Machine Learning for Financial Services

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

Machine Learning Customer Case Studies


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    Customer Experience

    Biz2Credit Streamlines Customer Onboarding by Using AI/ML

    Biz2Credit is a fully managed lending platform that helps financial institutions extend credit to small businesses. Biz2Credit improves process efficiency for loan applications by using AI/ML to digitalize and analyze customer information.
    Learn More »
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    Customer Experience

    John Hancock Transforms the Customer Experience

    John Hancock, one of the largest life insurers in the United States, leverages Contact Lens for Amazon Connect to understand the sentiment and trends of customer conversations and anticipate why customers are calling, which allows their agents to better serve their customers.
    Learn More »
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    Intelligent Document Processing

    BlueVine Provides Small Business Relief Loans Using ML

    BlueVine offers a modern approach to small business financing by helping business owners get access to Paycheck Protection Program (PPP) funds, saving more than 400,000 jobs. With Amazon Textract, Bluevine achieved a high degree of automation, which helped the service/risk teams focus on serving consumers faster.
    Learn More »
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    Customer Experience

    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.
    Learn More »

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    Credit Decisioning

    CreditVidya Extends the Loan Market to Millions

    CreditVidya is a startup headquartered in India whose underwriting technology is opening the country’s loans market to over 250 million financially excluded citizens. CreditVidya’s technology is reducing the cost of processing loans from about $2 to less than one cent while overcoming a lack of credit history or collateral by leveraging loan applicants’ digital footprints to measure their creditworthiness.
    Learn More »
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    Fraud Detection and Prevention

    Euler Hermes Rides Out Market Disruption Using AWS

    Euler Hermes, part of the Allianz group, is a credit insurance company that uses Amazon SageMaker to quickly detect any suspicious domains registered that could be used to exploit the Euler Hermes brand or its products. In under seven months, it was able to launch a new internal ML service from ideation to production. It can now identify URL squatting fraud within 24 hours after the creation of a malicious domain.
    Learn More »
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    Fraud Detection and Prevention

    NuData Security Mitigates Fraudulent Attacks in Real Time

    The behavioral biometrics firm, NuData Security, a Mastercard company, uses big data analytics and machine learning to verify that the person using a particular card is authorized to do so. In addition to using traditional methods such as passwords, security-questions, and birth dates, NuData uses passive biometrics to authenticate account holders’ identities by analyzing their digital profiles.
    Learn More »
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    Credit Decisioning

    OakNorth Leverages Big Data and Machine Learning to Redefine SME Lending

    OakNorth Bank is a financial services and fintech platform focused on using data analytics to provide a better borrowing experience for growth businesses. They created an ML solution to automate the loan decision-making process and speed up the approval process for small and medium-sized businesses.
    Learn More »

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.
    Learn more »
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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.
    Learn more »
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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.
    Watch now »

Machine Learning Customer Case Studies


  • FinServe_learn1.png
    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.
    Learn more »
  • FinServe_certified-files1.png
    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 »
  • FinServe_learn1.png
    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.
    Learn more »
  • FinServe_certified-files1.png
    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.
    Watch now »

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:

Customer Experience

Biz2Credit streamlines customer onboarding, Learn more »

John Hancock transforms the customer experience. Learn more »

Kabbage Improves Loan Origination with Machine Learning. Learn more »

Pitchbook Helps Private Capital Markets Extract Insights. Learn more »

Capital Markets

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

Learn more

Insurance

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

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Start Training on Machine Learning with AWS

  • AWS Machine Learning University

    Learn how to apply machine learning (ML), artificial intelligence (AI), and deep learning (DL) to your business, unlocking new insights and value. Explore real-world examples and labs based on problems we've solved at Amazon using ML. Access 65+ digital courses (many of them free).

    Learn More »

  • AWS ML Embark Program

    Born out of thousands of successful customer implementations and Amazon’s own experience scaling the use of machine learning (ML) in the organization, the AWS Machine Learning Embark program combines the training, coaching, and implementation support needed to launch your company’s machine learning journey and transform your development teams into machine learning practitioners.

    Learn More »

  • AWS Power Hour: Machine Learning

    Check out the AWS Power Hour: Machine Learning, a weekly Twitch show presented by AWS Training and Certification. AWS expert hosts Jon Dion and Kirsten Dupart, as well as special guests, will demonstrate how to build apps with AI Services from AWS.

    Learn More »

Featured Content

Machine Learning Best Practices for Financial Services


Whether to better serve customers or improve internal processes, machine learning plays a fundamental role for financial institutions. This whitepaper outlines security and model governance considerations to help you implement machine learning applications.

Building Secure Machine Learning Environments with Amazon SageMaker


In this blog post, we introduce a series of hands-on workshops and associated code artifacts to help you build secure machine learning environments on top of Amazon SageMaker, a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy ML models quickly.
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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 to 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.
Read Now »


re:Invent 2020 - Financial Services Machine Learning Session Replays

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.

Recommended Training Resources:

Customer experience

Computer vision helps streamline the onboarding process, while chatbots and an intelligent contact center platform and speech-to-text analytics improve customer engagement with deeper customer insights.

Credit decisioning and underwriting

Enable organizations to make more accurate credit and underwriting decisions and to offer loans to a broader segment of the population.

Fraud detection and prevention

Identify anomalies in the data while reducing the number of false-positive alerts generated by rules-based models.


Intelligent document processing

Make it easy for organizations to process, analyze, and extract key information from documents to conduct due diligence, document reviews, and financial analysis.

Predictive analytics

Enable organizations to extract insights from their data to better anticipate customer needs, predict loan defaults, and identify new investment signals.

Personalized recommendations

Make customized recommendations and develop products tailored to the specific needs and behaviors of each customer.


Featured Virtual Event

Applying AI/ML in Financial Services