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Snowflake Software / SaaS London, United Kingdom

Fresha

Fraud Detection · Real-time Analytics

scale partner analytics and payment-fraud detection without letting ingestion cost, manual analysis, or data fragmentation slow the business down.

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80% reduction in fraudulent transactions
95% cost reduction in ingesting data from PostgreSQL

Reported impact

Business outcome

Fresha reduced fraudulent transactions by 80%, cut PostgreSQL ingestion costs by about 95%, and gives wellness partners frequent, insight-rich dashboards through Fresha Insights.

  • Cost Reduction
  • Customer Experience
  • Risk Reduction

Solution

Fresha uses Snowflake and Snowpipe on Snowflake to scale partner analytics and payment-fraud detection without letting ingestion cost, manual analysis, or data fragmentation slow the business down. Production databases, marketing systems, finance platforms, and transaction streams are centralized in Snowflake to serve both internal teams and partner-facing dashboards. Fresha refreshes transaction data frequently, applies machine-learning fraud checks, and uses Snowpipe to make ingestion more efficient than its earlier PostgreSQL-to-Snowflake pattern. Sales, marketing, and account teams use the internal analytics, while partners receive fresher operational insights through Fresha Insights and risk teams get near-real-time fraud signals. Keeping ingestion, storage, transformation, and governed access on one platform reduces handoffs, improves concurrency, and makes it easier to reuse trusted data across teams and applications. Fresha reduced fraudulent transactions by 80%, cut...

Data flow

production, marketing, finance, and transaction data flow into Snowflake and Snowpipe; curated data and model outputs are then served to risk analysts, internal business teams, and Fresha partners.

Solution architecture

2 components · 2 layers
  1. Ingest
    • Snowpipe Automates incremental data ingestion into Snowflake from upstream systems.
  2. Storage
    • Snowflake Provides the centralized data platform that stores governed datasets and serves analytics or AI workloads.

Architecture clues

  • Combines production-database data with third-party marketing and finance platforms.
  • Feeds partner-facing analytics through Fresha Insights.
  • Refreshes transaction data regularly to support fraud models.

Evidence from the source

Using Snowflake, Fresha powers a fraud detection engine that detects suspicious transactions in near real-time
reduce fraud on the platform by 80%
we’ve cut those costs by around 95% while ingesting more data than ever before
Transparency Evidence & methodology 100/100 reference value · 74% 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 value100/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    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
    2 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence74%
  • 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 evidence15/20
    3 direct source quotes retained.
  • Date provenance0/10
    The publication date has no documented provenance.
  • 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. Snowflake

    Fresha customer story

    First-party
    https://www.snowflake.com/en/customers/all-customers/case-study/fresha/
    Source type
    Vendor-published customer story
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes

Date evidence

  • First archived Jan 16, 2025 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
    Open date source
  • Sitemap last modified Jul 11, 2026 · Snowflake · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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