Credit Strategy Reporting – Originations, Part 2

Introduction

The journey of a consumer credit relationship begins long before the first statement is issued, or payment is made. Originations, which is the process of acquiring, evaluating, and on-boarding new credit customers, sets the foundation for everything that follows.

This two-part article examines the critical metrics and reporting frameworks needed to optimise the originations process. Read Part 1 here…

Effective originations reporting doesn’t just track application volumes and approval rates. It provides a comprehensive view of acquisition effectiveness, risk selection quality, operational efficiency, and forward-looking performance indicators.

Let’s explore the essential reporting elements that help consumer lenders build healthy portfolios from day one.

Forward-Looking Performance Indicators

Early signals often predict long-term performance. These metrics help forecast portfolio outcomes.

First Payment Delinquency

The percentage of new accounts that miss their very first payment provides an early warning of potential underwriting issues. First payment delinquency typically runs 1.5-3 times higher than the steady-state delinquency rate for mature accounts, with significant variation by risk tier.

This metric proves particularly valuable when tracked by origination vintage, acquisition channel, and underwriter. Sudden increases may indicate fraud attacks, economic stress in specific geographic areas, or procedural issues with payment set-up during onboarding.

The correlation between first payment default delinquency and eventual charge-off is typically strong enough to enable early loss forecasting and potential underwriting adjustments before significant portfolio damage occurs.

Early Activation and Usage

For revolving products such as credit cards and lines of credit, early engagement metrics strongly predict long-term relationship value. The 30-day activation rate, which is the percentage of new accounts that make at least one transaction within a month of opening, typically ranges from 60-85% for prime portfolios.

The initial transaction size, frequency, and category distribution provide additional insight into the customer intent and relationship trajectory. Accounts with multiple small transactions in everyday spending categories often indicate primary card behaviour and higher long-term value than accounts with a single large transaction followed by inactivity.

These early engagement patterns vary systematically by acquisition channel and product offer type. Accounts acquired through balance transfer promotions, for example, typically show different usage patterns than those responding to rewards-focused marketing.

Credit Line Utilisation Patterns

For revolving credit products, initial utilisation behaviour offers predictive power for both risk and revenue forecasting. The percentage of available credit used within the first billing cycle, along with the trajectory over the first 3-6 months, suggests the customer’s intent and financial position.

Immediate high utilisation (>70% of available credit) with a slow pay down often indicates financial stress. Conversely, moderate initial utilisation (30-50% of available credit) followed by consistent revolving behaviour typically signals a profitable revolving relationship. Low initial utilisation (<10% of available credit) with occasional transactions suggests a secondary card relationship with limited revenue potential.

These patterns show strong correlation with the acquisition channel and offer structure. Accounts acquired through 0% introductory APR offers, for instance, typically display higher initial utilisation but may convert to lower post-promotional usage than accounts acquired through rewards programmes.

Policy Effectiveness Metrics

Originations policies drive portfolio composition. These metrics evaluate policy impact and effectiveness.

Policy Override Analysis

The frequency and performance of policy exceptions provide direct feedback on policy calibration. The override rate, which is the percentage of applications approved despite failing one or more standard policy rules, typically ranges from 2-10% of all applications, depending on the lender’s risk appetite and process maturity.

Override performance tracking compares the delinquency, charge-off, and profitability metrics of exception accounts against the broader portfolio. While some level of underperformance is expected, significant deviation suggests policy misalignment that warrants adjustment.

Tracking overrides by policy rule, credit tier, and decision authority reveals patterns that inform policy refinement. When certain rules are frequently overridden with favourable outcomes, they may be unnecessarily restrictive. Conversely, high-risk performance from specific override types suggests policy tightening might be appropriate.

Champion/Challenger Testing Results

Systematic testing of policy and model variations through champion/challenger frameworks drives continuous improvement. The percentage of applications decisioned through challenger strategies, along with the performance differential between champion and challenger populations, measures innovation effectiveness.

Effective testing frameworks track not just approval rate differences but also subsequent performance metrics such as activation, early delinquency, and revenue generation. The most sophisticated operations maintain multiple concurrent tests, typically allocating 10-20% of application volume to various challenger strategies.

Test results should be evaluated against clearly defined success metrics that balance multiple objectives. A challenger strategy that increases approval rates by 5% but raises first-year defaults by 10% may be undesirable despite expanding the customer base.

Decline Opportunity Analysis

Not all declined applications represent equivalent opportunity costs. Decline opportunity analysis segments the declined population by estimated profitability and the reason for decline, in order to identify potential policy refinements.

The near-miss population, which is applicants who narrowly miss approval thresholds on otherwise strong applications, often represents the most actionable opportunity. Tracking the size of this population and modelling its expected performance helps quantify the impact of potential policy adjustments.

This analysis becomes particularly valuable during economic transitions, when historical approval patterns may no longer align with current market conditions.

The ability to quickly recalibrate policies and scorecard cut-offs based on opportunity analysis provides competitive advantage in dynamic markets.

Summary –  Connecting Originations to Lifetime Value

The most powerful originations reporting connects acquisition decisions to lifetime customer outcomes. By linking originations metrics to subsequent account management and collections data, you can build a comprehensive view of which customers, channels, and strategies deliver sustainable value.

This end-to-end perspective transforms originations from a volume-driven acquisition function to a value-creation engine that optimises for long-term portfolio performance. The metrics outlined here provide the foundation for this approach, enabling both tactical improvements and strategic insights.

In today’s competitive and rapidly evolving consumer credit landscape, sophisticated originations reporting isn’t just a risk management tool, it’s a strategic imperative that drives sustainable growth.

By mastering these metrics and their interconnections, you create the information advantage that separates market leaders from followers in the consumer credit arena.

About the Author

Jarrod McElhinney is the Chief Experience Officer at ADEPT Decisions and has been with ADEPT Decisions since 2017, playing a key role in designing and managing the platform, and ensuring that all subscribers realise direct business benefits from our solutions.

About ADEPT Decisions

We disrupt the status quo in the lending industry by providing clients with customer decisioning, credit risk consulting and training, predictive modelling and advanced analytics to level the playing field, promote financial inclusion and support a new generation of financial products.