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Transformation Consulting
Executive Case Study
Case Study 1

Top 10 U.S. Financial Institution Collections Transformation

Improving collections performance through customer segmentation, analytics, automation, and targeted engagement strategies.

+37%

Collections Improvement

Increase in collections performance

$3.7M

Additional Revenue

Revenue uplift generated

$2.7M

Net Benefit

Approximate financial benefit realized

Banking

Industry

Collections transformation context

Revenue Leakage

Delinquent accounts were increasing while recovery rates remained stagnant.

Targeting Opportunity

Agent effort was not aligned to probability of payment or customer segment priority.

Data-Driven Transformation

Analytics, segmentation, automation, and workflow changes improved collections performance.

A major US bank was experiencing declining collections performance despite maintaining staffing levels and collection activity volumes.

Traditional collections strategies treated customers similarly regardless of their probability to pay, resulting in wasted effort, lower recovery rates, and rising operational costs.

A data-driven collections transformation was implemented combining analytics, automation, and targeted engagement strategies.

Result: 37% improvement in collections and approximately $2.7M net financial benefit.

The bank faced three key challenges:

Revenue Leakage Delinquent accounts were increasing while recovery rates remained stagnant.
Inefficient Resource Utilization Collection agents spent equal effort on both high-probability and low-probability recovery cases.
Regulatory Constraints The bank needed to remain compliant with strict collections regulations while improving performance.

Analysis revealed:

Finding Impact
One-size-fits-all collections strategy Low effectiveness
Poor customer prioritization Wasted agent effort
Limited predictive analytics Reduced recovery rates
High manual follow-up workload Increased cost
Phase 1: Customer Segmentation

Developed customer risk and payment propensity models.

Customers categorized into:

  • High probability to pay
  • Medium probability to pay
  • Low probability to pay
Phase 2: Engagement Optimization

Different treatment strategies developed for each segment.

Examples:

  • Self-service reminders
  • Automated outreach
  • Assisted collections
  • Specialist intervention
Phase 3: Automation

Implemented:

  • Voice bots
  • Email automation
  • RPA-based updates
  • Workflow automation

Financial Impact

  • 37% increase in collections
  • $3.7M additional revenue

Program Economics

  • Implementation cost: ~$1M
  • Net benefit: ~$2.7M

Operational Benefits

  • Reduced manual effort
  • Better agent productivity
  • More scalable collections operation
Collections TransformationRedesigning collections strategy around measurable recovery outcomes.
Customer Segmentation StrategyPrioritizing customers based on payment probability and treatment strategy.
Analytics & Predictive ModelingUsing risk and propensity models to improve decision quality.
Automation Opportunity AssessmentIdentifying where bots, RPA and workflow automation can reduce manual effort.
Business Case DevelopmentLinking program investment to financial benefits and net value.
Benefit Realization ManagementTracking financial, operational and productivity outcomes.
Operating Model TransformationImproving scalability through segmentation, automation and workflow redesign.

Collections performance improves dramatically when organizations optimize who to contact before optimizing how they contact them.

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