Over the last 5 years, artificial intelligence (AI) and machine learning (ML) have evolved from a niche activity to a rapidly growing mainstream endeavor. Today, more than 100,000 customers across numerous industries rely on AWS for ML and AI initiatives that infuse AI into a broad range of business use cases to automate repetitive and mundane tasks—from intelligent demand planning to document processing and content moderation. AWS AI services help customers create smoother, faster, and more efficient engagements with customers, driving greater efficiencies and lowering operational costs.
At AWS re:Invent, Amazon Web Services, Inc. has announced a series of features and enhancements across its portfolio of AI services, including purpose-built solutions to solve industry-specific challenges, representing a deeper integration of AI into everyday experiences. The new capabilities include Amazon Textract Analyze Lending to improve loan-document processing efficiency, Amazon Transcribe Call Analytics to analyze in-progress contact center calls, Amazon Kendra support for tabular search in HTML and seven new languages, Amazon HealthLake Imaging for medical image storing; Amazon HealthLake Analytics with multi-modal data querying capabilities, and broader programming languages support and easier administration in Amazon CodeWhisperer. These AI service innovations provide vertical markets and horizontal functions with deeper, real-time insights and cost-saving efficiencies to drive transformation across industries.
These new capabilities enhance AWS’s AI offerings at the top of its three-layer ML stack. The bottom layer includes foundational components (ML hardware and ML software libraries) to help customers build their own ML infrastructure, and the middle layer—Amazon SageMaker—is a fully managed ML development environment. The top layer of AI services brings ML to business use cases such as transcribing contact center calls, processing documents, and improving healthcare outcomes. Customers can use AWS AI services with no ML expertise required.
Customers from different industries rely on AWS AI services to improve efficiency and reduce operational costs. For example, WaFd Bank, a full-service US bank, improved its customer experience with Talkdesk (a global cloud contact center company) and AWS Contact Center Intelligence (CCI) solutions, reducing call times by up to 90%. And State Auto, a property and casualty insurance holding company, automated the property inspection process using Amazon Rekognition (a computer vision service), increasing the number of claims it reviews for potential fraud by 83%.
Amazon Textract Analyze Lending makes it easy to classify and extract mortgage loan data
Today, mortgage companies process large volumes of documents to extract business-critical data and make decisions on loan applications. For example, a typical US mortgage application can encompass 500 or more pages of diverse document types, including W2 forms, paystubs, bank statements, Form 1040, 1003, and many more. The lender’s loan processing application has to first understand and classify each document type to ensure that it is processed the right way. After that, the loan processing application has to extract all the data on each page of the document. The data in these documents exists in different formats and structures, and the same data element can have different names on different documents—for example, “SSN,” or “Social Security Number,” which can lead to inaccurate data extraction. So far, the classification and extraction of data from mortgage application packages have been primarily manual tasks. Furthermore, mortgage companies have to manage demand for mortgages that can fluctuate substantially during a year, so lenders are unable to plan effectively and must often allocate resources to process documents on an ad hoc basis. Overall, mortgage loan processing is still manual, slow, error-prone, and expensive.
Amazon Textract (AWS’s AI service to automatically extract text, handwriting, and data from scanned documents) now offers Amazon Textract Analyze Lending to make loan document processing more automated, faster, and cost-effective at scale. Amazon Textract Analyze Lending pulls together multiple ML models to classify various documents that commonly occur in mortgage packages, and then extracts critical information from these documents with high accuracy to improve loan document processing workflows. For example, it can now perform signature detection to identify whether documents have required signatures. It also provides a summary of the documents in a mortgage application package and identifies any missing documents. For instance, PennyMac, a financial services firm specializing in the production and servicing of US mortgage loans, uses Amazon Textract Analyze Lending to process a 3,000-page mortgage application in less than 5 minutes. Previously, PennyMac’s mortgage document processing required several hours of reviewing and preparing a loan package for approval.
Amazon Transcribe Call Analytics for improved end-user experiences
In most customer-facing industries such as telecom, finance, healthcare, and retail, customer experiences with call centers can profoundly impact perceptions of the company. Lengthy call-resolution times or the inability to deal with issues during live interactions can lead to poor customer experiences or customer churn. Contact centers need real-time insights into customer-experience issues (e.g., a product defect) while calls are in progress. Typically, developers use multiple AI services to generate live call transcriptions, extract relevant real-time insights, and manage sensitive customer information (e.g. identify and redact sensitive customer details) during live calls. However, this process adds unnecessary complexity, time, and cost.
Amazon Transcribe, an automatic speech recognition (ASR) service that makes it easy for developers to add speech-to-text capabilities to their applications, now supports call analytics to provide real-time conversation insights. Amazon Transcribe Call Analytics now provides real-time conversation insights that help analyze thousands of in-progress calls, identify call sentiment (e.g. calls that ended with a negative customer sentiment score), detect the potential reason for the call, and spot issues such as repeated requests to speak to a manager. Amazon Transcribe Call Analytics combines powerful automatic speech NLP models that are trained specifically to improve overall customer experience. With Amazon Transcribe Call Analytics, developers can build a real-time system that provides contact center agents with relevant information to solve customer issues or alert supervisors about potential issues. Amazon Transcribe Call Analytics also generates call summaries automatically, eliminating the need for agents to take notes and allowing them to focus on customer needs. Furthermore, Amazon Transcribe Call Analytics protects sensitive customer data by identifying and redacting personal information during live calls.
Amazon Kendra adds new search capabilities
Today, in the face of rapid growth in the volume and variety of data, enterprise search tools struggle to examine and uncover key insights stored across enterprise systems in heterogenous data formats and in different languages. Conventional enterprise search solutions are unable to find knowledge stored in unstructured datasets like HTML tables because it requires extracting information from two-dimensional formats (rows and columns). Sometimes, the information a customer may be seeking could exist in different languages, making the search even more challenging. As a result, enterprise employees waste time searching for information or are unable to perform their duties.
Amazon Kendra (AWS’s intelligent search service powered by ML) offers a new capability that supports tabular search in HTML. Customers can find more precise answers faster in HTML documents, whether they’re in the narrative body or tabular form, by using natural language questions. Amazon Kendra can find and extract precise answers from HTML tables by performing deeper analyses of HTML pages and using new specialized deep learning models that intelligently interpret columns and rows to pinpoint relevant data. Amazon Kendra is also adding semantic support for seven new languages (in addition to English): French, Spanish, German, Portuguese, Japanese, Korean, and Chinese. Customers can now ask natural language questions and get exact answers in any of the supported languages. One of AWS’s biopharmaceutical customers, Gilead Sciences Inc., increased staff productivity by cutting internal search times by roughly 50% using Amazon Kendra.
Amazon HealthLake offers next-generation imaging solutions and precision health analytics
Healthcare providers face a myriad of challenges as the scale and complexity of medical imaging data continues to increase. Medical imaging is a critical tool to diagnose patients, and there are billions of medical images scanned globally each year. Imaging data accounts for about 90% 1 of all healthcare data, and analyzing these complex images has largely been a manual task performed by experts and specialists. It often takes data scientists and researchers weeks or months to derive important insights from medical images, slowing down decision-making processes for healthcare providers and impacting patient-care delivery. To address these challenges, Amazon HealthLake (a HIPAA-eligible service to store, transform, query, and analyze large-scale health data) is adding two new capabilities for medical imaging and analytics:
Amazon HealthLake Imaging is a new HIPAA-eligible capability that enables healthcare providers and their software partners to easily store, access, and analyze medical images at petabyte scale. The new capability is designed for fast, subsecond image retrieval in clinical workflows that healthcare providers can access securely from anywhere (e.g., web, desktop, or phone) and with high availability. Typically, health systems store multiple copies of the same imaging data in clinical and research systems, leading to increased storage costs and complexity. Amazon HealthLake Imaging extracts and stores just one copy of the same image to the cloud. Customers can now access existing medical records and run analysis applications from a single encrypted copy of the same data in the cloud with normalized metadata and advanced compression. As a result, Amazon HealthLake Imaging can help providers reduce the total cost of medical imaging storage by up to 40%.
Amazon HealthLake Analytics is a new HIPAA-eligible capability that makes it easy to query and derive insights from multi-modal health data (e.g., imaging, text, or genetics), at the individual or population levels, with the ability to share data securely across the enterprise. It removes the need for healthcare providers to execute complex data exports and data transformations. Amazon HealthLake Analytics automatically normalizes raw health data from disparate sources (e.g., medical records, health insurance claims, EHRs, or medical devices) into an analytics and interoperable format in minutes. The new capability reduces what would otherwise take months of engineering effort to allow providers to focus on what they do best—delivering patient care.
Amazon CodeWhisperer offers broader support and easier administration
While the cloud has democratized application development through on-demand access to compute, storage, database, analytics, and ML, the traditional process of building software applications in any industry remains time-intensive. Developers must still spend significant time writing repetitive code not directly related to the core problems they want to solve. Even highly experienced developers find it difficult to keep up with multiple programming languages, frameworks, and software libraries, while ensuring they follow correct programming syntax and coding best practices.
With these new features and capabilities, AWS continues to expand its portfolio of the broadest and deepest set of AI services. AWS also recognizes that as AI-powered use cases become pervasive, it is important that these capabilities are built in a responsible way. AWS is committed to building its services in a responsible manner and supporting customers to help them deploy AI responsibly. By enabling customers to more easily and responsibly add new and expanded AI capabilities to their applications and workflows, AWS is unleashing even greater innovation and helping businesses reimagine how they approach and solve some of their most pressing challenges. To learn more about AWS’s comprehensive approach to responsible AI, visit Responsible use of artificial intelligence and machine learning.
1S. K. Zhou et al., “A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises,” in Proceedings of the IEEE, vol. 109, no. 5, pp. 820-838, May 2021, doi: 10.1109/JPROC.2021.3054390.
About the Author
Bratin Saha is the Vice President of Artificial Intelligence and Machine Learning at AWS.
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