FSI Service Spotlight Blog Series
The FSI Service Spotlight Blog series aims to provide financial services customers a deep dive into the following five key considerations of a particular AWS cloud service. This will allow financial services customers to accelerate realization of business value from consuming the particular AWS service—from developing personalized digital experiences, breaking down data silos, launching new products, driving down margins for existing products, to proactively addressing global risk and compliance requirements. The five key areas for consideration include:
1. Achieving compliance
2. Data protection
3. Isolation of compute environments
4. Automating audits with APIs
5. Operational access and security
Each of these five areas will include specific guidance that can help you streamline service approval for the particular AWS service, which may need to be adapted to your specific use case and environment. We can help you navigate the approval process of the wide range of AWS services available, so you can offload undifferentiated heavy lifting to AWS and focus on your core business objectives.
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Financial institutions are increasingly adopting Amazon Machine Learning services to easily build and train machine learning models to effectively detect online payment fraud or build a recommendations platform that provides more value to customers and enables data scientists to move projects from design to production quickly. An Amazon SageMaker notebook instance is a machine learning (ML) EC2 instance running the Jupyter Notebook App. SageMaker manages creating the instance and related resources for you.
A common use case for Amazon Comprehend for financial institutions is to analyze call transcriptions from their call centers to gather insights into their customer calls. This allows a financial institution to uncover common trends, personalize messages and offers, and ensure call center staff have all the available information on a given client to provide the best experience.
Amazon Textract is a fully managed AI service that extracts text, handwriting, and other data from scanned documents that goes beyond simple optical character recognition (OCR) to identify and understand the relationship of the data from forms and tables. In financial services, Financial institutions are leveraging Amazon Textract for a number of workloads: to automate the loan applications processing and claims processing pipeline, resulting in a great customer experience while increasing operational efficiencies.
Amazon Transcribe uses a deep learning process called automatic speech recognition (ASR) to recognize speech in audio files and transcribe them into text. Financial services customers can use Amazon Transcribe to convert speech to text and to create applications that incorporate the content of audio files. Managed call centers such as Contact Lens for Amazon Connect builds on Amazon Transcribe to generate call transcripts and provide accurate call transcriptions, redaction of sensitive data, and automated call metrics to determine the effectiveness of the contact center.
Amazon Lex, an AI service for building conversational interfaces for applications using voice and text, is powered by the same technology as Amazon’s voice assistant Alexa and enables any developer to build conversational bots quickly, with no deep learning expertise necessary. By using an Amazon Lex bot, financial institutions can provide their clients 24/7 availability to get questions answered and tasks performed without needing to speak to an agent.
Amazon Athena is a serverless interactive SQL query service that enables customers to query large volumes of data stored on Amazon Simple Storage Service (Amazon S3) or in other sources without the need to manage the underlying infrastructure or having to set up complex ETL processes. Customers simply register their preexisting S3 datasets as Tables in Athena’s underlying metastore and can immediately begin querying it using standard SQL. With the rise in prevalence of data lake and lakehouse architectures, Athena has become a popular option for FSI customers for interactive SQL analytics.
Amazon Elastic Kubernetes Service (Amazon EKS) gives you the flexibility to start, run, and scale Kubernetes applications in the AWS Cloud or on-premises. Amazon EKS helps you provide highly available and secure clusters and automates key tasks such as patching, node provisioning, and updates. Customers such as Intel, Snap, Intuit, GoDaddy, and Autodesk trust EKS to run their most sensitive and mission-critical applications.
Amazon Elastic Container Service (Amazon ECS) is a fully managed container orchestration service that helps you easily deploy, manage, and scale containerized applications. It deeply integrates with the rest of the AWS platform to provide a secure and easy-to-use solution for running container workloads in the cloud and now on your infrastructure with Amazon ECS Anywhere.
Amazon ECS leverages serverless technology from AWS Fargate to deliver autonomous container operations, which reduces the time spent on configuration, patching, and security. Instead of worrying about managing the control plane, add-ons, and nodes, Amazon ECS enables you to rapidly build applications and grow your business.
Amazon Kendra is an intelligent search service powered by machine learning. Amazon Kendra reimagines enterprise search for your websites and applications so your employees and customers can easily find the content they are looking for, including business documents, corporate glossaries, and internal websites, even when it is scattered across multiple locations. Amazon Kendra provides native and partner-developed connectors for popular data sources like Amazon Simple Storage Service (Amazon S3), SharePoint, ServiceNow, OneDrive, Salesforce, and Confluence so you can easily add data from different content repositories and file systems into a centralized location.
Amazon Redshift is a fast, fully managed, cloud-native and cost-effective data warehouse. It enables fast, simple and cost-effective analysis of customer data using standard SQL and a customer’s existing Business Intelligence (BI) tools. It allows AWS customers to run complex analytic queries against terabytes to petabytes of structured and semi-structured data, using sophisticated query optimization, columnar storage on high-performance storage, and massively parallel query execution.
For this edition of the Service Spotlight, we are covering AWS Glue, a fully managed extract, transform, and load (ETL) tool that makes it easy to prepare and load data for analytics, machine learning, and development. You simply point AWS Glue to your data source, and AWS Glue discovers your data and stores the associated metadata (e.g. table definition and schema) in the AWS Glue Data Catalog. Once catalogued, your data is immediately searchable, queryable, and available for ETL.
For this edition of the Service Spotlight, we are covering Amazon Aurora (Aurora), a fully managed relational database engine that’s compatible with MySQL and PostgreSQL. Aurora MySQL and PostgreSQL combine the speed and reliability of high-end commercial databases with the simplicity and cost-effectiveness of open-source databases. The code, tools, and applications you use today with your existing MySQL and PostgreSQL databases can be used with Aurora.
For this edition of the Service Spotlight, we are covering AWS Lambda, which is a serverless compute service that removes the heavy lifting of provisioning and managing underlying infrastructure to enable teams to build and deploy new application and functionality quickly. AWS Lambda natively supports seven programming languages: Java, Go, PowerShell, Node.js, C#, Python, and Ruby. It further supports other languages through custom runtimes.
For this edition of the Service Spotlight, we’re covering AWS Lake Formation, a fully managed service that helps you build, secure, and manage your data lake at scale. Lake Formation provides a central console where you can discover data sources, set up transformation jobs to move data to an Amazon Simple Storage Service (Amazon S3) data lake, remove duplicates and match records, catalog data for access by analytic tools, configure data access and security policies, and audit and control access from AWS analytic and machine learning (ML) services.
For this edition of the Service Spotlight, we’re covering Amazon Connect. Amazon Connect is an omnichannel cloud contact center. You can create a contact center in a few steps, add agents anywhere, and start engaging with your customers. It supports personalized customer experiences through dynamic chat and voice communications. Meanwhile, agents can conveniently handle all customers from a single interface. Furthermore, Amazon Connect scales up or down to meet demand, with the flexibility to onboard tens of thousands of agents working from anywhere. This flexibility can save up to 80% compared to traditional contact center solutions, along with minimum fees, long-term commitments, or upfront licensing charges.
For this edition of the Service Spotlight, we’re covering AWS Glue DataBrew. AWS Glue DataBrew makes it easy for data scientists and data analysts to clean and normalize data using a visual interface, reducing the time it takes to prepare data by up to 80%. With Glue DataBrew, you can visualize, clean, and normalize data directly from your data lake, data warehouses, and databases. You can choose from over 250 built-in transformations to automate data cleaning and normalization tasks, and save these transformation steps so they’re applied to new data as it comes in. You can evaluate the quality of your data by profiling it to understand data patterns and detect anomalies, all without writing a single line of code. As part of AWS Glue, Glue DataBrew is serverless so you don’t need to manage the infrastructure. You pay only for what you use, with no upfront commitment.
In this edition of the Financial Services Industry (FSI) Services Spotlight monthly blog series, we highlight five key considerations for customers running workloads on Amazon FSx for NetApp ONTAP (FSx for ONTAP) achieving compliance, data protection, isolation of compute environments, audits with APIs, and access control/security. Across each area, we will examine specific guidance, suggested reference architectures, and technical code to help streamline service approval of FSx for ONTAP.
In this edition of the Financial Services Industry (FSI) Services Spotlight monthly blog series, we highlight five key considerations of Elastic Load Balancing (ELB): achieving compliance, data protection, isolation of compute environments, automating audits with APIs, and operational access and security. Each of the five areas will include specific guidance, suggested reference architectures, and technical code that can help streamline service approval of ELB in your environment. These may need to be adapted to your business, compliance, and security requirements