CASE STUDY: Gaon.ai

Gaon

How we built an event-driven ML system that predicts lease defaults up to 45 days earlier than traditional credit scoring — turning reactive collections into proactive risk management.

The Challenge

Gaon.ai manages a high-volume equipment leasing portfolio where credit risk is not static — it evolves continuously over the life of each lease. Their existing risk assessment relied on point-in-time credit bureau scores pulled at origination, which told them nothing about how a client's financial health was changing month-to-month. By the time a payment was 60+ days overdue, the damage was already done: recovery rates dropped sharply, collection costs spiked, and bad debt write-offs were eating into margins. The collections team was entirely reactive, with no early-warning system to flag deteriorating accounts before they became delinquent.

EVENT-DRIVEN REAL-TIME ML INFERENCE PIPELINEEVENT SOURCESNew LeaseInitiatedPaymentReceivedMissed DueDatePartial PaymentDetectedFEATURE ENG.Payment sequencesDelay trend (3-6mo)Partial payment %Response patternsBehavioral vectorsML ENGINEGradient BoostedEnsemble Model4+ years training dataSub-second inferenceReal-time scoring→ Live Risk IndexRISK TIERSGREENHealthy — No actionYELLOWWatch — MonitorREDCritical — AlertCRMALERTSRisk summaryKey factorsRecommendedactions↑ 45 daysearly warningFeedback loop → Model retraining & origination decisions

The Method: Behavioral Machine Learning

We built a continuous behavioral scoring model that treats credit risk as a time-series problem, not a static classification. The model ingests temporal sequences of payment events — not just whether a payment was made, but how late it was, whether it was partial, the trend direction over the last 3–6 months, and behavioral signals like changes in communication responsiveness. Using gradient-boosted ensemble methods trained on 4+ years of historical lease performance data, the model learns the behavioral fingerprints that precede defaults — patterns that are invisible to traditional credit scores but highly predictive when analyzed as sequences.

The Solution

The system is fully event-driven. Every payment event, missed due date, or lease modification triggers a real-time inference call through a lightweight ML serving layer. The model recomputes a live risk index for the affected client and compares it against calibrated threshold tiers (green, yellow, red). When a client's behavioral trajectory crosses into a risk tier, the system pushes an automated alert directly into Gaon.ai's CRM with a risk summary, recommended mitigation actions, and the key contributing factors — giving the revenue team actionable context, not just a score. The entire pipeline runs on managed cloud infrastructure with sub-second latency from event to alert.

BEHAVIORAL RISK SCORING — EARLY DEFAULT DETECTIONRISKINDEX10080604020CRITICALWATCHJanFebMarAprMayJunJulAugSepOctMarqov Alert: WatchMarqov Alert: CriticalTraditional: DefaultClient A — HealthyClient C — Watch ZoneClient B — Deteriorating45 days

End State & Outcomes

The system now flags at-risk accounts an average of 45 days before they would have been caught by traditional methods — giving the collections team a critical intervention window. Since deployment, Gaon.ai has seen a significant reduction in bad debt write-offs and a measurable improvement in recovery rates on flagged accounts, because outreach happens when clients are still responsive rather than after they've gone silent. The revenue team moved from fighting fires to managing risk proactively, and the behavioral risk index has since been integrated into origination decisions as well — creating a feedback loop where the model informs both ongoing monitoring and new lease approvals.