Stop fraud and defaults before they happen — not after the loss is already recorded
What We Build
Real examples, not abstractions
Propensity-to-pay scoring model
Scores every statement at issuance using payment history and behavior, so collections can act before a due date is even missed.
Real-time collections dashboard
Segments the collections portfolio by recovery probability and aging, with automated queues that prioritize the accounts worth chasing.
Credit acceptance model
Combines bureau, application, and internal data into one accept/reject recommendation with a calibrated risk tier and exposure limit.
Automated credit line governance engine
Reduces, freezes, or flags credit lines the moment live risk scores cross a threshold, without waiting for a manual review cycle.
What We Solve
Common challenges — and exactly how we address them
No early warning before a billing statement goes unpaid
Collections teams react after delinquency occurs, with no signal on which clients are likely to miss payment before the due date.
A propensity-to-pay model trained on payment history, behavioral patterns, and account attributes — scoring each statement at issuance so collections can act before the due date.
Unrecovered debt managed through spreadsheets and gut feel
No unified view of the collections portfolio means teams duplicate effort, miss follow-up windows, and can't prioritize the accounts most likely to recover.
A real-time collections dashboard segmenting debt by recovery probability, aging bucket, and assigned agent — with automated prioritization queues and recovery KPI tracking.
Client onboarding decisions made without a consistent credit model
Analysts apply inconsistent criteria across new applications, leading to over-acceptance of risky clients or rejection of profitable ones.
A credit acceptance model combining bureau data, application signals, and internal benchmarks — outputting an accept/reject recommendation with calibrated risk tier and exposure limit.
Credit line restrictions applied manually and always too late
Adjusting credit limits relies on periodic manual reviews, missing real-time behavioral signals that indicate a client is approaching distress.
An automated rules engine fed by live model scores that triggers credit line reductions, freezes, or flags the moment a client crosses predefined risk thresholds — no human intervention required.
How It Works
From first conversation to working system
- 1
Risk Mapping
We review your historical fraud cases and defaults to understand the patterns your current process misses.
- 2
Feature Engineering
We design the behavioral signals and transaction features that predict risk better than your existing rules.
- 3
Model Training
We train scoring models on your data and validate against held-out periods to measure real predictive accuracy.
- 4
Integration
We connect the scoring models to your transaction processing, CRM, or collections workflows.
- 5
Monitoring
We track model performance and adapt as fraud patterns evolve to prevent score decay.
Ready to explore this for your business?
Tell us about your situation. We'll show you what's possible — in plain language.
Schedule a free meeting