Financial Services Industry Overview

Financial services organizations operate under intense regulatory scrutiny, process millions of transactions daily, and face adversarial threats from sophisticated fraud networks. The MATIH Platform addresses these challenges by providing a unified data-to-insights pipeline with built-in governance, auditability, and compliance controls.

Industry Context

Financial institutions face a unique combination of pressures that demand both speed and rigor:

Sample Datasets

These datasets represent a mid-size bank ("Meridian Bank") with 500K retail customers and a commercial lending portfolio. All walkthroughs in this section reference these tables.

Core Transaction Data

Market and Reference Data

External and Analytical Data

Data Sources and Connectivity

┌──────────────────────────────────────────────────────────────────────┐
│                        MATIH Ingestion Layer                        │
│                    (Airbyte Connectors + File Import)               │
└──────────┬───────────┬───────────┬──────────┬──────────┬────────────┘
           │           │           │          │          │
     ┌─────▼─────┐ ┌───▼───┐ ┌────▼────┐ ┌──▼───┐ ┌───▼────────┐
     │ Core      │ │Bloom- │ │ SWIFT   │ │ CSV  │ │ Snowflake  │
     │ Banking   │ │ berg  │ │ Payment │ │ Reg  │ │ Analytics  │
     │ PostgreSQL│ │ API   │ │ Gateway │ │Files │ │ Warehouse  │
     │           │ │       │ │         │ │      │ │            │
     │ accounts  │ │market │ │payment  │ │bureau│ │historical  │
     │ txns      │ │ data  │ │messages │ │scores│ │ defaults   │
     │ credit    │ │       │ │         │ │      │ │ analytics  │
     │ apps      │ │       │ │         │ │      │ │            │
     └───────────┘ └───────┘ └─────────┘ └──────┘ └────────────┘

Business KPIs

These KPIs are tracked across all walkthroughs and appear in dashboards, model metrics, and executive reports.

Risk and Fraud

Regulatory and Operations

Compliance Requirements

All walkthroughs incorporate these regulatory frameworks:

Governance Configuration

The Governance Service enforces these policies automatically:

{
  "policies": [
    {
      "name": "pci-card-masking",
      "type": "column_masking",
      "columns": ["card_number", "cvv", "pan"],
      "mask": "HASH_SHA256",
      "applies_to": ["analyst", "data_scientist"],
      "exempt_roles": ["compliance_officer"]
    },
    {
      "name": "gdpr-pii-restriction",
      "type": "row_level_security",
      "filter": "customer_region = CURRENT_USER_REGION()",
      "tables": ["customer_kyc", "accounts"],
      "applies_to": ["ALL"]
    },
    {
      "name": "model-decision-audit",
      "type": "audit_logging",
      "events": ["model_prediction", "credit_decision", "fraud_alert"],
      "retention_days": 2555,
      "immutable": true
    }
  ]
}

Persona Walkthroughs

Each walkthrough follows a specific persona through all eight lifecycle stages at Meridian Bank:

Data Scientist Journey: Credit Risk Scoring

Persona: Amir, Senior Data Scientist (Risk Analytics team)

Amir builds a credit risk scoring model to predict probability of default for loan applications. He ingests data from core banking and credit bureaus, engineers risk features using federated SQL, trains and validates an XGBoost model with fairness constraints, and runs a champion-challenger test against the existing scorecard.

Key platform features: ML Workbench, Feature Store, Model Registry, Data Quality gates, Governance (column masking on SSN)

ML Engineer Journey: Real-Time Fraud Detection

Persona: Kenji, ML Engineer (Fraud Operations team)

Kenji builds and operates a real-time fraud detection pipeline processing 10K transactions per minute. He configures streaming ingestion from Kafka, builds velocity features, deploys an ensemble model with sub-50ms latency SLA, and monitors model performance in production with automated retraining triggers.

Key platform features: Pipeline Service, Ray Serve, Streaming Ingestion, Real-time Monitoring, Shadow Deployment

BI Lead Journey: Regulatory Reporting and Portfolio Analytics

Persona: Rachel, BI Lead (Finance and Regulatory Reporting)

Rachel builds the regulatory reporting suite and portfolio analytics dashboards. She defines semantic layer metrics that match Basel III formulas exactly, builds automated report generation pipelines with sign-off gates, and enables self-service analytics for relationship managers with governance guardrails.

Key platform features: BI Workbench, Semantic Layer, Scheduled Reports, Data Quality validation, Governance (PII masking)

Executive Leadership Journey: Strategic Risk Analytics

Persona: Elena, Chief Risk Officer

Elena uses the Agentic Workbench for strategic portfolio analysis and board-level decision support. She asks natural language questions about portfolio exposure, receives AI-generated scenario analyses, and subscribes to automated risk briefings with real-time alerts on concentration limit breaches.

Key platform features: Agentic Workbench, Text-to-SQL, Scenario Analysis, Automated Reporting, KPI Alerts

Related Resources

• Platform Architecture -- How services interconnect
• Governance Service -- Data governance and compliance features
• ML Service -- Model lifecycle management
• Query Engine -- SQL federation capabilities
• Quickstart Tutorials -- Hands-on introduction before diving into walkthroughs