Google Cloud

Google Cloud Components Used

1 Cloud Run (Inference & Control Plane)

Purpose

• Hosts stateless microservices for:

  • Fraud scoring API

  • Alerting logic

  • Integration endpoints

Why Cloud Run

• Auto-scales with transaction volume

• No infrastructure management

• Low latency for real-time scoring

How it works

• Kafka consumers (or connectors) forward enriched events to a Cloud Run endpoint

• Each request contains a fully engineered feature vector

• Cloud Run calls Vertex AI for scoring

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2 Vertex AI (Fraud Detection Engine)

Purpose

• Hosts the fraud detection model

• Performs real-time inference

• Supports future retraining

Model Type (MVP-appropriate)

• Gradient Boosted Trees (XGBoost / AutoML)

• Hybrid rules + ML scoring

Inputs A structured feature vector, e.g.:

{
  "tx_amount": 1250,
  "tx_count_60s": 14,
  "avg_amount_1h": 82,
  "unique_receivers_5m": 9,
  "wallet_age_days": 0.3,
  "burst_score": 0.91
}

Outputs

{
  "risk_score": 0.87,
  "risk_level": "HIGH",
  "model_version": "v1.2"
}

Vertex AI guarantees:

• Consistent inference latency

• Model versioning

• Auditability (important for fraud systems)

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3 BigQuery (Historical Analysis & Training)

Purpose

• Stores historical transactions and outcomes

• Enables offline model training and evaluation

• Supports fraud analytics dashboards

How it works

• Kafka Connect sinks data from Confluent topics into BigQuery

• Used to:

  • Label known fraud patterns

  • Evaluate false positives

  • Retrain models

Why BigQuery

• Handles massive transaction volumes

• SQL-based analysis for transparency

• Direct integration with Vertex AI pipelines

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4 Gemini (Optional but High-Impact)

Purpose

• Explains why a transaction was flagged

• Improves analyst trust and UX

Example Output

“This wallet exhibited an unusually high transaction velocity combined with first-time interactions with multiple receivers, which is consistent with laundering behavior.”

Judges strongly value explainability in AI fraud systems.