Credit Strategy Reporting – Collections

Introduction

When accounts transition from a normal status to delinquent collections, the reporting focus shifts dramatically. Our last article covered the metrics that matter for healthy revolving credit accounts. This companion piece explores the critical reporting requirements once accounts enter the collections phase.

Effective collections reporting doesn’t just track recovery efforts, it provides actionable insights that can improve outcomes, optimise resource allocation, and even help prevent future delinquencies.

Early-Stage Delinquency Metrics

The journey into collections begins with early-stage delinquency, typically defined as accounts that are 1-29 days past due (also known as 1 cycle delinquent). This critical window represents your best opportunity for efficient recovery and customer retention.

Roll-Rate Analysis

Roll-rates measure the percentage of accounts that ‘roll’ from one delinquency stage to the next. For example, what percentage of current accounts become 1-29 days past due? What percentage of those accounts then progress to 30-59 days past due?

These transition matrices serve as both performance indicators and predictive tools. By tracking roll rates over time and across different segments, you can identify trends before they become problematic.

A sudden increase in the rate at which current accounts roll to early delinquency might indicate emerging economic stress, underwriting issues, or problems with payment processing systems.

Most sophisticated operations track roll rates in both directions, known as forward rolls (deterioration) and backward rolls (improvement). The cure rate, which measures accounts returning to current (up to date) status, is particularly important as it validates the effectiveness of your early intervention strategies.

First Payment Default

A special case worth monitoring separately is first payment defaults (FPD). These are  accounts that miss their very first payment. These accounts behave differently than those that establish a pattern of on-time payments before falling delinquent.

High FPD rates typically indicate issues with origination practices, fraud, or economic headwinds affecting new borrowers.

First payment defaults often correlate strongly with eventual charge-offs, making them an important early warning indicator. Tracking FPD rates by origination channel, underwriter, or credit score band can reveal specific vulnerabilities in your acquisition strategy.

Contact Success Rates

Your ability to reach delinquent customers directly impacts collection success. Contact rates measure the percentage of attempts that result in actual customer communication. These should be tracked across channels (phone, email, text, etc.) and time periods, in order to optimise outreach strategies.

Right-party contact rates – reaching the actual accountholder rather than a family member or roommate – provide an even more refined measure.

The most effective collections operations typically achieve right-party contact rates of 25-35% on early-stage delinquent accounts, though this varies significantly by portfolio type and demographic factors.

Mid-Stage Collections Metrics

As accounts progress deeper into delinquency (typically 30-89 days past due, or 2-3 cycles delinquent), both the collections approach and relevant metrics evolve.

Promise-to-Pay Performance

Once contact is established, collections agents typically secure promises to pay (PTPs). Tracking the percentage of contacts that result in PTPs measures agent effectiveness, while the PTP kept rate reveals how many of these commitments actually materialise into payments.

The gap between promised and actual payments provides insight into both customer intent and agent skill. Low PTP rates might indicate weak negotiation techniques, while high PTP rates paired with low fulfilment suggests agents may be pressuring customers into unrealistic commitments.

Payment Arrangement Effectiveness

For customers who cannot immediately resolve their past-due balance, payment arrangements offer a structured path back to good standing. Tracking the percentage of delinquent accounts placed on arrangements, the average terms of these arrangements, and, most importantly, the completion rate provides essential feedback on this strategy’s effectiveness.

Arrangements that routinely fail may be too aggressive in their terms. Conversely, arrangements that are too lenient might unnecessarily extend collection timelines. The optimal balance varies by customer segment and delinquency stage, which is why granular reporting is crucial.

Recovery Rate by Delinquency Bucket

The percentage of past-due balances recovered typically declines as delinquency ages. By tracking recovery rates within specific delinquency buckets (30-59 days, 60-89 days, etc.), you can set realistic targets for each stage and allocate resources accordingly.

Recovery rate variation by customer segment, credit tier, or product type can reveal opportunities for targeted strategies. For instance, if near-prime customers show significantly higher recovery rates in the 60-89 day bucket compared to subprime customers, this might justify different treatment paths based on original credit quality.

Late-Stage Collections and Charge-off Metrics

Once accounts reach serious delinquency (90+ days past due), the focus shifts toward maximising recovery before and after the inevitable charge-off.

Loss Forecasting Accuracy

The ability to accurately predict which accounts will ultimately charge-off and the expected loss amount is crucial for financial planning. By comparing predicted to actual charge-offs, you can refine forecasting models and identify blind spots.

This forecasting accuracy should be measured not just at the portfolio level but also for specific segments and vintages. Consistent under-prediction of losses in certain segments may indicate systemic underwriting issues or risk assessment gaps that need addressing.

Recovery Timing Distribution

The speed of recovery impacts both customer relationships and net present value calculations. Tracking the distribution of recovery timing, what percentage occurs within the first week of delinquency versus the second month, for example, helps optimise resource allocation across different delinquency stages.

Rapid recovery not only improves financial outcomes but also increases the likelihood of rehabilitating the customer relationship. This timing data should inform workflow decisions about when to escalate collection intensity or transition accounts between treatment strategies.

 

Post Charge-off Recovery Metrics

Even after charge-off, effective reporting remains critical for optimising recovery operations or third-party management.

Agency Performance Comparison

If you place charged-off accounts with collection agencies, comparing performance across vendors reveals significant value differences. Beyond simple recovery rate comparisons, sophisticated operations track performance after controlling for account characteristics, placement timing, and seasonal factors.

The most informative approach involves champion/challenger testing, where similar account segments are placed with different agencies simultaneously. This controlled comparison eliminates timing and portfolio mix variables that might otherwise skew results.

Legal Recovery ROI

For larger balances, legal action may be warranted. The return on investment from legal recovery efforts should be closely monitored, tracking both direct costs (filing fees, attorney costs) and indirect costs (operational overhead, opportunity cost).

Segmenting legal recovery ROI by balance size, customer location, and account age helps refine criteria for legal placement. Many operations discover that legal action is only cost-effective above specific balance thresholds or within certain jurisdictions.

Debt Sale Performance

If you sell charged-off debt, tracking the spread between actual recovery (had you kept the accounts) and sale price provides valuable feedback on this strategy. Historical recovery curves from similar segments can serve as the baseline for this comparison.

Operational Effectiveness Metrics

Beyond account-level metrics, collections reporting should include operational measures that drive efficiency and compliance.

Collections Capacity Utilisation

The percentage of available collector time spent in actual customer interaction (versus administrative tasks, waiting, or other non-value activities) directly impacts results. Utilisation rates below 65-70% typically indicate workflow inefficiencies or staffing imbalances.

Modern collections platforms can automatically track these utilisation metrics, often revealing significant variation by time of day, day of week, or collector experience level. This granular data enables precise capacity planning and workflow optimisation.

Regulatory Compliance Monitoring

With collections heavily regulated in most jurisdictions, systematic compliance monitoring is essential. Tracking call frequency, time-of-day compliance, disclosure delivery, dispute handling timeliness, and other regulatory requirements provides protection against violations.

These compliance metrics should be reviewed alongside performance data, as compliance issues often correlate with certain collection techniques or individual collector behaviours. The goal is to identify practices that maximise recovery while maintaining strict regulatory adherence.

Agent Performance Variation

The difference between average and top-performing collectors is typically substantial. By quantifying this performance gap across key metrics (contact rate, promise rate, average payment amount, etc.), you can identify best practices for broader implementation.

Agent scorecards that combine these metrics provide a balanced view of performance, avoiding the pitfalls of single-metric focus. The most effective operations maintain healthy internal competition while systematically elevating team-wide performance through targeted coaching based on these metrics.

Strategic Impact Metrics

The most valuable collections reporting connects operational metrics to broader business implications.

Customer Retention After Delinquency

Not all delinquent customers are lost forever. Tracking the percentage who return to good standing and remain active provides insight into the long-term value of rehabilitation efforts. This retention rate typically varies significantly based on delinquency depth and collections treatment strategy.

Particularly for revolving products where long-term customer relationships drive profitability, rehabilitation success may be more valuable than one-time recovery. Sophisticated operations segment this analysis by customer value tier, measuring retention differently for high value versus marginal relationships.

Credit Policy Feedback Loop

Collections outcomes provide invaluable feedback for origination and account management practices. By tracking performance by origination channel, underwriter, or credit policy exception, you can identify specific acquisition sources or decisions that correlate with elevated collections risk.

This feedback loop should be formalised, with regular reporting that connects collections outcomes to origination variables. When implemented effectively, this connection can substantially improve portfolio performance by refining front-end decision models.

Vintage Performance Analysis

Comparing collections performance across different account vintages reveals how underwriting changes, economic conditions, or collections strategy shifts impact outcomes. This vintage analysis should examine not just overall charge-off rates but also roll rates, timing patterns, and recovery percentages at each delinquency stage.

When combined with origination information, vintage analysis provides perhaps the most comprehensive picture of portfolio quality evolution. Deteriorating performance in recent vintages may indicate emerging risks, while improvement validates policy adjustments.

 

Summary: From Measurement to Improvement

The most effective collections operations use reporting not just to track performance but to continuously refine strategies. This means moving beyond simple scorecards to analytics that reveal cause-and-effect relationships.

Why are certain segments outperforming others? Which treatments work best for specific customer profiles? How do early intervention tactics impact long-term outcomes?

By answering these questions through systematic reporting and analysis, collections can transform from a cost centre focused on loss recovery to a strategic function that enhances overall portfolio returns.

The most sophisticated organisations even extend this approach to pre-delinquent accounts, using early warning indicators to prevent collections entries altogether.

In an environment where regulatory scrutiny continues to intensify and customer expectations evolve rapidly, data-driven collections management isn’t just a competitive advantage, it’s increasingly a requirement for sustainable consumer lending.

The metrics outlined here provide the foundation for this approach, enabling both tactical improvements and strategic insights that benefit the entire credit life cycle.

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.