Use Case Intelligence RSS Search use cases Search
AWS Financial Services United States

Discover Financial Services

Enterprise Knowledge Search · IT Operations Automation

In its various lines of business, including decision and credit and portfolio risk management, Discover faced the challenge of making sure that it could launch its services fast enough. “We wanted to make decisions quicker and get to insights faster so that we could respond faster to our customers,” says Rahul Gupta, AI/ML expert platform engineer at Discover.

Read the original story Explore all use cases
The high compute power available was used for parallel model training, which reduced the processing time of 30 million records from days to hours.
Using 16 CPUs, the processing took 6.5–7 hours, while a single-GPU setup took 23 minutes.
Using multiple GPUs brought the processing time down to 4 minutes but also increased costs.
reduction in engineering and platform costs 35%

Reported impact

Business outcome

The Discover team now wants to explore using Amazon S3 to add event-based activations for further automation. It currently uses a scheduler to schedule daily tasks, such as identifying and classifying customers. Key reported metrics include sample size instead of 20% for better model accuracy and results 100%; reduction in engineering and platform costs 35%; Using 16 CPUs, the processing took 6.5–7 hours, while a single-GPU setup took 23 minutes..

  • Cost Reduction
  • Customer Experience
  • Risk Reduction

Solution

Learn how Discover Financial Services, a digital bank, used AWS services to build a generative AI/ML solution for better decision-making and customer service. Financial institutions need to respond quickly when situations change, and customers look to these institutions for accurate analysis and fast responses. Digital bank Discover Financial Services (Discover) wanted to enhance its customer service by speeding up analytics, insights, and decision-making. It used Amazon Web Services (AWS) to create a data science solution that its scientists and analysts could use for machine learning (ML) and generative artificial intelligence (AI) workloads. The solution helped speed up time to market, mitigate risk, and improve the customer experience.

Data flow

Source data enters the Discover Financial Services workflow through its application services, stored or indexed in Amazon S3, Amazon EFS, processed with Amazon EC2 P4 Instances, Amazon EC2 P3 Instances.

Solution architecture

4 components · 2 layers
  1. Compute
    • Amazon EC2 P4 Instances Provides GPU infrastructure for large-scale training workloads.
    • Amazon EC2 P3 Instances Provides GPU infrastructure for model training or inference.
  2. Storage
    • Amazon S3 Stores raw, intermediate, or curated files and datasets.
    • Amazon EFS Provides shared file storage across distributed compute jobs.

Architecture clues

  • To share these artifacts and data with different engineering teams and lines of business, Discover uses Amazon Elastic File System (Amazon EFS), which provides serverless, fully elastic storage.

Evidence from the source

Discover built an analytics workbench on Amazon EC2 and a unified data science workbench that its data scientists can use to run or process AI/ML applications, train models with large sample sizes—requiring up to 6 TB of memory—and deliver
In its various lines of business, including decision and credit and portfolio risk management, Discover faced the challenge of making sure that it could launch its services fast enough. “We wanted to make decisions quicker and get to insigh
The high compute power available was used for parallel model training, which reduced the processing time of 30 million records from days to hours.
Using 16 CPUs, the processing took 6.5–7 hours, while a single-GPU setup took 23 minutes.
Using multiple GPUs brought the processing time down to 4 minutes but also increased costs.
“We have provided our scientists with a cloud-scale data warehouse with on-demand HPC scaling, and we have accelerated our analytics innovation,” says Gupta.
Transparency Evidence & methodology 95/100 reference value · 87% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value95/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    5 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    6 direct source quotes retained.
  • Date provenance8/10
    The publication date comes from publisher-controlled metadata.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. AWS

    Discover Financial Services customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/discover-financial-services-generative-ai/
    Source type
    Vendor-published customer story
    Published
    Nov 21, 2024
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

Related

Similar use cases

See all like this
ServiceNow Modified · Aug 13, 2026 Robinhood Financial Services Menlo Park, California Robinhood had more than 250 fragmented IT, HR, legal, facilities, and procurement support queues, so employees had to navigate different intake paths for routine needs and the small tech-ops team was overwhelmed by… 2,200 hours saved per month Enterprise Knowledge SearchIT Operations Automation HR Service Delivery · IT Service Management · Moveworks OpenAI Jul 7, 2026 Australian Payments Plus Financial Services Asia-Pacific & Oceania AP+ needed a secure way for employees to move faster through complex payment, identity, and reconciliation work while preserving the governance, accuracy, and accountability demanded by a regulated payments environment. 1 day to build working simulations with Codex Enterprise Knowledge SearchIT Operations Automation ChatGPT Enterprise · Codex OpenAI Dec 12, 2025 BNY Financial Services Global BNY wanted to scale generative AI across a systemically important financial institution without treating it as a side experiment, so it needed a governed way for employees to build agents, use frontier models, and… 20,000 employees actively building AI agents Enterprise Knowledge SearchIT Operations Automation ChatGPT Enterprise · Eliza · deep research