Sutherland Transforms Collections for Leading U.S. Credit Card Provider with AI, Achieving 10% Churn Reduction and $2M in Overdue Recovery

Client Overview

The client is one of the largest credit card providers in the United States, specializing in subprime and near-prime credit lending. With a customer base of over 10 million cardholders, the company offers flexible credit solutions tailored to individuals building or rebuilding their credit.

As the credit landscape evolved, the client faced challenges in optimizing collections, reducing delinquencies, and managing customer retention, requiring a strategic partner to improve collections efficiency while maintaining positive customer engagement.

The Challenge

Outdated Processes and One-Size-Fits-All Approaches Eroding Collections Performance

The client’s existing collections model faced critical inefficiencies that hampered revenue recovery, operational effectiveness, and customer retention. A lack of strategic segmentation, outdated recovery tactics, and manual processes led to suboptimal outcomes:

• Inconsistent Recovery Rates: Traditional, rule-based collections methods failed to adapt to customer-specific risk profiles, leading to unpredictable recovery outcomes and higher charge-offs. The absence of data-driven strategies limited the ability to optimize repayment success across different customer segments.
• High Churn Risk: Aggressive, one-size-fits-all collection tactics resulted in customer dissatisfaction and attrition, negatively impacting long-term portfolio value. Without a personalized approach, high-potential customers were lost due to rigid recovery processes rather than rehabilitated into long-term paying relationships.
• Ineffective Segmentation: The lack of a risk-based prioritization framework meant that all delinquent accounts were treated uniformly, regardless of payment history, risk profile, or likelihood of repayment. This inefficiency diverted resources toward low-probability recoveries while missing opportunities to engage customers more strategically.
• Operational Inefficiencies: Collections efforts relied heavily on manual interventions, leading to slower response times, inconsistent customer interactions, and an increased cost-to-collect ratio. The absence of automation and AI-driven decisioning resulted in inefficiencies that limited the speed and effectiveness of recovery efforts.

To address these challenges, the client required a sophisticated, AI-powered collections framework capable of optimizing outreach strategies, maximizing recovery rates, and retaining valuable customers through a data-driven, personalized approach.

Sutherland Solution

A Data-Driven, AI-Powered Collections Model Designed for Performance and Retention

Sutherland deployed an advanced, segmented collections strategy that leveraged data analytics, automation, and behavioral insights to optimize recovery rates while preserving customer relationships. By integrating AI-powered decisioning and intelligent automation, the solution transformed collections into a more efficient, customer-centric process.

1. AI-Powered Risk Segmentation
   Sutherland developed a sophisticated risk-based segmentation model that prioritized high-risk accounts while refining outreach strategies for varying customer profiles. Machine learning algorithms analyzed repayment likelihood, enabling a data-driven approach to personalize engagement, optimize resource allocation, and maximize recovery potential.
2. Intelligent Customer Contact Optimization
   To enhance customer engagement, Sutherland implemented AI-driven call routing that identified the optimal contact channels and timing for each delinquent account. This precision-driven approach improved response rates and reduced call fatigue. Additionally, frictionless self-service payment options were introduced, allowing customers to resolve past-due balances conveniently and efficiently.
3. Automated Collections Process for Higher Efficiency
   Sutherland automated key aspects of the collections workflow, including reminders, follow-ups, and tailored repayment plans, significantly improving process efficiency and customer responsiveness. Advanced speech analytics tools monitored agent interactions, offering real-time performance insights and identifying at-risk customers for proactive intervention.
4. Churn Prevention & Customer Retention Focus
   Recognizing the importance of maintaining long-term customer value, Sutherland introduced personalized hardship programs to support financially distressed customers, reducing unnecessary churn. Behavioral analytics differentiated high-risk defaulters from customers facing temporary setbacks, allowing for customized retention strategies that strengthen customer relationships while maintaining recovery goals.

The Outcome

Smarter Segmentation, Faster Recovery, and Sustainable Collections Performance

Sutherland’s data-driven collections transformation generated significant financial and operational improvements, enhancing recovery rates, customer retention, and compliance adherence:

• 10% Reduction in Churn – AI-powered segmentation and predictive engagement strategies minimized unnecessary customer attrition, fostering long-term account stability and preserving revenue streams.
• $2M in Overdue Recovery – Intelligent prioritization of delinquent accounts, combined with automated outreach and repayment facilitation, significantly boosted cash recovery while mitigating charge-offs.
• Expedited Collections Cycle – Advanced automation and precision-driven customer engagement strategies accelerated the recovery process, reducing delinquency roll-forwards and enhancing portfolio liquidity.
• Enhanced Customer Experience & Regulatory Compliance – Personalized repayment pathways and proactive hardship management improved borrower satisfaction while ensuring strict adherence to regulatory mandates and risk controls.
• Sustainable Financial Resilience – The AI-powered collections framework established a scalable, future-ready model that reinforced risk mitigation, optimized financial performance, and ensured long-term portfolio health.

That’s digital outcomes delivered!

With Sutherland’s AI-driven risk segmentation and automation-first approach, the client significantly improved its collections performance, reduced churn, and increased cash recovery rates – while maintaining strong customer relationships.

By leveraging predictive analytics, automated workflows, and customer-centric outreach, the client transformed its collections operations into a smarter, scalable, and financially sustainable model.