AWS Financial Services United States

Petal

Risk Management

To fulfill the company's mission, Petal sought to build an infrastructure that could scale and grow with its business. Upon launching its first credit card, the Petal team needed to iterate quickly on its product.

More than 40 percent of these customers approved in the past two years were first denied credit by a major bank.

Reported impact

Business outcome

Petal continues to expand its reach to consumers. After initially launching with one credit card, Petal now offers three cards, Petal 2, Petal 1, and Petal 1 Rise.

  • Customer Experience
  • Revenue Growth
  • Scalability

Solution

Credit card startup Petal is a trailblazer in broadening access to consumer credit. Consumers can use their banking history to qualify for Petal credit cards instead of relying on credit history alone to demonstrate creditworthiness. This transformative approach to credit underwriting, which Petal pioneered, has enabled hundreds of thousands of consumers who would typically be turned away by mainstream card issuers to gain access to credit and build credit history. Petal was born in 2016 after one of its founders experienced challenges acquiring credit in the United States as an international student with no US credit history. This inspired the founders to reimagine the credit decisioning process to better serve those with little or no credit history.

Data flow

"By using Amazon S3, we can maintain high availability of our application page for our millions of applicants." For data storage, Petal uses Amazon Relational Database Service (Amazon RDS) for PostgreSQL to store its core transactional business data. RDS helped Petal quickly and easily scale its transactional data processing needs during periods of rapid growth. On AWS, Petal's infrastructure easily scales vertically and horizontally as the company has grown from processing a few dozen applications in the beginning to now handling millions of applications.

Solution architecture

5 components · 2 layers
  1. Compute
    • Amazon SageMaker Builds, trains, or deploys the machine learning models behind the risk analysis and decisioning workflows.
    • Amazon EKS Runs containerized services that deliver the risk analysis and decisioning workflows.
  2. Storage
    • Amazon S3 Stores source data, documents, or generated artifacts used by the risk analysis and decisioning workflows.
    • Amazon Redshift Serves as the analytics warehouse for the risk analysis and decisioning workflows.
    • Amazon RDS Stores relational data that supports the risk analysis and decisioning workflows.

Architecture clues

  • "By turning to Amazon EKS, we take away a lot of the extra complexity of running and hosting Kubernetes natively for our small team." Finally, Petal attributes its success building on AWS to the...
  • To run its Kubernetes-based workloads on Amazon EC2 in an efficient manner, the company uses Amazon Elastic Kubernetes Service (Amazon EKS), a managed service to run Kubernetes in the cloud and...

Evidence from the source

"The uptime and scalability of our AWS stack facilitate our ability to focus on our company's mission," says John Wang, vice president of engineering at Petal.
By building on AWS scalable solutions, the engineering team was also able to avoid many of the scalability pitfalls faced by rapidly growing startups.
By leveraging AWS infrastructure, Petal's engineering team was able to focus on creating a smooth application journey and a compelling credit card experience for its customers.
Petal hosts the user interface for these first webpages using Amazon Simple Storage Service (Amazon S3), an object storage service offering industry-leading scalability, data availability, security, and performance.
Petal's data-driven infrastructure runs on AWS services, from backend infrastructure to the front-office applications landscape.
When they did run into engineering challenges, they could simply leverage the AWS platform to scale out their infrastructure or reach out to AWS Support and their AWS account team.
Transparency Evidence & methodology 61/100 reference value · 85% 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 value61/100
  • 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 specificity16/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact0/25
    0 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence85%
  • 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 specificity8/10
    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

    Petal customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/petal-case-study/
    Source type
    Vendor-published customer story
    Published
    Jan 31, 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.