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Databricks Financial Services Brazil

AME Digital

Fraud Detection · Risk Management

AME Digital needed stronger fraud prevention on top of rapidly growing customer and transaction volumes because peak demand periods and legacy workflows made anti-fraud operations costly and slow.

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3.2x
38%
90%
Accuracy in fraud prevention models 3.2x

Reported impact

Business outcome

AME Digital reported Combatting the impact of financial fraud 90%, Accuracy in fraud prevention models 3.2x, and More speed while running the model pipeline (vs. on-premises) 38%, improving payment fraud detection and large-scale data management with measurable gains in risk reduction, cost reduction, and productivity improvement.

  • Cost Reduction
  • Productivity Improvement
  • Risk Reduction

Solution

AME Digital uses Databricks Data + AI Platform, Delta Lake, Unity Catalog, and MLflow to support payment fraud detection and large-scale data management. AME Digital needed stronger fraud prevention on top of rapidly growing customer and transaction volumes because peak demand periods and legacy workflows made anti-fraud operations costly and slow. In practice, the implementation stores or organizes core datasets in Delta Lake, governs access and lineage with Unity Catalog, runs transformation or AI logic on Databricks Data + AI Platform, and delivers outputs through Power BI. Transaction, claims, risk, and event data are consolidated into Delta Lake; Databricks Data + AI Platform applies analytics or model logic; results are surfaced through Power BI. The case study reports Combatting the impact of financial fraud 90%, Accuracy in fraud prevention models 3.2x, and More speed while running the model pipeline (vs. on-premises) 38%, showing measurable improvement after the rollout.

Data flow

Transaction, claims, risk, and event data are consolidated into Delta Lake; Databricks Data + AI Platform applies analytics or model logic; results are surfaced through Power BI.

Solution architecture

5 components · 4 layers
  1. Compute
    • Databricks Data + AI Platform Unifies engineering, analytics, and ai processing on one governed platform
    • MLflow Tracks experiments and model lifecycle management for the deployed ai workloads
  2. Storage
    • Delta Lake Stores curated lakehouse tables used for analytics and machine learning
  3. Serving
    • Power BI Serves dashboards, reporting, and business-facing analytics on top of governed data
  4. Governance
    • Unity Catalog Enforces access controls, lineage, and governance across the data and models used in this workflow

Architecture clues

  • Centralized governance and lineage are enforced through Unity Catalog.

Evidence from the source

3.2x
90%
Accuracy in fraud prevention models 3.2x
Combatting the impact of financial fraud 90%
More speed while running the model pipeline (vs. on-premises) 38%
Transparency Evidence & methodology 95/100 reference value · 89% 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
    6 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • 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
    5 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • 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. Databricks

    AME Digital customer story

    First-party
    https://www.databricks.com/customers/ame-digital
    Source type
    Vendor-published customer story
    Published
    May 3, 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

  • Published on source page May 3, 2024 · Databricks · First-party
    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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