em breve para você. Obrigado, mas eu esperava recebê-la semana passada. Nesse momento, eu prefiro cancelar a ordem por completo. Nos desculpamos pelo incoveniente senhor. Você estaria disposto a continuar com o pedido se lhe oferecessemos um desconto de 10% sobre o valor pago? Sim, estaria disposto. Obrigado. Pessoa: John Doe Organização: AnyCompany Parte: XT2457 Ação_Conta: Cancel the order
- 37. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Saídaestruturada doAmazonComprehend Pessoa Organização Parte Conta_Ação John Doe AnyCompany XT2457 Cancelar o pedido Ricardo Silva Example Corp. SD5578 Renovar assinatura Entidades pré-treinadas Entidades customizadas
- 38. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Amazon Comprehend DEMO
- 40. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Amazon Sagemaker
- 41. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. VISION SPEECH TEXT SEARCH CHATBOTS PERSONALIZATION FORECASTING FRAUD CONTACT CENTERS Deep Learning AMIs & Containers GPUs & CPUs Elastic Inference Trainium Inferentia FPGA AI SERVICES ML SERVICES FRAMEWORKS & INFRASTRUCTURE DeepGraphLibrary Amazon Rekognition Amazon Polly Amazon Transcribe +Medical Amazon Lex Amazon Personalize Amazon Forecast Amazon Comprehend +Medical Amazon Textract Amazon Kendra Amazon CodeGuru Amazon Fraud Detector Amazon Translate INDUSTRIAL AI CODE AND DEVOPS NEW Amazon DevOps Guru Voice ID For Amazon Connect Contact Lens NEW Amazon Monitron NEW AWS Panorama + Appliance NEW Amazon Lookout for Vision NEW Amazon Lookout for Equipment O stack de ML na AWS NEW Amazon HealthLake HEALTH AI NEW Amazon Lookout for Metrics ANOMALY DETECTION Amazon Transcribe Medical Amazon Comprehend Medical Amazon SageMaker Label data NEW Aggregate & prepare data NEW Store & share features Auto ML Spark/R NEW Detect bias Visualize in notebooks Pick algorithm Train models Tune parameters NEW Debug & profile Deploy in production Manage & monitor NEW CI/CD Human review NEW: Model management for edge devices NEW: SageMaker JumpStart SAGEMAKER STUDIO IDE
- 42. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Amazon SageMaker visão geral PREPARAR SageMaker Ground Truth Rotulamento de datasets SageMaker Data Wrangler Agregue e prepare dados para machine learning SageMaker Processing Python nativo, BYO R/PySpark SageMaker Feature Store NEW Store, update, retrieve, and share features SageMaker Clarify NEW Detect bias and understand model predictions CONSTRUIR SageMaker Studio Notebooks Jupyter notebooks with elastic compute and sharing Built-in and Bring your-own Algorithms Dozens of optimized algorithms or bring your own Local Mode Test and prototype on your local machine SageMaker Autopilot Automatically create machine learning models with full visibility SageMaker JumpStart NEW Pre-built solutions for common use cases TREINAR E OPTIMIZAR Managed Training Distributed infrastructure management SageMaker Experiments Capture, organize, and compare every step Automatic Model Tuning Hyperparameter optimization Distributed Training NEW Training for large datasets and models SageMaker Debugger NEW Debug and profile training runs Managed Spot Training Reduce training cost by 90% DEPLOY & GERENCIAMENTO Managed Deployment Fully managed, ultra low latency, high throughput Kubernetes & Kubeflow Integration Simplify Kubernetes-based machine learning Multi-Model Endpoints Reduce cost by hosting multiple models per instance SageMaker Model Monitor Maintain accuracy of deployed models SageMaker Edge Manager NEW Manage and monitor models on edge devices SageMaker Pipelines NEW Workflow orchestration and automation Amazon SageMaker SageMaker Studio Integrated development environment (IDE) for ML
- 43. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. CASOS DE SUCESSO NO BRASIL
- 44. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Funcionalidades importantes do Amazon SageMaker para FSI Segurança Autenticação Proteção dos dados Gerenciamento de artefatos Explicabilidade de modelo Monitoramento de modelo Autorização Auditoria Todo o tráfego passa por redes e links seguros Garanta a conformidade usando a autenticação corporativa padrão Criptografe os dados em transito e em repouso com suas próprias chaves gerenciadas Permite auditoria fim-a-fim a nível de objeto e usuário Limite o acesso aos dados, código, e recursos de treinamento conforme a função da pessoa Persista de forma segura seu código e artefatos de modelos Fornece interpretabilidade nos resultados do treinamento de modelos Monitore os desvios sobre os dados e sobre os modelos Atributos de segurança: Atributos de governança de modelos:
- 45. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Amazon Sagemaker DEMOS
- 46. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 47. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 48. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Obrigado ! Alexandre Bicas
Notas do Editor
- You’ve probably heard these technologies described in a number of ways so let’s take a step back to level-set on what they are. AI is a way to describe any system that can replicate tasks that previously required human intelligence. Almost always, this is related to some kind of complex decision making where human judgment would be required. Most uses cases for AI are looking for a probabilistic outcome – making a prediction or decision with a high degree of certainty, similar to human judgement. Almost all AI systems today are created using machine learning, which uses large amounts of data to create and validate decision logic, known as a model. The AI application then feeds input data into that model, and the model outputs human-like decisions. So machine learning is the underlying technology that is powering intelligent systems. Deep Learning is a type of machine learning that uses a technique known as deep neural networks. These systems replicate how the human brain functions. This lets AI systems address more complex uses cases than was previously possible.
- For more than 20 years, ML has been at the core of how Amazon improves our services and drive customer value. We started by applying deep learning to recommendations on Amazon.com 20 years ago. We’ve improved that model significantly over time and moved it to other products such as Amazon Prime. We use ML throughout our fulfillment process including developing a forecast system that can predict the appropriate amount of demand for each product we sell worldwide in order to deliver on our customer expectation on convenience, cost, and delivery speed. We’ve developed natural language understanding and text-to-speech technology to give consumers and entirely new way to interact with technology through Alexa. Currently Amazon has more than 25 fulfilment centers using the help of 100,000 robots to fulfill customer orders. And we’ve developed ground breaking technology with autonomous flight via Prime Air Drones all in service of getting our customers their packages faster. Not only are these use cases potentially applicable to your business, but our experience implementing ML to make impact at Amazon is applied to helping our customers accelerate their adoption of machine learning.
- There’s been a lot of hype around AI and Machine Learning, and their potential to drive digital transformation in the enterprise. Each year, Gartner authors a report on the hype cycle for emerging technologies. For the last four years running, machine and deep learning have been at the peak of their hype cycle. Its interesting to note that machine learning has been around for more than 50 years. Most of the common machine learning techniques we use today were really invented decades ago. What has changed recently is that with cloud computing, AI and machine learning have become accessible to all businesses – not limited to just the major tech giants and hardcore academic researchers. Cloud has removed so many of the barriers to experimenting and innovating with AI that even risk-adverse businesses are making it part of their strategies. So we’re seeing a tipping point, where the recent hype for these technologies is transitioning to real impact on businesses. A recent IDC study estimated that this year, 40% of digital transformation initiatives will take advantage of AI. IDC also predicted that by 2021, global spending on AI and cognitive technologies will exceed $50 billion. For example: Customer experience is being transformed via capabilities such as conversational interfaces, smart biometric authentication, and personalization and recommendations. In retail, sophisticated demand planning and forecasting models are dramatically improving accuracy. Automation is making supply chain management more efficient. In healthcare, we’re seeing a shift from reactive to predictive care, including the use of predictive models to accelerate research and discovery of new drugs and treatments regiment. FACTS FOR COLOR: According to IDC, global spending on AI and cognitive technologies will reach $19.1 billion in 2018, up 54.2 percent compared to a year ago. By 2021, AI and cognitive spending will hit $52.2 billion. (“Worldwide Spending on Cognitive and Artificial Intelligence Systems Will Grow to $19.1 Billion in 2018,” March 22, 2018, IDC.) 42% of executives say AI will drive innovation in their organization (Shook, Ellyn and Mark Knickrehm, ”Reworking the Revolution: Future Workforce,“ 2018, Accenture.) Enterprises balancing in-house innovation with strong external collaboration in AI have seen nearly twice the growth rate in the value of their companies. (“Boost Your AIQ: Transforming Into an AI Business,” 2017, Accenture.) Analysts are currently predicting there will be 5.2 Zettabytes of ML analyzable data by 2025, which is 50X more than 2016 - IDC White Paper: April 2017 – The evolution of data to life critical. https://solutionsreview.com/data-management/idc-data-creation-to-reach-163-zettabytes-by-2025/ Retail: AI-based demand forecasting reduced errors by 30-50%, with lost sales due to product unavailability reduced by up to 65%. (“Artificial Intelligence: The Next Digital Frontier?” June 2017, McKinsey Global Institute.) Manufacturing: Using AI to predict sources of servicing revenues and to optimize sales efforts can increase EBIT by 13%. (“Artificial Intelligence: The Next Digital Frontier?” June 2017, McKinsey Global Institute.) Electric Utilities: Automated inspection, preventive maintenance, demand management, and theft detection could raise EBITDA by 20-30%. (“Artificial Intelligence: The Next Digital Frontier?” June 2017, McKinsey Global Institute.) Health Care: Use of AI could mean $300 billion in savings domestically. Nurses supported by AI tools can increase productivity by up to 50%. (“Artificial Intelligence: The Next Digital Frontier?” June 2017, McKinsey Global Institute.)
- Specifically in financial services, we are seeing adoption of AI/ML across five primary use cases: Compliance, surveillance, and fraud detection Payment fraud detection – optimizing the model to provide sufficient protection for the user and merchant, but not over doing it where the false positives erode the customer experience Markets surveillance – use ML to detect market anomalies and other behavioral patterns Identify Verification – use ML to streamline and enhance the customer onboarding experience while automating the KYC process Document processing Use ML techniques such as optical character recognition (OCR) and natural language processing (NLP) to streamline due diligence and document review processes Extract entities, dates, locations from documents and match them with internal databases Pricing and product recommendation Develop personalized products, services, and recommendations For example, Vanguard’s presentation at re:Invent noted using a person’s goals, current investments, demographics to create an optimal investment portfolio Trading and analytics Applying ML to alternative data sets like satellite imagery to predict crop yields; or count the number of cars in the parking lot of a department store to predict quarterly sales and earnings Applying ML for sentiment analysis on social media (and traditional media) feeds Predictive analytics (i.e. trying to predict stock loan rates) Customer Experience ML is used to automate the customer onboarding process – 20 years ago, opening a bank account required a person to fill out a physical application form and it took several days. Today, you can open an account in under a minute, by taking a picture of yourself and you ID with your phone, and uploading it to the application. Using ML, the FI uses OCR to extract text data from the ID and uses facial recognition to confirm the identity of the applicant. ML is also used in the form of chat bots to communicate with the customer’s choice of interaction channels: voice, text, web
- For all the progress we have made in Machine Learning, there are still some common challenges that we’ve observed that keep organizations from implementing machine learning. According to McKinsey Global AI Survey 2019, 65 percent from the high performers report having a clear data strategy that supports and enables AI, compared with 20 percent from other companies. According to the Gartner’s 2019 CIO Agenda survey, skill of staff is rated the number one blocker to AI adoption with 56% of people citing it as a blocker. McKinsey Global AI Survey 2019: 72 percent of respondents from AI high performers say their companies’ AI strategy aligns with their corporate strategy, compared with 29 percent of respondents from other companies.
- Our mission is to take our rich experience and expertise with machine learning across Amazon and put it in the hands of all organizations--every developer, data scientist, reseacher. Said another way, we want to simplify machine learning. We want to make it easy for all developers to easily build intelligent applications
- Company description: Founded in 2013, Nubank is a financial services startup based in Brazil, offers customers a no-fee, low-interest credit card, banking, financial services. The company's differentiating factor is to offer a digital bank account and a credit card which is controlled completely through a mobile app. Use Case: All-in Geo: LATAM Sources/Links: -NuBank -https://youtu.be/GMCqu357hlk -https://aws.amazon.com/pt/solutions/case-studies/nubank2019/
- We’re innovating on behalf of our customers to deliver the broadest and deepest set of machine learning capabilities for builders of all levels of expertise. At each layer of the stack, we’re investing in removing the undifferentiated heavy lifting so your teams can move faster. We also now have purpose-built solutions for industries, such as industrials and healthcare. Let’s take a walk through this the three layers.
- We’re innovating on behalf of our customers to deliver the broadest and deepest set of machine learning capabilities for builders of all levels of expertise. At each layer of the stack, we’re investing in removing the undifferentiated heavy lifting so your teams can move faster. We also now have purpose-built solutions for industries, such as industrials and healthcare. Let’s take a walk through this the three layers.
- Speaker Notes: Idea is to go broad if the right audience is in the room. Briefly bring up Mortgage, CC application processing, and finally Invoice processing before moving to next slide.
- OpenData – OpenBanking - LGPD Speaker Notes: Can automatically check information on premises before migrating to a cloud or across jurisdictional boundaries . Can run rules against all text-readable documents including Word, .pdf, Open Office and many legacy programs. TheTeraDactor to take action on data from hundreds of different types of databases including: Oracle, MSSQL, Mongo DB, Cassandra, SAP HANA, etc. during the extract, transform, load (ETL) process. One of the largest immediate use cases for this capability is to address the European Union’s General Data Protection Regulation (GDPR) where, among other things, TeraDactor can ensure an individual’s right to be forgotten in compliance with Articles 17 & 20 of those regulations. Tokens allow customers to segment storage of sensitive data ensuring protection of originals and tokens while allowing for privacy compliant analytic to be performed on de-identified data. Payment Card Industry Data Security Standard (PCI-DSS) Health Insurance Portability and Accountability Act (HIPAA) Federal Information Security Management Act (FISMA) European Union Agency for Network and Information Security (ENISA) General Data Protection Regulation (GDPR) :
- One of the most popular areas of AI adoption with FSI is around transforming customer experiences. This includes using Amazon Polly, our text to speech service, to automate communications with customers (FICO; Bloomberg) or building chatbots using Amazon Lex (Old Mutual; Liberty Mutual). Additionally, as with all AWS services, each AI service is a building block that can be built on top of each other to provide an end to end workflow. For example, voice data today is stored for compliance reasons and generally not mined for business insights. With the AI services, we can now take the voice data stored by Amazon Connect -> Transcribe it into text -> Translate it (if necessary) -> Run Comprehend across this text to identify the entities involved and the sentiment of the conversation to better understand the context of the call. [Note to presenter]: Individual slides with details for each customer reference is available in the appendix.
- Another common area of AI adoption is in the area of identity verification and compliance. For example, Aella Credit uses Amazon Rekognition, which is our object and facial recognition service, to match the face of the user with that of his/her ID as well as a private database and provides instant loans to individuals with a verifiable source of income. FINRA uses Amazon Comprehend, a natural language processing service, to extract names of individuals and organizations and match them to their Central Registration Depository (CRD) records. FINRA then passes this data into Amazon Neptune, a graph database, to better understand the relationship across the various individuals and organizations. WeLend, social lending platform based in Hong Kong, utilizes Amazon’s AI tools, such as Amazon Rekognition, to identify and categorize each document uploaded to their application, such as a driver’s license or ID card. PrivatBank, the largest commercial bank in Ukraine, launched a biometric payment system called FacePay24, which uses Amazon Rekognition, that allows users to pay for purchases by looking at a tablet’s camera. [Note to presenter]: Individual slides with details for each customer reference is available in the appendix.
- It provides timestamps for every word so you can align the text with the audio for subtitling and search use cases The output text has punctuation, which makes it easy to read
- To gain insights from your business’ conversations, you can stream audio from telephone calls to Amazon Kinesis Video Streams in real-time. You can quickly build audio analytics applications through integration with Amazon Comprehend, Amazon Transcribe, Amazon SageMaker, and other common machine learning (ML) libraries. For example, you may need to analyze contact center conversations for Quality Assurance and training purposes. Simply download the pre-packaged lambda function for call recording, enable streaming on the Voice Connector, create your S3 bucket then ensure that the Lambda execution role has access to the services that you plan to enable, this would include Kinesis Video Streams and S3. Another example would be where you add Lambda execution access to Amazon Transcribe to build a compliance app that checks transcriptions to verify that proper disclosures were issued . Like when someone buys stocks or signs up for a service over the phone. When using Voice Connector for SIP trunking, simply select the streaming option. Even when you do not use Voice Connector for SIP trunking, you can still use Voice Connector to stream calls by using SIPREC, an industry standard for sending media that is supported by most SBCs, contact centers, and PBXs.
- O Amazon Textract é capaz de extrair dados rapidamente e de forma precisa, com a flexibilidade de suportar diversos tipos de documentos. Ele ajuda a reduzir, ou até eliminar, o esforço manual de extrair dados, reduzindo o custo de muitas soluções que necessitam de esfoço manual ainda. Uma das vantagens do uso de serviços de AI é que você não precisa ter experiência com Machine Learning. Através de chamadas de API, usando os SDK da AWS, você poderá enviar documentos do tipo JPG, PNG, ou PDF, e fazer tanto a extração de textos de documentos ou formulários. A vantagem de uso de um serviço de AI também é a agilidade de criar um produto e disponibilizá-lo no Mercado mais rapidamente, assim como também não ter que se preocupar em gerenciar a infraestrutura e a segurança do serviço, deixando com a AWS assuma essa responsabilidade na camada do serviço em si. Amazon Textract can extract data quickly, accurately and with flexibility from various document types. This helps reduce or in some cases eliminate manual effort that we see a lot of our customers facing. By extracting data and reduce manual effort this saves money and processing costs and doesn’t require any special machine learning skills. The beauty of the AWS AI services is, no ML experience is required so you can get to market faster.
- O Amazon Comprehend também é capaz de utilizer jargões específicos de um segment de Mercado, ao aprender entidades customizadas. Sem a necessidade de conhecer como algoritmos de Machine Learning para NLP funcionam, nossos clients podem se beneficiar das técnicas de AutoML para criar modelos customizados de NLP. Utilizamos uma rede neural Deep Learning desenvolvida por nós, onde utilizamos uma técnica chamada Transfer Learning (transferência de conhecimento). Dessa forma, treinamos seus modelo customizado sem a necessidade de ter um grande dataset, ao trazermos um modelo previamente treinado com grandes datasets que envolvem múltiplos domínios. A junção deste modelo previamente treinado com os jargões customizados, resulta em um modelo customizado que entende detalhes específicos de um segment de Mercado. Além disso, é possível criar um modelo específico para análise dos documentos de forma a incluir estes jargões.
- Vejamos essa estrutura customizada. A partir do modelo native do Amazon Comprehend seríamos capazes de extrair dados como pessoa e organização. Entretanto, o Amazon Comprehend não saberia pegar as informações de parte e qual ação tomar. Como usamos a função de Custom Entities e treinamos um modelo customizado para analisar os textos, o Amazon Comprehend foi capaz de identificar exatamente qual a peça que o cliente estava mencionando, assim como a ação que ele gostaria de tomar. Com este tipo de abordagem, podemos usar o Amazon Comprehend não só para nos ajudar a analisar os textos, mas Podemos usar estas informações para melhorar fluxos de conversa em chatbots, para construer dashboards que demonstrem o grau de satisfação dos clients em relação aos nossos produtos, analisar chamadas telefonicas para fins de melhorar os atendentes, entre muitas outras opções.
- Amazon Augmented AI (Amazon A2I) makes it easy to build the workflows required for human review of ML predictions. Amazon A2I brings human review to all developers, removing the undifferentiated heavy lifting associated with building human review systems or managing large numbers of human reviewers. Many machine learning applications require humans to review low confidence predictions to ensure the results are correct. For example, extracting information from scanned mortgage application forms can require human review in some cases due to low-quality scans or poor handwriting. But building human review systems can be time consuming and expensive because it involves implementing complex processes or “workflows”, writing custom software to manage review tasks and results, and in many cases, managing large groups of reviewers. Amazon A2I makes it easy to build and manage human reviews for machine learning applications. Amazon A2I provides built-in human review workflows for common machine learning use cases, such as content moderation and text extraction from documents, which allows predictions from Amazon Rekognition and Amazon Textract to be reviewed easily. You can also create your own workflows for ML models built on Amazon SageMaker or any other tools. Using Amazon A2I, you can allow human reviewers to step in when a model is unable to make a high confidence prediction or to audit its predictions on an on-going basis.
- Amazon Augmented AI (Amazon A2I) makes it easy to build the workflows required for human review of ML predictions. Amazon A2I brings human review to all developers, removing the undifferentiated heavy lifting associated with building human review systems or managing large numbers of human reviewers. Many machine learning applications require humans to review low confidence predictions to ensure the results are correct. For example, extracting information from scanned mortgage application forms can require human review in some cases due to low-quality scans or poor handwriting. But building human review systems can be time consuming and expensive because it involves implementing complex processes or “workflows”, writing custom software to manage review tasks and results, and in many cases, managing large groups of reviewers. Amazon A2I makes it easy to build and manage human reviews for machine learning applications. Amazon A2I provides built-in human review workflows for common machine learning use cases, such as content moderation and text extraction from documents, which allows predictions from Amazon Rekognition and Amazon Textract to be reviewed easily. You can also create your own workflows for ML models built on Amazon SageMaker or any other tools. Using Amazon A2I, you can allow human reviewers to step in when a model is unable to make a high confidence prediction or to audit its predictions on an on-going basis.
- We’re innovating on behalf of our customers to deliver the broadest and deepest set of machine learning capabilities for builders of all levels of expertise. At each layer of the stack, we’re investing in removing the undifferentiated heavy lifting so your teams can move faster. We also now have purpose-built solutions for industries, such as industrials and healthcare. Let’s take a walk through this the three layers.
- As I mentioned previously, SageMaker is a service with a lot of different features and capabilities in it. We typically talk about those capabilities as falling into four categories: Data preparation, the model build phase, training and tuning, and deployment and management (or hosting). These four categories really address the needs that ML builders have when dealing with each stage of a model’s lifecycle. Later in this presentation I can give you a full feature tour of as many of these features that might be interesting to you. ===== Interaction Opportunity ===== I’ll pause here for a moment to see if there are any capabilities that you see listed here which may be of particular interest to you and your organization at this point in time? Great – well we can certainly dive deeper into that in just a few moments.
- It’s worth noting that there are a number of key considerations when applying machine learning, especially for financial institutions. We have reference architectures and best practices that we can share with you around the security and governance aspects of machine learning. These are materials that are developed and compiled based on our interactions with customers such as Vanguard, Moody’s, Intuit, and others.