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AWS Financial Services United States

WaFd Bank

Customer Service Automation · Marketing Personalization

Supporting financial needs for a diverse customer base requires innovative solutions that put the customer experience at the center. WaFd realized that regional banks, though excellent at building customer relationships, were hampered by outdated systems and disconnected data infrastructure.

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15 million
4 minutes
45 percent
95 percent

Reported impact

Business outcome

Archway is now making its banking-innovation-as-a-service platform available nationwide. Banks looking to adopt Archway’s solutions are provided with an underlying platform called Keystone, which all other Archway products connect to and facilitates innovation in weeks. Product suites cover retail online banking, mobile banking, back-office functions, digital account opening, and contact center solutions.

  • Speed to Market

Solution

Supporting financial needs for a diverse customer base requires innovative solutions that put the customer experience at the center. WaFd realized that regional banks, though excellent at building customer relationships, were hampered by outdated systems and disconnected data infrastructure. The first step WaFd took was to centralize these disparate data sources into a purpose-built data lake using Amazon Simple Storage Service (Amazon S3), an object storage service offering industry-leading scalability, data availability, security, and performance. This new method of data organization made it easier and faster to get a comprehensive view of each customer, instead of having to manually combine and sort through siloed data. “The data lake was how we combined data in disparate systems at scale,” says Hubbard. “Once the data got in there, we could use all kinds of solutions, like ML, to improve marketing.

Data flow

Data is stored in Amazon S3 and processed by Amazon SageMaker.

Solution architecture

6 components · 4 layers
  1. Compute
    • Amazon SageMaker Supports model training, tuning, deployment, or managed ML inference.
    • Amazon Comprehend Extracts text insights that enrich customer or business workflows.
  2. Storage
    • Amazon S3 Stores documents, media, telemetry, or training data durably at scale.
  3. Serving
    • Amazon Lex Handles conversational interfaces and intent routing for voice or chat experiences.
    • Amazon Personalize Generates personalized recommendations and next-best actions.
  4. Governance
    • AWS WAF Protects public applications from common web threats.

Architecture clues

  • The first step WaFd took was to centralize these disparate data sources into a purpose-built data lake using Amazon Simple Storage Service (Amazon S3), an object storage service of
  • “Once the data got in there, we could use all kinds of solutions, like ML, to improve marketing.
  • “The data lake was how we combined data in disparate systems at scale,” says Hubbard.

Evidence from the source

In 2019, WaFd kicked off Initiative 2025 with the goal of achieving full digital transformation by 2025.
Supporting financial needs for a diverse customer base requires innovative solutions that put the customer experience at the center.
The first step WaFd took was to centralize these disparate data sources into a purpose-built data lake using Amazon Simple Storage Service (Amazon S3), an object storage service offering industry-leading scalability, dat
This new method of data organization made it easier and faster to get a comprehensive view of each customer, instead of having to manually combine and sort through siloed data.
WaFd formed its own innovation lab, called Pike Street Labs, to modernize banking services while maintaining operational continuity and the same community-first aspects that make banking with regional banks distinctively
WaFd realized that regional banks, though excellent at building customer relationships, were hampered by outdated systems and disconnected data infrastructure.
Transparency Evidence & methodology 85/100 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 value85/100
  • 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 impact15/25
    1 metric 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

    WaFd Bank customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/archway-software-wafd-bank-case-study/
    Source type
    Vendor-published customer story
    Published
    Oct 13, 2023
    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.

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