Fordel StudiosFinancial Fraud Detector
Real-time transaction fraud scoring with explainable decisions.
The problem
being solved
A digital banking platform processing 50,000+ daily transactions uses rule-based fraud detection: hard thresholds, velocity checks, geographic restrictions. These catch obvious fraud but miss account takeover, synthetic identity, and coordinated rings operating below thresholds.
False positives plague the system: 200+ daily flags with only 15-20% actual fraud. Losses grow as attackers learn the rules. Feedzai processes over 8 trillion pounds annually — ML behavioral scoring dramatically outperforms rules. Featurespace detects coercion in real time. UK banks report 65% fraud detection accuracy improvement with these approaches.
Mid-market fintechs need these capabilities without dedicated data science teams.
How this
agent works
The agent sits in the authorization path scoring every transaction in under 100ms. Gradient-boosted models trained on your history establish per-account behavioral baselines: typical amounts, merchants, geography, time patterns, device fingerprints.
Context-aware scoring: is this amount unusual for this customer but normal for this merchant? Is this location new but consistent with travel? Different device but same biometric auth? This reduces false positives compared to threshold rules.
Every score includes feature-level explanations for regulatory compliance. Network analysis detects coordinated fraud: shared fingerprints, fund flow patterns, synthetic identity clusters.
A senior engineer integrates directly with your payment pipeline and transaction database, with no off-the-shelf model wrappers. XGBoost models are trained on 6+ months of your labeled transaction history; per-account behavioral baselines are cached in Redis for sub-100ms inference via FastAPI. Neo4j maps account relationships into traversable graphs, surfacing fraud rings and synthetic identities that row-based models miss. Kafka streams live transaction events into the scoring pipeline, and the system retrains automatically on confirmed fraud outcomes — setup takes 4–5 weeks from kickoff to production.
- 01
Behavioral Scoring
XGBoost scores each transaction against a per-account baseline stored in Redis, evaluating amount, merchant category, geography, time-of-day, and device fingerprint together. Inference completes in under 100ms, making it viable for pre-authorization checks without adding latency to the payment flow.
- 02
Network Analysis
Neo4j graph traversal maps relationships between accounts, devices, and merchants to detect fraud rings, synthetic identity clusters, and structured money flows that look clean in isolation. Edges are updated in near-real-time as new transactions arrive via Kafka.
- 03
Explainable Decisions
Every score ships with SHAP-based feature attributions — which signals drove the decision, and by how much. This satisfies model explainability requirements under regulations like the Equal Credit Opportunity Act and EU AI Act Article 13 without a separate audit layer.
- 04
Adaptive Learning
Confirmed fraud outcomes feed back into the training pipeline automatically via a Kafka consumer, triggering periodic XGBoost retrains without manual rule updates. Fraud pattern drift — new merchant categories, device spoofing techniques — gets corrected within days, not sprints.
Build this agent
for your workflow.
We custom-build each agent to fit your data, your rules, and your existing systems.
Free 30-min scoping call