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AWS Financial Services Global

Trustly

Real-time Analytics

As Trustly started growing rapidly, the fintech company wanted to modernize its data analytics capabilities and become more data driven.

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Trustly can also update dashboards that benefit from near real-time data every 15 minutes rather than three times per day, providing fresher data that leads to better insights.
Trustly estimates that the solution costs 50 percent less to maintain, helping the company use resources more effectively and give access to more users.
With the previous solution, one analyst dedicated 4 hours a day to maintaining dashboards, and users often needed to wait for responses to queries.

Reported impact

Business outcome

In 2024, Trustly plans to increase data governance by enhancing its data catalog, data lineage, and data quality, which will give teams more visibility into the entire data life cycle. Trustly also plans to continue investing in its solution and equipping employees with data literacy skills to support its vision of being a data-driven company. Key reported metrics include Trustly estimates that the solution costs 50 percent less to maintain, helping the company use resources more effectively and give access to more users.; With the previous solution, one analyst dedicated 4 hours a day to maintaining dashboards, and users often needed to wait for responses to queries.; Trustly can also updat

  • Compliance
  • Cost Reduction
  • Customer Experience

Solution

Learn how Trustly, a global leader in open-banking payments, scaled to support growth by modernizing data analytics using Amazon QuickSight. As Trustly started growing rapidly, the fintech company wanted to modernize its data analytics capabilities and become more data driven. When its previous analytics solution could no longer handle the company’s increasing volume of data, Trustly looked to Amazon Web Services (AWS) for an alternative. Using Amazon QuickSight , which powers data-driven organizations with unified business intelligence at scale, Trustly increased productivity and scalability, reduced costs by an estimated 50 percent, and empowered users to independently generate insights. Using QuickSight, Trustly has access to its entire dataset from a single pane of glass, giving users a comprehensive view of data. Source data enters the Trustly workflow through its application services, stored or indexed in Amazon Redshift, and delivered through Amazon QuickSight, Amazon Q.

Data flow

Source data enters the Trustly workflow through its application services, stored or indexed in Amazon Redshift, and delivered through Amazon QuickSight, Amazon Q.

Solution architecture

4 components · 2 layers
  1. Storage
    • Amazon Redshift Stores analytical data and powers warehouse-style querying for reporting or ML features.
  2. Serving
    • Amazon QuickSight Delivers dashboards and business intelligence views to analysts and decision makers.
    • Amazon Q Provides conversational AI assistance for employees or end users.
    • Amazon Q in QuickSight Adds natural-language analysis and question answering on top of QuickSight dashboards.

Architecture clues

  • Ricardo Oliveira Director of Data Analytics, Trustly As Trustly started growing rapidly, the fintech company wanted to modernize its data analytics capabilities and become more data driven.
  • “Because Amazon QuickSight is serverless, we can onboard more teams and users very quickly and simply,” says Padilha.
  • “The solution increased our visibility, monitoring, availability, and resilience.” Since completing the project, Trustly has rapidly scaled the solution and increased the number of users from 80 to 300.

Evidence from the source

As Trustly started growing rapidly, the fintech company wanted to modernize its data analytics capabilities and become more data driven.
Trustly also strengthened data governance and can deliver data to specific users rather than giving everyone access.
Trustly can also update dashboards that benefit from near real-time data every 15 minutes rather than three times per day, providing fresher data that leads to better insights.
Trustly estimates that the solution costs 50 percent less to maintain, helping the company use resources more effectively and give access to more users.
Using QuickSight, Trustly has access to its entire dataset from a single pane of glass, giving users a comprehensive view of data.
With the previous solution, one analyst dedicated 4 hours a day to maintaining dashboards, and users often needed to wait for responses to queries.
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
    3 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

    Trustly customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/trustly-using-amazon-quicksight-case-study/
    Source type
    Vendor-published customer story
    Published
    Apr 15, 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.

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