Predictive decisioning for every transaction.
An ML-based decision layer that evaluates transaction quality before action is taken — scoring authorization success, fraud and chargeback risk, and retry outcome probability.
Authorization success probability
Fraud / chargeback probability
Retry outcome prediction
Decision layer · deterministic ML
Assessed, decided, measured.
For each transaction the engine evaluates probability of successful authorization, probability of fraud or chargeback, and the expected retry outcome — then acts accordingly.
Assessment
Decision core
Controlled actions
Measured outcomes
Three models. One decision layer.
Each module is built, validated and calibrated for its own slice of the payment lifecycle.
Retry Prediction Service
200+ parametersDry modePredictive scoring of scheduled repeat transactions across transaction and behavioral parameters, with configurable execution thresholds — fewer low-probability retries, lower issuer load.
Fraud Prediction Service
100+ parametersTransaction-level scoring of chargeback and fraud-alert likelihood, trained on client-specific historical data — a configurable threshold cuts only the highest-risk tail, optionally under a blocking budget.
Repeat Payment Optimization
Precision vs coverageFalse-positive sensitiveOne-click and tokenized repeat payments are scored for decline probability — calibrated with higher sensitivity to false positives, with thresholds tuned to balance precision and coverage.
Validated before it acts.
Every model runs in dry mode alongside production, is validated against control groups with prediction-vs-actual tracking, and only then influences live decisions — under a controlled rollout, monitored over time.
Measured on validated production traffic.
These figures describe measured outcomes on validated traffic — not a promise for every integration.
38%
Fewer unnecessary retries
0.4% false-positive rate, scheduled transactions
10%
Reduction in failed one-click payments
0.09% false-positive rate
44%
Authorization approval-rate improvement
Across validated production traffic
17.5%
Of fraudulent transactions blocked
Via the fraud prediction service
Based on validated production data. Results depend on traffic profile, integration scope and decisioning configuration.
Built for payments, not for prose.
Transaction processing involves high-frequency events and structured, multi-parameter data — a domain where classical and advanced ML outperforms general-purpose AI.
Why deterministic ML
The approach
From event to monitored decision.
Models are calibrated per client — adapted to traffic, GEO and BIN behaviour — and run at operational scale with monitoring and alerting on prediction accuracy.
Transaction events
Card payment events stream into the decision layer.
Historical outcomes
Authorizations, declines, disputes and retries feed the data foundation.
Training & validation
Models are trained and validated against real outcomes.
Decision layer
Per-transaction scoring supports allow, block, route and retry decisions.
Operations monitoring
Prediction accuracy is monitored continuously over time.
The loop closes. Monitoring feeds outcomes back into training and validation — decisioning is configured per integration and tuned over time.
Deploy predictive decisioning with controlled validation.
Talk to our team about retry decisioning, fraud scoring, repeat-payment optimization and the validation lifecycle behind them.
Three predictions
Authorization success, fraud / chargeback and retry outcome.
Controlled actions
Allow, block, route, retry and repeat-payment optimization.
Validation-first
Dry mode, control groups and prediction-vs-actual tracking.
Client-calibrated
Models adapted to your traffic, GEO and BIN behaviour.