AI / ML / Backend / SecurityPLANNED

LedgerGuard

Real-time financial fraud detection platform

Planned flagship system for real-time fraud detection, risk scoring and event-driven financial intelligence.

Architecture / Planning

Overview

LedgerGuard is a proposed real-time fraud detection platform focused on low-latency transaction scoring, explainable risk signals and feedback-driven model improvement.

The problem

Fraud systems must make decisions quickly while balancing detection quality, false positives, explainability and changing transaction behavior.

Engineering challenges

Designing a streaming path that can score transactions with predictable latency.
Combining rules, statistical signals and ML risk scores without creating an opaque decision layer.
Handling model drift and feedback from confirmed fraud outcomes.

Proposed event-driven fraud architecture

The intended architecture separates ingestion, feature computation, risk scoring and decisioning so each layer can evolve independently.

Transaction events → streaming ingestion
Events → feature enrichment → feature store
Features → rules + ML scoring
Scores → risk decision + explanation
Outcomes → feedback and model evaluation

Engineering decisions

Keep decisioning explainable

Context

Financial risk decisions need actionable reasons, not only a probability score.

Decision

Return structured risk factors alongside the score.

Rationale

This makes decisions easier to investigate, audit and improve.

Evidence

What the project can prove.

StatusArchitecture / Planning

Implementation has not yet started.

Lessons learned

Fraud detection is a systems problem as much as a modeling problem.
Latency, explainability and feedback loops must be designed together.

What comes next

Implement streaming ingestion and transaction schemas.
Build baseline rules and ML risk scoring.
Add model evaluation, drift monitoring and feedback workflows.