Machine Learning for Financial Services

Featured content:
Mining Intelligent Insights with Machine Learning in Financial Services

Machine Learning Customer Case Studies


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    Customer story

    Intuit’s Machine Learning Helps Customers Save More Money

    Intuit, with its products and platforms like QuickBooks and TurboTax, has spent over a decade developing machine learning (ML) and AI applications. Using Amazon SageMaker, Intuit developed ML models that can pull a year’s worth of bank transactions to find deductible business expenses for customers, and reduced deployment time by 90%, from 6 months to 1 week.
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    Customer story

    At Capital One, Enhancing Fraud Protection With Machine Learning

    Capital One is one of the largest banks in the United States, and the largest digital bank. As consumers continue to forgo brick-and-mortar banks for digital-first banking, Capital One has embraced new technologies, adopting and applying AI and machine learning solutions to nearly every facet of the business and infusing the customer experience with intelligence.
    Read now »
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    Customer story

    FINRA Steps Up Market Surveillance by Processing Exabytes of Data on AWS

    Financial Industry Regulatory Authority (FINRA) stores and analyzes a staggering quantity of information on a daily basis. As one of the main regulatory bodies of finance in the US, it presides over some 36 billion market events on average each day and stores petabytes of both new and historic data. Going forward, FINRA plans to use machine learning algorithms on its data to better identify potential market manipulation activities.
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    Video

    Liberty Mutual Redefines the Customer Experience

    Liberty Mutual Insurance was looking to redefine how their customers experience insurance. With Amazon Lex, Liberty Mutual Insurance used natural language processing to create a chatbot that would help employees answer questions, find information, and perform simple tasks.
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Web and mobile refers to the collection of tools and technologies required to power internet applications. AWS provides on-demand access to scalable web and application servers, storage, databases, content delivery, cache, search, and other application services that make it easier to build and run apps that deliver a great customer experience.

Using AWS, Pinterest has tripled its use of storage and compute over just two years without worrying about reliability or scalability. Pinterest provides one of the world's largest visual-bookmarking tools, with more than 200 million users and 2 billion boards. The company has used a variety of AWS services to scale its processing, storage, and data-analysis workloads to help developers focus on delighting customers.


Explore segments:

Banking & Payments

Optimize, accelerate, and automate every aspect of your business – from customer service delivery to data-enabled risk management.

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Capital Markets

AWS enables organizations to innovate and transform to move faster, better service customers, and increase shareholder value.

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Insurance

Enable an agile infrastructure to optimize and innovate your business, deepen customer relationships, and improve risk profiles with AWS.

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Events and Webinars

  • View all AWS Events

    AWS holds events, both online and in-person, bringing the cloud computing community together to connect, collaborate, and learn from AWS experts.

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  • AWS Summits

    Hear from AWS leaders, experts, partners, and customers. Learn by attending technical breakout sessions, demonstrations, hands-on workshops, labs, and team challenges. Network with AWS partners and your peers in our Partner and Solutions Expo.

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  • AWS Financial Services Cloud Symposiums

    Join us for the AWS Financial Services Cloud Symposium, an event designed for financial services professionals seeking new strategies to drive business transformation.

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Start Training on Machine Learning with AWS

  • Building Secure Environments Workshop

    We will introduce you to the recommended practices for building a secure data science environment powered by Amazon SageMaker. You will learn how to combine multiple secure by default services to enforce secure configurations and create a data science environment that meets common security requirements. You will cover various security topics and work through hands-on lab materials to exercise and explore the many security features available with Amazon Web Services.

  • Using Secure Environments Workshop

    You will be introduced to the recommended practices for using Amazon SageMaker in a secure data science environment. Like many other AWS services, Amazon SageMaker is secure by default. Throughout this workshop you will see how you can work in a secured data science environment. You will cover the many stages of the machine learning lifecycle and be provided with Jupyter notebooks to step through that lifecycle while maintaining a high bar for security.

  • AWS Power Hour: Machine Learning

    This 7-week program demonstrates how to build apps with AWS AI Services. You can catch the live stream on Thursdays at 4pm PT or watch the recorded streams.

Thought Leadership

Whitepaper: Machine Learning Best Practices for Financial Services



Based on feedback from customers running workloads in a highly regulated environment, AWS recently published a whitepaper that outlines security and model governance considerations for financial institutions using ML applications. It illustrates how financial institutions can create a secure machine learning environment on AWS and use best practices in model governance based on the firm’s risk tolerance, integration with existing governance, and regulatory expectations.
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Article: Infusing Intelligence into Financial Services



This article authored in collaboration with Wall Street Journal Custom Content, covers a broad range of machine learning use cases within the financial services industry–ranging from identity verification, automating internal processes and building custom models for assessing loans. Whether to better serve customers or improve internal processes, machine learning plays a fundamental role for financial institutions. Learn how global financial institutions are harnessing the power of data to expand their service offerings and serve customers in new ways.
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Article: The AI & Machine Learning Imperative



In collaboration with MITSloan Management Review, the AI & Machine Learning imperative offers insights from leading academics and practitioners in data science and artificial intelligence. The article series explore how managers and companies can overcome challenges and identify opportunities by assembling the right talent, stepping up their own leadership, and reshaping organizational strategy.
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Whitepaper: The Enterprise Machine Learning Guide



Advances in machine learning technology, coupled with the growing abundance and importance of data, have created a perfect storm for transformation in the Financial Services industry. As financial institutions aggregate data across sources, deriving actionable and timely insights has become increasingly critical. Download this guide to learn more.
Download Now »

Customer Stories: Machine Learning customer stories: Mastercard’s NuData and Coinbase



Read these customer stories to discover how Machine learning is creating better business results across nearly every industry—including financial services. While risk management use cases abound, financial institutions of all sizes are using ML to enhance document processing, pricing and product recommendations, trading and analytics, customer experience, and more.
Read Now »

eBook: Machine Learning Journey



This blog discusses how to think about and understand financial services cloud security and AWS services, how to get started with automation in the cloud, and provides self-service cloud security resources.
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Machine Learning Thought Leadership

Whitepaper
The Enterprise Machine Learning Guide
Advances in machine learning technology, coupled with the growing abundance and importance of data, have created a perfect storm for transformation in the Financial Services industry. As financial institutions aggregate data across sources, deriving actionable and timely insights has become increasingly critical. Download this guide to learn more.


Whitepaper
Machine Learning Best Practices for Financial Services
Based on feedback from customers running workloads in a highly regulated environment, AWS recently published a whitepaper that outlines security and model governance considerations for financial institutions using ML applications. It illustrates how financial institutions can create a secure machine learning environment on AWS and use best practices in model governance based on the firm’s risk tolerance, integration with existing governance, and regulatory expectations.
Customer Stories
Machine Learning customer stories: Mastercard’s NuData and Coinbase
Read these customer stories to discover how Machine learning is creating better business results across nearly every industry—including financial services. While risk management use cases abound, financial institutions of all sizes are using ML to enhance document processing, pricing and product recommendations, trading and analytics, customer experience, and more.
eBook
Machine Learning Journey
It’s time for organizations to take the leap with machine learning and forge ahead with confidence. Follow the proven path to machine learning success. Read the Machine Learning Journey eBook to discover how to transform investments into business-differentiating solutions.

Start Training on Machine Learning with AWS

Dive deep into the same machine learning (ML) curriculum used to train Amazon’s developers and data scientists. We offer 65+ ML training courses totaling 50+ hours, plus hands-on labs and documentation, originally developed for Amazon's internal use. Developers, data scientists, data platform engineers, and business decision makers can use this training to learn how to apply ML, artificial intelligence (AI), and deep learning (DL) to their businesses unlocking new insights and value. Validate your years of experience building, training, tuning, and deploying ML models using the AWS Cloud with an AWS Certification.


This free, on demand event is for aspiring machine learning developers, applications developers, and data scientists to dive deep into how the cloud can help you build artificial intelligence (AI) applications and machine learning (ML) models.

On Demand

This free, online event is for ML aspiring developers, applications developers, and data scientists that want to learn how to build artificial intelligence/machine learning (AI/ML) into new and existing applications. In this online event featuring instructor-led sessions, you will learn how to conceptualize the end-to-end process of building ML models.

Attend Live

AWS re:Invent 2019

Las Vegas, Nevada: Dec 2nd-6th

Join us for deep technical sessions, hands-on bootcamps, hackathons, workshops, chalk talks, keynotes, and of course, some uniquely Amazonian fun.

Use code to save $100: SALAMSA19

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Welcome to the world’s first global autonomous racing league, open to anyone. It’s time to race for prizes, glory, and a chance to advance to the AWS DeepRacer Championship Cup at re:Invent 2019 to win the coveted AWS DeepRacer Cup. Get on the track to compete online in the monthly Virtual Circuit races or in-person at Summit Circuit race events worldwide.

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