Fraud Detection Graph AI

Fraud Detection Graph AI (Neo4j + Python)

We built a graph-based fraud detection platform that links users, devices, cards, IPs, and merchants into an identity graph and uses graph embeddings to spot collusion rings and anomalous behavior. Scores are served in real time at checkout and payout, with explainable factors for risk teams.

The engine combines rules, graph features, and anomaly models to reduce fraud loss while keeping false positives under control. Analysts can visualize networks, drill into relationships, and understand why a transaction or account was flagged.

Identity graph (Neo4j) Graph embeddings & features Anomaly & risk scoring Explainable decisions

Impact at a glance

↓ Fraud loss

Reduced chargebacks & write-offs

↓ False positives

Fewer good users blocked

ms

P99 scoring latency

Graph-first

Ring & mule detection

By scoring risk at both account creation and transaction time, the system prevents downstream abuse while still allowing genuine customers to move through the funnel smoothly.

Problem

Traditional fraud detection struggled with:

Rule-based systems that fraudsters quickly learned to evade.
Limited view of connections across users, devices, cards, and merchants.
High false-positive rates whenever thresholds were tightened.

Solution

We introduced a graph- and ML-driven fraud platform:

Built an identity graph of accounts, devices, emails, cards, IPs, and merchants.
Used graph embeddings and topological features as inputs to risk models.
Combined anomaly detection with business rules and AML-style watchlists.
Surfaced explainable risk factors and graph visualizations for analysts.

Outcome

The Fraud Detection Graph AI delivered:

Better detection of collusion rings, synthetic identities, and mule networks.
Lower chargeback and fraud loss without over-blocking genuine users.
Faster investigations with graph views and risk explanations in one place.

Architecture overview

The platform uses Neo4j as the identity graph backbone, with Python-based jobs to compute graph features, train models, and expose REST scoring endpoints that production services can call at checkout and payouts.

Event ingestion – Signups, logins, device fingerprints, payments, and chargebacks are streamed into the graph and data warehouse.
Graph construction – Nodes (users, cards, devices, IPs, merchants) and edges (shared attributes, transactions, referrals) are maintained in Neo4j.
Feature & embedding generation – Python jobs compute graph metrics (degree, triangles, communities) and graph embeddings per node.
Risk modeling – Anomaly models and supervised learners combine graph features with transactional and device signals to produce fraud scores.
Real-time scoring – A REST API serves scores and explanations in low latency; high-risk events can be auto-blocked or queued for manual review.

Key features in production

Identity graph & rings

Links entities into a single view, making it easy to spot tightly connected clusters, shared devices, and suspicious referral chains.

Dynamic risk scoring

Scores are updated as new events happen (new device, new card, chargeback), not just at account creation, capturing evolving risk.

Explainable output

Frontline tools show top contributing risk factors (e.g., “shares device with 4 chargeback accounts”) to speed up decisions and appeals.

Investigation tools

Analysts can pull full graph neighborhoods for an account, tag entities, and feed feedback back into rules and model retraining.

The architecture is flexible enough to support new risk signals, jurisdictions, and AML scenarios while keeping the same core graph and scoring patterns.

Graph & ML capabilities

Neo4j-based graph modeling of users, devices, cards, IPs, and merchants.
Graph embeddings and structural features for fraud and AML patterns.
Anomaly detection and supervised models for risk scoring.
Calibration of thresholds to balance fraud catch vs. false positives.

Engineering & infra

Python services for feature computation, model training, and scoring APIs.
Neo4j as the operational graph store with tuned queries and indexes.
Streaming pipelines to ingest events from payment processors and apps.
Monitoring dashboards for model performance, latency, and drift.

Typical use cases

Payment fraud detection at checkout and payouts.
Account opening and KYC risk scoring (synthetic identities, mules).
Marketplace abuse detection (collusion, fake buyers/sellers).
AML-style monitoring for suspicious networks and transaction flows.