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ClearLedger

AI reconciliation engine processing $2B in monthly transactions.

AI Integration
The Challenge

Manual Reconciliation at Scale

ClearLedger was processing $2B in monthly transactions across dozens of payment rails—but reconciliation still relied on spreadsheets and overnight batch jobs.

  • Settlement delays of 24–48 hours blocked cash flow
  • Exception handling required 40+ analyst hours daily
  • No real-time visibility into reconciliation status
  • Regulatory reporting was assembled manually each month
The Solution

ClearLedger Real-Time Reconciliation Engine

We delivered an AI-powered reconciliation engine that matches transactions across payment rails in sub-second time, with automated exception routing and explainable variance detection.

A compliance dashboard generates regulatory reports on demand, while anomaly detection flags suspicious patterns before they become write-offs.

$2B

Monthly Volume Processed

<1s

Settlement Accuracy

94%

Auto-Match Rate

85%

Analyst Hours Saved

99.99%

Reconciliation Uptime

0

Regulatory Findings

What We Built

What We Built

  • Multi-rail transaction ingestion with schema normalization
  • ML-powered matching engine with explainable variance scores
  • Real-time exception queue with automated routing rules
  • Compliance dashboard with on-demand regulatory exports
  • Anomaly detection for suspicious transaction patterns
  • API layer for treasury and finance system integration
The Impact

Measurable Results, Real Impact

Finance teams gained real-time visibility while settlement accuracy reached institutional grade.

MetricBeforeAfterImprovement
Settlement Time24–48h<1sReal-time
Auto-Match Rate61%94%+33%
Analyst Hours/Day40+6-85%
Exception Resolution3 days4 hours18x faster
Reporting Time5 daysInstantAutomated
  • ↗ Multi-rail transaction ingestion with schema normalization
  • ↗ ML-powered matching engine with explainable variance scores
  • ↗ Real-time exception queue with automated routing rules
  • ↗ Compliance dashboard with on-demand regulatory exports
  • ↗ Anomaly detection for suspicious transaction patterns
Technology Stack

Built on Modern, Scalable Technology

FrontendReact, TypeScript, D3.js
BackendPython, Node.js, Apache Kafka
AI/MLscikit-learn, XGBoost, feature stores
DatabasePostgreSQL, Redis, TimescaleDB
CloudAWS Lambda, S3, ECS
SecuritySOC 2, encryption, RBAC
DevOpsDocker, GitHub Actions, Terraform
MonitoringGrafana, PagerDuty, custom alerts
The Outcome

Finance Operations Transformed

ClearLedger now reconciles billions in monthly volume with sub-second accuracy—freeing analysts to focus on exceptions that actually matter.

  • ↗ ML-powered matching engine with explainable variance scores
  • ↗ Real-time exception queue with automated routing rules
  • ↗ Compliance dashboard with on-demand regulatory exports
  • ↗ Anomaly detection for suspicious transaction patterns
We went from drowning in spreadsheets to reconciling in real time. The compliance team finally sleeps at night.
Marcus Webb · ClearLedger
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