Credit Strategy Reporting – Originations, Part 1

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.

While our previous articles explored account management and collections reporting, this two-part article examines the critical metrics and reporting frameworks needed to optimise the originations process.

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.

Acquisition Funnel Metrics

The originations process resembles a funnel, with potential customers dropping off at each stage. Understanding where and why this happens is fundamental to improving results.

Awareness to Application Conversion

The journey begins with marketing efforts that generate awareness and interest. For digital channels, the click-through rate on advertisements and the subsequent conversion to application starts measures initial engagement effectiveness.

For traditional channels, such as direct mail, response rates serve the same purpose.

Tracking these metrics over time reveals seasonal patterns, competitive shifts, and changing consumer behaviours. When conversion rates decline across channels, it may indicate broader market saturation or competitive pressures rather than channel-specific issues.

Application Completion Rate

Once started, applications often go unfinished. The application completion rate, which is the percentage of started applications that reach submission, directly impacts acquisition costs and volumes.

Modern digital applications typically achieve 30-60% completion rates, with significant variation based on application length, complexity, and mobile optimisation.

Abandonment analysis pinpoints where potential customers exit the application process. Are they leaving at the income verification step? When asked for detailed employment information? When presented with available terms and conditions?

These insights drive application flow improvements that can dramatically increase the throughput.

The most sophisticated lenders track completion rates by device type, time of day, and customer segment, in order to identify specific optimisation opportunities. A notable pattern often emerges where completion rates drop significantly during evening hours on mobile devices, suggesting different user contexts and attention spans.

Offer Acceptance Rate

For approved applications, the percentage that accept offered terms and proceed with account opening represents the final conversion hurdle. Offer acceptance typically ranges from 60-85% for revolving credit products, with significant variation based on pricing, credit limits, and competitive positioning.

Segment-level acceptance reporting is particularly valuable. If near-prime customers accept at much lower rates than prime customers, it might indicate pricing misalignment in that segment. Similarly, tracking acceptance by offer type (e.g. different interest and fee tiers or reward structures) provides direct feedback on product design effectiveness.

The time between approval and acceptance often predicts long-term engagement, with quick acceptances correlating with higher activation and usage. Tracking this timing dimension adds another layer of insight into customer intent and relationship quality.

Risk Selection Metrics

How effectively you select risk ultimately determines portfolio performance. These metrics help evaluate and refine risk selection strategies.

Approval Rate Dynamics

The overall approval rate, which is approved applications as a percentage of completed applications, serves as the broadest risk selection metric. While target approval rates vary widely by lender and product type (from below 20% for premium products to over 70% for subprime offerings), the trend and variation matter more than the absolute number.

Tracking approval rates by acquisition channel, marketing campaign, and customer segment reveals where quality applicants originate. Significant variation often exists; direct mail campaigns might generate 60-70% approval rates due to pre-screening, while organic digital applications might see approval rates below 30%. These disparities directly impact channel-level customer acquisition costs.

Beyond simple approved/declined outcomes, many lenders track downgrade rates, which is the percentage of applicants who receive offers with less favourable terms than the headline product. This provides a more nuanced view of risk distribution within the approved population.

Credit Quality Distribution

Approval rates alone don’t tell the complete risk story. The credit quality distribution of approved applicants, which is typically measured by credit score bands, risk tiers, or expected default rates, provides deeper insight into selection effectiveness.

Tracking this distribution by vintage (monthly or quarterly application segments) reveals shifts in risk appetite over time. When paired with subsequent performance data, it enables powerful vintage analysis that connects origination decisions to long-term outcomes.

For products with risk-based pricing, the distribution of approvals across price tiers directly impacts portfolio yield expectations. Significant deviation from targets in either direction warrants attention, as it affects both risk and revenue projections.

Adverse Action Reason Analysis

When applications are declined, the reasons why provide valuable insights into both applicant quality and policy effectiveness. Tracking the distribution of primary adverse action reasons helps identify potential policy refinements or market positioning adjustments.

For example, if ‘insufficient income’ consistently appears as the top decline reason, you might need to reassess target marketing or underwriting income requirements.

If ‘too many recent inquiries’ dominates, your target market might be experiencing broader credit stress that warrants caution.

This analysis becomes particularly powerful when segmented by channel, credit tier, or demographic factors. Patterns may emerge showing that certain decline reasons concentrate in specific customer segments, suggesting targeted policy refinements.

Operational Efficiency Metrics

Efficient originations operations maximise conversion while controlling costs. These metrics help optimise the process.

Decision Automation Rate

The percentage of applications decided through automated processes without manual review directly impacts both cost efficiency and customer experience. Most modern consumer lenders achieve 70-90% automation rates, with the variation largely driven by product complexity and risk appetite.

Automation rates typically vary significantly by decision outcome, with approvals more frequently automated than declines. Tracking this breakdown helps identify opportunities for policy refinement. When manual reviews consistently overturn system recommendations in specific scenarios, it suggests potential policy gaps.

Beyond the binary automated/manual distinction, many lenders track decision confidence distribution, which is how many decisions fall into high, medium, and low confidence bands.

Concentrating manual review resources on truly ambiguous cases rather than routine decisions improves efficiency.

Application Processing Time

The time from application submission to decision directly impacts customer experience and conversion rates. Digital applications for unsecured consumer credit typically achieve decision times under one minute for automated approvals, while manual reviews can extend this timeline to hours or days.

Tracking the complete distribution rather than just averages will reveal process vulnerabilities. A bimodal distribution, with many quick decisions and many very slow ones, suggests process gaps rather than general inefficiency.

The slowest 10% of applications often consume a disproportionate share of operational resources.

Processing time variation by application channel, time of day, or customer segment helps identify specific improvement opportunities. Consistent slowdowns during evening hours might indicate staffing misalignment, while channel-specific delays could suggest integration issues.

 

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.