Verified Transformation

Fraud Alerts: 35% Fewer False Positives.

Behavioral ML on streaming transactions cut false positives by 35%—while preserving recall, auditability, and response speed.

Request Fraud Briefing
Outcome_TelemetryAUDIT_TRAIL
35%
False Positives Reduced
Rule-Heavy
28%
Analyst Load Removed
ROI: 10 Weeks
12ms
p95 Scoring Latency
SECURE_INDEX: 1.0
01 // Strategic Context
Vertical
FinTech Fraud Ops

High-volume card + bank transfer monitoring with real-time authorization constraints.

Engagement
Managed Squad

ML Lead + 2 Data Engineers + MLOps + QA embedded with Fraud & Risk stakeholders.

Objective
Reduce Alert Fatigue

Lower false positives without sacrificing recall, compliance, or chargeback outcomes.

Technology
Streaming ML Stack

Kafka/Flink, Feature Store, Gradient Boosted Models, and audit-first decision logs.

02 // The Situation

The Bottleneck: Alert Volume vs Analyst Reality.

The client’s fraud system relied on dense rule chains that were effective at catching obvious fraud, but generated high noise during normal customer behavior shifts (paydays, travel, seasonal spikes). The result was analyst overload, slow queues, and unnecessary customer friction.


The key risk wasn’t “missing fraud.” The risk was operational: a sustained false-positive rate forces investigators to work defensively, causing slow approvals, low trust, and higher churn. Any change also required auditability and explainability across every decision.

Alert Fatigue

High noise created backlogs, inconsistent decisions, and delayed response during actual fraud spikes.

Customer Friction

False declines and manual reviews increased support volume and reduced transaction completion rate.

Governance Risk

Model updates without explainability and audit trails were blocked by compliance and risk committees.

Architecture // Step 03

The Operational Gates

01

Real-Time Feature Integrity

Built a streaming behavioral feature pipeline with contract-locked schemas to ensure stable, repeatable scoring in production.

Feature_Pipeline
ModeStream_First
SchemaContract_Locked
FreshnessSub_Second
02

Human-in-the-Loop Learning

Introduced controlled review gates and feedback labeling to continuously improve precision without destabilizing operations.

HITL_Control
QueueRisk_Banded
FeedbackActive_Learning
RolloutCanary
03

Explainability + Audit Trail

Every score shipped with decision reasons, thresholds, and evidence so fraud, risk, and compliance could approve changes confidently.

Governance_Log
ReasonsTop_Features
TraceImmutable
ReviewPolicy_Gated
Step // 04: The Architecture Shift

The Structural Evolution.

Dimension
Legacy Rules
Behavioral ML Mesh
Detection Logic

Static Thresholds

Rule chains reacted to isolated signals, causing frequent false positives during normal behavior shifts.

Behavioral Patterns

Sequence + velocity + peer-group features detect anomalies in context, not in isolation.

Governance

Manual Justification

Rules had no consistent “reason trail,” making audits slow and causing policy disputes across teams.

Audit-First Decisions

Every decision carries reasons, thresholds, and trace logs for compliance sign-off.

Operational Speed

Batch-Like Reviews

Investigations accumulated into spikes, slowing response time when fraud waves hit.

Stream-Native Scoring

Sub-second feature updates enable real-time risk banding and queue stabilization.

Step // 05: The Secret Sauce

Implementation Highlights.

LOW_LATENCY

Streaming Feature Store

Designed transaction + user behavior features (velocity, recency, peer deviation) updated in real-time for consistent scoring.

Impact // Precision
Noise Reduced Without Recall Loss
AUDIT_TRAIL

Reason Codes + Explainability

Each alert included top contributing signals and policy mapping for compliance review and investigator clarity.

Impact // Governance
Audit-Ready Decision Logs
AGENTIC_AI

Investigation Assist Routing

Built triage recommendations and queue routing rules so analysts see fewer, higher-quality alerts first.

Impact // Ops
28% Analyst Load Removed
Proprietary Assets // Step 06

Accelerated by Coretus Kernels™.

Identity & Access Kernel

Scoped service tokens, RBAC, and secure event access for regulated fraud pipelines.

Feature Integrity Kernel

Schema controls + drift checks to prevent silent feature breakage during releases.

Telemetry Mesh

Score latency, throughput, and alert quality dashboards for continuous operational improvement.

Cost Guardrails

Right-sized streaming compute and model serving to keep scoring predictable at scale.

Time_To_Production
35% Faster
Typical Build16 Weeks
Coretus Build10.5 Weeks
Faster delivery + higher precision removed repeated rework, saving 800+ analyst hours/year in avoidable review time.
Step // 07: Verification Phase

The Performance Delta.

FRAUD_METRIC: PRECISION

False-Positive Alerts

Behavioral ML reduced unnecessary investigations while keeping detection coverage stable under real traffic.

Rules BaselineHigh
Coretus Model-35%
↓ 35% False Positives
OPS_METRIC: THROUGHPUT

Analyst Queue Efficiency

Risk banding + better prioritization reduced backlog and improved response during fraud waves.

BeforeBacklogs
AfterStable
↑ 28% Load Removed
PERF_METRIC: LATENCY

Real-Time Scoring

Streaming features + optimized serving achieved consistent low latency with full audit logs per decision.

LegacySlower
Coretus12ms
↓ p95 12ms Scoring
Audit Result: Precision Lift Verified
Operational // Section 09

Operational Governance.

01
Model Accountability
Every deployment ships with model versions, thresholds, and reason codes for audit review.
Status: AUDIT_TRAIL
02
Privacy & Data Control
Sensitive identifiers are protected through scoped access and secured pipelines. No uncontrolled data exposure.
Status: SOC2_READY
03
Drift & Quality Monitoring
Automated drift signals + feature freshness checks prevent silent performance decay and support continuous improvement.
Status: K8S_OPTIMIZED
04
IP & Ownership
You retain 100% ownership of pipeline code, features, and model logic. Full handover on completion.
Status: Legal_Safe
Discuss Fraud Specs
“
Coretus didn't just tune a model—they built a governed fraud engine. We cut noise immediately, and every decision is traceable enough for audit and risk sign-off.

Head of Fraud Operations

Enterprise FinTech // Risk & Compliance