# Fintech User Guide

A comprehensive guide for implementing AtomIngest in fintech environments with regulatory compliance, security best practices, and industry-specific patterns.

## Table of Contents

- [Introduction](#introduction)
- [Compliance & Regulatory Requirements](#compliance--regulatory-requirements)
- [Security Best Practices](#security-best-practices)
- [Common Fintech Data Patterns](#common-fintech-data-patterns)
- [Sample Use Cases](#sample-use-cases)
- [Performance Considerations](#performance-considerations)
- [Testing & Validation](#testing--validation)

## Introduction

AtomIngest provides specialized features for fintech data ingestion including:

- **Regulatory Compliance**: Built-in audit trails, immutable records, and compliance reporting
- **Data Security**: PII encryption, field-level security, and access controls
- **Risk Management**: Real-time data validation, anomaly detection, and alerting
- **Performance**: High-throughput processing for market data and transaction volumes

## Compliance & Regulatory Requirements

### SOX Compliance

For Sarbanes-Oxley compliance, ensure:

```yaml
# Enable audit trails for all financial data
audit:
  enabled: true
  retain_days: 2555  # 7 years retention
  fields:
    - user_id
    - transaction_timestamp
    - source_system
    - data_hash

# Mark sensitive financial fields
schema:
  revenue:
    type: DecimalField
    security:
      classification: "financial_data"
      audit_required: true

  expense:
    type: DecimalField
    security:
      classification: "financial_data"
      audit_required: true
```

### GDPR/PII Protection

```yaml
# Encrypt personally identifiable information
schema:
  customer_ssn:
    type: CharField
    max_length: 11
    security:
      encryption: "AES-256"
      classification: "PII"
      retention_days: 730

  customer_email:
    type: EmailField
    security:
      classification: "PII"
      anonymize_after_days: 1095
```

### Basel III / Banking Regulations

```yaml
# Risk-weighted asset calculations
schema:
  loan_amount:
    type: DecimalField
    validation:
      min: 0
      business_rules:
        - rule: "credit_risk_validation"
          params:
            max_ltv_ratio: 0.8

  risk_weight:
    type: DecimalField
    validation:
      min: 0
      max: 1.5
      business_rules:
        - rule: "basel_risk_weight_validation"
```

## Security Best Practices

### Field-Level Encryption

```yaml
security:
  encryption:
    key_management: "aws_kms"  # or "azure_keyvault", "hashicorp_vault"
    default_algorithm: "AES-256-GCM"

schema:
  account_number:
    type: CharField
    security:
      encryption: true
      key_rotation_days: 90
```

### Access Controls

```yaml
security:
  rbac:
    enabled: true
    default_role: "data_consumer"

  field_access:
    pii_data:
      roles: ["compliance_officer", "data_protection_officer"]
    financial_data:
      roles: ["financial_analyst", "compliance_officer"]
```

### Audit Logging

```yaml
audit:
  enabled: true
  log_format: "json"
  destinations:
    - type: "database"
      table: "audit_log"
    - type: "siem"
      endpoint: "https://splunk.company.com/api"

  events:
    - "data_ingestion"
    - "schema_changes"
    - "access_violations"
    - "encryption_operations"
```

## Common Fintech Data Patterns

### 1. Transaction Processing

```yaml
target_table: transactions

schema:
  transaction_id:
    type: CharField
    max_length: 50
    unique: true
    nullable: false

  amount:
    type: DecimalField
    precision: 15
    scale: 2
    nullable: false
    validation:
      min: 0.01
      business_rules:
        - rule: "daily_limit_check"
          params:
            max_daily_amount: 50000.00

  currency:
    type: CharField
    max_length: 3
    choices: ["USD", "EUR", "GBP", "JPY", "CAD"]
    default: "USD"

  transaction_type:
    type: CharField
    max_length: 20
    choices: ["DEBIT", "CREDIT", "TRANSFER", "FEE"]

  merchant_id:
    type: CharField
    max_length: 50
    nullable: true
    validation:
      - type: "merchant_validation"

  timestamp:
    type: DateTimeField
    nullable: false
    validation:
      - type: "business_hours_check"
      - type: "future_date_check"

normalization:
  steps:
    - step: "CurrencyNormalizationStep"
      config:
        base_currency: "USD"
        fx_service: "xe.com"

    - step: "TransactionCategorizationStep"
      config:
        mcc_mapping_file: "merchant_category_codes.yaml"

    - step: "FraudDetectionStep"
      config:
        velocity_checks: true
        geolocation_validation: true
        pattern_analysis: true

hooks:
  post_insert:
    - hook: "real_time_risk_scoring"
    - hook: "aml_screening"
    - hook: "notification_service"
```

### 2. Market Data Ingestion

```yaml
target_table: market_data

schema:
  symbol:
    type: CharField
    max_length: 10
    nullable: false
    validation:
      - type: "symbol_validation"
        params:
          exchanges: ["NYSE", "NASDAQ", "LSE"]

  timestamp:
    type: DateTimeField
    nullable: false
    validation:
      - type: "market_hours_validation"

  price:
    type: DecimalField
    precision: 10
    scale: 4
    nullable: false
    validation:
      min: 0
      business_rules:
        - rule: "price_deviation_check"
          params:
            max_deviation_percent: 20.0

  volume:
    type: BigIntegerField
    nullable: false
    validation:
      min: 0

normalization:
  steps:
    - step: "MarketDataDeduplicationStep"
      config:
        time_window_seconds: 1

    - step: "PriceAdjustmentStep"
      config:
        corporate_actions_file: "corporate_actions.yaml"

    - step: "TechnicalIndicatorStep"
      config:
        indicators: ["SMA", "EMA", "RSI", "MACD"]
        periods: [5, 10, 20, 50, 200]

performance:
  batch_size: 10000
  parallel_workers: 8
  high_frequency_mode: true
```

### 3. Customer KYC Data

```yaml
target_table: customer_kyc

schema:
  customer_id:
    type: CharField
    max_length: 50
    unique: true
    nullable: false

  full_name:
    type: CharField
    max_length: 255
    nullable: false
    security:
      classification: "PII"

  date_of_birth:
    type: DateField
    nullable: false
    security:
      classification: "PII"
    validation:
      - type: "age_validation"
        params:
          min_age: 18
          max_age: 120

  social_security_number:
    type: CharField
    max_length: 11
    nullable: true
    security:
      encryption: true
      classification: "PII_SENSITIVE"
    validation:
      - type: "ssn_format_validation"

  address:
    type: JSONField
    nullable: false
    security:
      classification: "PII"
    validation:
      - type: "address_validation"
        params:
          verify_with_usps: true

  risk_score:
    type: DecimalField
    precision: 5
    scale: 2
    nullable: true
    validation:
      min: 0
      max: 100

normalization:
  steps:
    - step: "NameStandardizationStep"
    - step: "AddressStandardizationStep"
    - step: "PEPScreeningStep"
    - step: "WatchlistScreeningStep"
      config:
        lists: ["OFAC", "EU_SANCTIONS", "UN_SANCTIONS"]

hooks:
  pre_insert:
    - hook: "identity_verification"
    - hook: "aml_screening"

  post_insert:
    - hook: "risk_assessment"
    - hook: "compliance_notification"
```

### 4. Regulatory Reporting

```yaml
target_table: regulatory_reports

schema:
  report_id:
    type: CharField
    max_length: 50
    unique: true
    nullable: false

  report_type:
    type: CharField
    max_length: 50
    choices: ["CFTC", "SEC", "FINRA", "OCC", "FDIC"]
    nullable: false

  reporting_period:
    type: DateField
    nullable: false

  data_snapshot:
    type: JSONField
    nullable: false
    validation:
      - type: "regulatory_schema_validation"

  submission_deadline:
    type: DateTimeField
    nullable: false

  status:
    type: CharField
    max_length: 20
    choices: ["DRAFT", "SUBMITTED", "APPROVED", "REJECTED"]
    default: "DRAFT"

normalization:
  steps:
    - step: "RegulatoryFormatStep"
      config:
        format_mapping: "regulatory_formats.yaml"

    - step: "DataValidationStep"
      config:
        schema_files:
          CFTC: "cftc_schema.json"
          SEC: "sec_schema.json"
          FINRA: "finra_schema.json"

audit:
  enabled: true
  retention_days: 3650  # 10 years for regulatory data
  tamper_proof: true

hooks:
  pre_insert:
    - hook: "regulatory_validation"

  post_insert:
    - hook: "submission_scheduler"
    - hook: "compliance_notification"
```

## Sample Use Cases

### Use Case 1: Real-Time Payment Processing

**Business Requirement**: Process credit card transactions with real-time fraud detection and regulatory compliance.

**Pipeline Configuration**:
```yaml
target_table: payment_transactions

real_time: true
max_processing_latency_ms: 100

schema:
  payment_id:
    type: CharField
    max_length: 50
    unique: true
    nullable: false

  card_number:
    type: CharField
    max_length: 19
    security:
      encryption: true
      tokenization: true
    validation:
      - type: "luhn_check"

  amount:
    type: DecimalField
    precision: 10
    scale: 2
    validation:
      min: 0.01
      max: 10000.00

  merchant_code:
    type: CharField
    max_length: 15
    validation:
      - type: "mcc_validation"

normalization:
  steps:
    - step: "CardTokenizationStep"
    - step: "FraudScoringStep"
    - step: "VelocityCheckStep"

hooks:
  real_time:
    - hook: "fraud_detection"
      timeout_ms: 50
    - hook: "auth_check"
      timeout_ms: 30
```

### Use Case 2: End-of-Day Risk Reporting

**Business Requirement**: Generate daily risk reports for regulatory submission with complex calculations.

**Pipeline Configuration**:
```yaml
target_table: daily_risk_report

schedule:
  cron: "0 1 * * *"  # Run at 1 AM daily
  timezone: "America/New_York"

schema:
  report_date:
    type: DateField
    nullable: false

  var_95:
    type: DecimalField
    precision: 15
    scale: 2

  var_99:
    type: DecimalField
    precision: 15
    scale: 2

  stress_test_result:
    type: JSONField

normalization:
  steps:
    - step: "RiskCalculationStep"
      config:
        confidence_levels: [0.95, 0.99]
        lookback_days: 252

    - step: "StressTestStep"
      config:
        scenarios: "stress_scenarios.yaml"

validation:
  critical_checks:
    - rule: "var_coherence_check"
    - rule: "regulatory_limits_check"

hooks:
  post_processing:
    - hook: "regulatory_submission"
    - hook: "risk_committee_notification"
```

## Performance Considerations

### High-Frequency Data Ingestion

```yaml
performance:
  mode: "high_frequency"
  batch_size: 50000
  parallel_workers: 16
  memory_buffer_mb: 1024

  database:
    connection_pool_size: 20
    bulk_insert: true
    disable_foreign_keys: true  # Re-enable after ingestion

  optimization:
    pre_sort_data: true
    use_temporary_tables: true
    disable_indexes_during_load: true
```

### Large File Processing

```yaml
performance:
  streaming: true
  chunk_size_mb: 100
  memory_mapped_files: true

  progress_tracking:
    enabled: true
    checkpoint_interval: 10000
    resume_on_failure: true
```

## Testing & Validation

### Unit Testing Pipeline Configurations

```python
# test_fintech_pipelines.py
import pytest
from atomingest.testing import PipelineTestCase
from atomingest.core import Pipeline

class TestTransactionPipeline(PipelineTestCase):
    pipeline_config = "transactions_pipeline.yaml"

    def test_valid_transaction(self):
        sample_data = {
            "transaction_id": "TXN_12345",
            "amount": "100.50",
            "currency": "USD",
            "transaction_type": "DEBIT"
        }

        result = self.pipeline.process_record(sample_data)
        assert result.success
        assert result.data["amount"] == 100.50

    def test_fraud_detection(self):
        suspicious_data = {
            "transaction_id": "TXN_FRAUD",
            "amount": "10000.00",  # Large amount
            "timestamp": "2026-01-29T03:00:00Z"  # Unusual time
        }

        result = self.pipeline.process_record(suspicious_data)
        assert result.fraud_score > 0.8
        assert "HIGH_RISK" in result.flags
```

### Integration Testing

```python
# test_integration.py
def test_end_to_end_transaction_flow():
    """Test complete transaction processing flow"""

    # 1. Ingest transaction data
    pipeline = Pipeline("transactions_pipeline.yaml")
    result = pipeline.process_file("sample_transactions.csv")

    # 2. Verify data in database
    transactions = Transaction.query().filter(
        Transaction.timestamp >= datetime.now().date()
    ).all()

    assert len(transactions) == 100

    # 3. Verify fraud detection triggered
    high_risk = [t for t in transactions if t.risk_score > 0.7]
    assert len(high_risk) > 0

    # 4. Verify audit trail created
    audit_records = AuditLog.query().filter(
        AuditLog.operation == "transaction_insert"
    ).all()

    assert len(audit_records) == 100
```

### Compliance Testing

```python
# test_compliance.py
def test_pii_encryption():
    """Verify PII data is properly encrypted"""

    customer_data = {
        "customer_id": "CUST_12345",
        "social_security_number": "123-45-6789",
        "full_name": "John Doe"
    }

    pipeline = Pipeline("customer_kyc_pipeline.yaml")
    result = pipeline.process_record(customer_data)

    # Verify encrypted fields are not stored in plain text
    db_record = Customer.query().filter(
        Customer.customer_id == "CUST_12345"
    ).first()

    assert db_record.social_security_number != "123-45-6789"
    assert db_record.social_security_number.startswith("enc:")

def test_audit_trail_immutability():
    """Verify audit records cannot be modified"""

    # Create audit record
    audit = AuditLog.create({
        "operation": "test_operation",
        "data_hash": "abc123"
    })

    original_hash = audit.record_hash

    # Attempt to modify
    with pytest.raises(ImmutableRecordError):
        audit.update({"operation": "modified_operation"})

    # Verify hash unchanged
    assert audit.record_hash == original_hash
```

## Conclusion

This guide provides the foundation for implementing secure, compliant, and performant data ingestion pipelines in fintech environments. The key principles are:

1. **Security First**: Always encrypt PII data and implement proper access controls
2. **Compliance by Design**: Build regulatory requirements into your pipeline configuration
3. **Performance at Scale**: Use appropriate batching, parallel processing, and optimization techniques
4. **Quality Assurance**: Implement comprehensive testing and validation
5. **Operational Excellence**: Monitor, alert, and maintain detailed audit trails

For more specific examples and advanced patterns, see the [Fintech Examples](fintech_examples/) directory.
