Industry: Consumer Lending Capabilities: Collections and Recovery
Designing Collections Strategy
From Undifferentiated Outreach to Data-Driven Collections: A Five-Pillar Strategy to Protect a $1.8B Loan Portfolio
Client Profile
Our client is a point-of-sale consumer loans fintech that experienced rapid, sustained growth in loan originations. At the time of engagement, the client was originating $100M in new loans monthly, managing a $1.8B loan portfolio, and absorbing roughly $30M in annual credit losses.
The Challenge
The client had outperformed its cumulative gross loss forecast through most of 2025, but late-year trends put that outperformance at risk, with the potential to push 2026 charge-offs meaningfully above plan. A diagnostic of the collections operation showed why.
Outreach relied almost entirely on outbound calls from a single phone number, requiring between 22 and 36 calls to generate a single payment, with most early-delinquency customers rolling deeper rather than curing. Every past-due customer received the same treatment regardless of payment history, ability-to-pay, or lien value, and escalation meant filing a lien on the customer’s home once an account crossed 60 days past due. Post-charge-off recovery was similarly thin, leaning on a small internal team and a single third-party litigator charging a 35% contingency fee.
We traced these symptoms to five root causes: low contact rates, undifferentiated treatment, an underdeveloped communication plan, manual processes, and a recovery strategy not built to scale.
The Solution
We recommended a coordinated set of initiatives across five fronts:
1. Improving contact rates.
A data-driven right-time-to-call algorithm (built on prior call history and, absent that, employment profile), a battery of local phone numbers to counter number-blocking, and a dedicated line for 90+ day accounts so delinquent customers aren’t queued behind general customer service.
2. Differentiated treatment.
A segmentation framework scoring each customer on ability-to-pay (latest bureau data), lien-coverage (whether the client’s lien position would actually cover the balance owed), and willingness-to-pay (inferred from response to calls and two-way SMS) — mapping each of the eight resulting segments to a distinct treatment path: in-house collections, litigation, modification, debt sale, or limited effort.
3. Enhancing the communication plan.
A specific channel mix of calls, SMS, letters, and email calibrated to each segment’s cure likelihood, paired with ongoing randomized testing of creative, timing, and messaging to keep improving contact and cure rates over time.
4. Reducing resource intensiveness.
Replacing manual click-to-call and disposition entry with an auto-dialer, call-prioritization logic, and restructured campaigns aligned to right-time-to-call rather than delinquency bucket alone.
5. Strengthening recovery.
Engaging multiple third-party collection vendors with rotation after six months, expanding litigation for high-value loans with confirmed ability-to-pay, and initiating debt sale as early as 90 days past due for low-recovery segments.
Bridging Strategy and Infrastructure
We partnered with the client’s technology team to map existing infrastructure against what the strategy required, identifying gaps in automated SMS, a data warehouse to store and query bureau feeds, and a call-prioritization engine. We recommended a $2M budget allocation to close these gaps and sequenced the rollout — targeting Q1 2026 launches for the right-time-to-call algorithm and the new segmentation model — so the client could act before the 2026 charge-off headwinds fully materialized.