Metrics Engine

Overview

The Metrics feature in timveroOS provides a flexible framework for defining and calculating custom business indicators across the lending portfolio. This foundational component transforms raw operational data into actionable metrics through a configuration-based approach that serves as the computational backbone for advanced features like covenant monitoring and dynamic limit determination.

Business Purpose

The Metrics module addresses the need for custom calculations beyond standard system fields. Financial institutions require the ability to define their own business metrics, risk indicators, and performance measurements that reflect their specific lending strategies and risk appetite.

Core Capabilities

Metric Definition

Metrics are configured with the following attributes:

Basic Configuration:

• Metric name and description

• Entity type (participant or collateral)

• Calculation expression

• Data source references

Expression-Based Logic:

• Mathematical calculations

• Conditional logic

• Data aggregation

• Reference to multiple sources

Data Integration

The Metrics engine leverages the platform's data integration layer:

• Access to internal system data

• Integration with external data sources

• Mapping layer for data abstraction

• Unified data access interface

Interactive Demo

Configuration Process

Creating Metrics

1. Navigate to Metrics Configuration

   • Access through Settings menu

   • Select Metrics Engine option

2. Define Metric Properties

   • Provide unique metric name

   • Select applicable entity type

   • Write calculation expression

   • Map required data sources

3. Test and Validate

   • Use test data sets

   • Verify calculations

   • Check edge cases

   • Validate results

Expression Syntax

Metrics use standard expression syntax:

• Basic arithmetic operations (+, -, *, /)

• Comparison operators (>, <, =, !=)

• Logical operators (AND, OR, NOT)

• Functions for aggregation and transformation

Data Source Mapping

Connect metrics to data through the mapping layer:

• Reference system fields

• Access external data provider results

• Use previously calculated metrics

• Combine multiple data points

Common Metric Types

Financial Metrics

Examples of financial calculations:

• Debt-to-Income Ratio: Total debt obligations divided by income

• Loan-to-Value Ratio: Loan amount relative to collateral value

• Debt Service Coverage: Cash flow available for debt payments

• Net Disposable Income: Income after expenses and obligations

Risk Indicators

Risk assessment metrics:

• Credit Score Bands: Categorization of credit scores

• Payment History Score: Historical payment performance

• Concentration Risk: Exposure limits by category

• Portfolio Risk Score: Aggregate risk measurement

Operational Metrics

Business performance indicators:

• Application Velocity: Processing time measurements

• Conversion Rates: Application to loan conversions

• Portfolio Growth: Month-over-month changes

• Delinquency Rates: Past due calculations

Integration Features

Covenant Monitoring

Metrics serve as the foundation for covenant tracking:

• Define threshold values

• Monitor compliance status

• Track changes over time

• Enable automated alerts

Workflow Integration

Use metrics within decision workflows:

• Reference in decision logic

• Set approval thresholds

• Create conditional branches

• Trigger specific actions

Reporting Integration

Metrics feed into reporting systems:

• Dashboard visualizations

• Performance reports

• Risk analytics

• Compliance monitoring

Execution Modes

On-Demand Calculation

Metrics calculate when needed:

• During application processing

• Upon data updates

• For report generation

• Through manual triggers

Scheduled Calculation

Configure periodic calculations:

• Daily updates

• Weekly aggregations

• Monthly summaries

• Custom schedules

Event-Driven Calculation

Trigger calculations based on events:

• Status changes

• Data arrivals

• Threshold breaches

• System events

Performance Considerations

Optimization Strategies

• Efficient expression design

• Appropriate calculation frequency

• Smart caching implementation

• Resource usage monitoring

Scalability

The system handles growth through:

• Distributed calculation processing

• Intelligent caching strategies

• Query optimization

• Load balancing

Best Practices

Metric Design

• Use clear, descriptive names

• Document calculation logic

• Consider data availability

• Plan for edge cases

Testing Protocol

• Validate with sample data

• Test boundary conditions

• Verify calculation accuracy

• Monitor performance impact

Maintenance

• Regular review of metric usage

• Update calculations as needed

• Archive unused metrics

• Document changes

Common Use Cases

Underwriting Metrics

Support automated underwriting decisions:

• Income verification calculations

• Risk score computations

• Eligibility determinations

• Pricing factor calculations

Portfolio Management

Monitor portfolio health:

• Concentration measurements

• Performance indicators

• Risk distributions

• Trend analysis

Compliance Metrics

Ensure regulatory compliance:

• Fair lending indicators

• Regulatory ratios

• Audit measurements

• Reporting requirements

Troubleshooting

Calculation Errors

Common issues and solutions:

• Data Not Available: Verify source connectivity

• Expression Errors: Check syntax and references

• Unexpected Results: Validate test data

• Performance Issues: Optimize expressions

Integration Issues

• Confirm data mapping configuration

• Check source system availability

• Verify permissions and access

• Review error logs

Configuration Examples

Simple Ratio Metric

Name: Debt to Income Ratio
Entity: Participant
Expression: total_monthly_debt / monthly_gross_income
Sources: Credit Report, Income Verification

Conditional Metric

Name: Risk Category
Entity: Participant
Expression: IF(credit_score > 700, "Low", IF(credit_score > 600, "Medium", "High"))
Sources: Credit Bureau Data

Aggregated Metric

Name: Total Exposure
Entity: Participant
Expression: SUM(all_loan_balances) + SUM(credit_line_limits)
Sources: Internal Systems, Credit Report

Relationship to Feature Store

While the Metrics Engine focuses on real-time calculations and business indicators, the Feature Store provides a specific framework for:

• Creating data transformations exclusively for workflow usage

• Managing feature definitions used in underwriting decisions

• Providing a unified catalog of workflow-specific features

• Enabling feature discovery and reuse within workflows

See Feature Store for workflow-specific data transformation capabilities.

Next Steps

With metrics configured, explore:

• Feature Store - Configure workflow-specific data transformations

• Covenants - Use metrics for compliance monitoring

• Workflow Management - Integrate metrics in decisions

• BI Dashboard - Visualize metric trends

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For additional metrics configuration support, consult your implementation team or system documentation.