Data Sources

Understanding Data Source Architecture

Data sources in timveroOS are information repositories that support decision-making and scoring processes. These sources provide the essential data required for evaluating participants and assets throughout the lending lifecycle.

Core Concepts

Data sources can be either external or internal to the system:

• External Data Sources: Third-party providers delivering specialized information

• Internal Data Sources: System-generated data from applications and collateral

Each data source associates with participants in specific roles or with assets, enabling role-based data retrieval and processing. The portfolio of integrated data sources is determined by your risk model requirements.

Important Integration Principles

The system enforces specific integration rules to maintain clarity and control:

• Multiple endpoints from the same data provider must be connected as separate data sources

• Same endpoints used at different process stages must be connected as separate data sources

• Each data source maintains independent configuration and mapping

This approach ensures precise control over data usage, versioning, and process-specific configurations.

Available Data Source Types

External Data Sources

As documented in the system, external sources include:

• Experian: Credit bureau data

• Tink: Open banking information

• TrueLayer: Transaction data

• Market Check: Valuation services

• Core Banking System: Institutional data

Internal Data Sources

System-provided sources include:

• Application Data Source: Participant attributes stored in the system

• Collateral Data Source: Asset attributes and characteristics

These internal sources enable workflows to access data already collected during the application process.

Configuration Architecture

Integration Framework

Data sources integrate at the framework level, requiring:

• API credential configuration

• Endpoint specification

• Authentication setup

• Response format definition

Workflow Integration

Data sources connect to workflows through:

• Load Data Source nodes in the Workflow Tool

• Mapping configurations for data extraction

• Feature creation for decision logic

• Profile building for offer calculations

Process Stage Association

Different stages may require different data source configurations:

• Preliminary assessment with limited data

• Comprehensive evaluation with full data access

• Renewal processes with updated information

• Monitoring with periodic refreshes

Implementation Patterns

Pattern 1: Tiered Data Strategy

Configure multiple levels of data access:

• Stage 1: Consent-free sources for initial assessment

• Stage 2: Comprehensive sources after consent

• Stage 3: Specialized sources for specific products

Pattern 2: Role-Based Data Access

Different participant roles access different sources:

• Borrowers: Full credit and income verification

• Guarantors: Credit assessment focused

• Collateral Providers: Asset-specific data

Pattern 3: Redundancy and Fallback

Implement backup strategies:

• Primary source for optimal data

• Secondary source if primary unavailable

• Manual process as final fallback

Mapping and Feature Extraction

Data sources provide raw responses that require processing:

• Mappings: JavaScript/Python scripts extract specific values

• Features: Named values resulting from mapping functions

• Profiles: Accumulated features for decision-making

The relationship flows as:

Data Source → Response → Mapping → Feature → Profile → Decision

Configuration Management

Setup Process

1. Framework Configuration: Technical integration setup

2. Admin Panel Access: Business configuration interface

3. Mapping Creation: Data extraction logic

4. Workflow Integration: Decision flow connection

5. Testing: Validation and verification

Version Control

• Each data source configuration is versioned

• Changes tracked for audit purposes

• Rollback capabilities available

• Testing before production deployment

Performance Considerations

• Timeout configuration for reliability

• Caching strategies for efficiency

• Error handling for resilience

• Cost optimization through smart usage

System Requirements and Behavior

Integration Rules

The system enforces specific requirements:

• Multiple endpoints from same provider require separate data source configurations

• Same endpoints at different stages need independent configurations

• Each data source associates with specific participant roles or asset types

• Framework-level integration defines connection parameters

Data Source Applications

As documented, data sources serve specific purposes in the system:

• Supporting decision-making throughout origination

• Enabling participant and asset scoring

• Providing data for mapping and feature extraction

• Feeding information to workflows for automated decisions

Implementation Resources

Through the Admin Panel (Step 2)

Access data source configuration:

• Data Source Setup - Integration configuration

• Data Integration Overview - Complete guide

• Feature Store - Mapping configuration

• Metrics Engine - Create Metrics for tracking Coventants

Through the SDK (Step 1)

For technical integration:

• Data Source Integration - Technical setup

Framework Capabilities

The data source framework provides:

• Separation of endpoints for precise control

• Role-based data access patterns

• Version tracking for all configurations

• Integration with workflows and mappings

Related Topics

• Mappings - Data transformation logic

• Workflow Tool - Decision flow integration

• Manual Review - Exception handling

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timveroOS: Comprehensive data integration for informed lending decisions