Sub-100ms scoring that catches fraud and keeps customers moving
ML-powered transaction scoring across cards, ACH, and wires delivering $18M annual savings at 95%+ detection.
Block too many transactions and customers leave; miss fraud and losses mount.
The Challenge
A regional bank with $25M in annual fraud losses across credit card, ACH, and wire channels was running a rules-based detection system with a 65% false positive rate. Legitimate transactions were declined at a rate that drove customer attrition and call center volume. Fraud analysts spent 80% of their time clearing false alerts rather than investigating genuine fraud. Meanwhile, sophisticated attacks—account takeover, synthetic identity, and authorized push payment fraud—exploited the rule system's inability to detect behavioral anomalies.
The Innovoco Solution
We deployed ML-powered real-time fraud detection analyzing payment patterns, merchant categories, geographic anomalies, device fingerprints, and behavioral biometrics. Sub-100ms transaction scoring integrates with the bank's authorization flow across all payment channels, with continuous model retraining as fraud tactics evolve.
Phase 1 — Model development and shadow scoring
Trained AI models on 24 months of labeled transaction data across all channels. Deployed in shadow mode alongside the existing rules engine for six weeks, measuring detection lift and false positive reduction before any production impact.
Phase 2 — Production deployment and adaptive learning
Replaced rules-based scoring with AI models in the authorization path. Implemented continuous retraining on confirmed fraud outcomes, with champion/challenger model management and automated rollback if performance degrades.
Phase 3 — Behavioral biometrics and cross-channel fusion
Added device fingerprinting and session behavior features for card-not-present and digital banking channels. Cross-channel risk signals (e.g., address change followed by wire) feed composite scores that capture multi-step attack patterns.
Key implementations
Sub-100ms inline scoring
Models deployed on low-latency inference infrastructure score every transaction within the authorization timeout window—no batch delays or post-transaction-only detection.
Multi-channel coverage
Unified scoring across credit card, debit, ACH, wire, and P2P channels with channel-specific feature engineering and shared behavioral profiles.
Adaptive retraining
Confirmed fraud outcomes trigger incremental model updates; champion/challenger framework ensures new models outperform incumbents before promotion.
Explainable decisioning
Top contributing features accompany every score, enabling fraud analysts to understand and document the basis for blocks and alerts—supporting SR 11-7 model governance.
Dynamic risk thresholds
Configurable thresholds by channel, customer segment, and transaction type allow the bank to balance fraud prevention with customer experience by product line.
Technical Innovation
A unified data layer computes 200+ real-time and historical signals per transaction in under 10 milliseconds—combining session behavior, spending velocity, and identity patterns. This ensures the AI sees the same data in production that it was trained on, preventing the drift that degrades fraud models over time.
Impact
- $18M annual fraud savings across all payment channels.
- 95%+ fraud detection rate, up from 72% with the rules-based system.
- 80% reduction in false positives, cutting customer friction and call center volume.
- Sub-100ms scoring latency with 99.99% availability SLA.
The bank stopped choosing between security and experience. Fraud losses dropped by $18M annually while legitimate transactions flowed faster—and the system adapts as attack tactics evolve.
Explore this outcome on your stack
We map scope, guardrails, and rollout to your data boundaries and teams—practical next steps, not a generic slide deck.
60 min · Free · No obligation
More case studies
Explore adjacent patterns from the same production playbook.
AI-Powered Business Intelligence
Ask questions in plain language, get governed answers with charts — no analysts, no ticket queue.
Read more
Customer support & voice
Triage, sentiment-aware routing, CRM-grounded answers at scale.
Read more
Regulated onboarding (KYC / AML)
Document intelligence, exception queues, immutable evidence for auditors.
Read more