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Databricks Financial Services Philippines

Maya

Fraud Detection · Marketing Personalization · Risk Management

Maya needed a unified real-time lakehouse because legacy architecture limited the scale of underwriting, fraud analytics, and personalized customer experiences across its financial ecosystem.

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$2.1 billion
45%
Dispersed for loans with advanced ML-powered underwriting Up to 98%
Making banking more accessible and secure $2.1 billion

Reported impact

Business outcome

Maya reported Making banking more accessible and secure $2.1 billion, Dispersed for loans with advanced ML-powered underwriting Up to 98%, and Reduction in fraud loss with transactional sequence embedding 45%, improving real-time fintech lending, fraud, and personalization with measurable gains in revenue growth, risk reduction, customer experience, and scalability.

  • Customer Experience
  • Revenue Growth
  • Risk Reduction
  • Scalability

Solution

Maya uses Databricks Data + AI Platform, Delta Lake, Unity Catalog, and MLflow to support real-time fintech lending, fraud, and personalization. Maya needed a unified real-time lakehouse because legacy architecture limited the scale of underwriting, fraud analytics, and personalized customer experiences across its financial ecosystem. In practice, the implementation stores or organizes core datasets in Delta Lake, governs access and lineage with Unity Catalog, and runs transformation or AI logic on Databricks Data + AI Platform. Transaction, claims, risk, and event data are consolidated into Delta Lake; Databricks Data + AI Platform applies analytics or model logic. The case study reports Making banking more accessible and secure $2.1 billion, Dispersed for loans with advanced ML-powered underwriting Up to 98%, and Reduction in fraud loss with transactional sequence embedding 45%, 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.

Solution architecture

4 components · 3 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. 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.
  • Making banking more accessible and secure $2.1 billion
  • The platform is designed for near-real-time or streaming analysis rather than batch-only reporting.

Evidence from the source

$2.1 billion
Dispersed for loans with advanced ML-powered underwriting Up to 98%
Making banking more accessible and secure $2.1 billion
Reduction in fraud loss with transactional sequence embedding 45%
Up to 98%
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

    Maya customer story

    First-party
    https://www.databricks.com/customers/maya
    Source type
    Vendor-published customer story
    Published
    May 15, 2025
    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 15, 2025 · 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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