Major System Components

1. Blockchain Ingestion Layer

Responsibility

• Collects raw blockchain transactions in real time

Characteristics

• Can be:

  • Blockchain node

  • Indexer

  • Transaction simulator (for MVP)

• Pushes events to Kafka

Output

• Immutable transaction events

---

2. Confluent Cloud (Streaming Backbone)

Responsibility

• Durable, ordered, real-time event transport

Key Topics

• raw-transactions

• enriched-transactions

• fraud-alerts

Why It Exists

• Decouples ingestion, processing, AI scoring, and alerting

• Enables replay, scaling, and fault tolerance

---

3. Stream Processing Layer (Confluent)

Responsibility

• Real-time behavioral feature engineering

Capabilities

• Sliding and tumbling windows

• Wallet-level aggregation

• Burst and anomaly indicators

Outputs

• Feature-enriched transaction events

This layer is the core Confluent value demonstration.

---

4. Fraud Scoring Service (Google Cloud Run)

Responsibility

• Acts as the real-time inference gateway

Characteristics

• Stateless

• Auto-scaling

• Low-latency HTTP interface

Flow

• Receives feature vectors

• Calls Vertex AI

• Applies policy logic

---

5. Vertex AI (Machine Learning Layer)

Responsibility

• Computes fraud risk scores

Model Characteristics

• Supervised or semi-supervised

• Outputs probabilistic risk score

• Versioned and auditable

Why Vertex AI

• Managed inference

• Model lifecycle control

• Native integration with BigQuery

---

6. Decision & Policy Layer

Responsibility

• Converts risk scores into actions

Example Policies

• Allow

• Flag for review

• Escalate / block (off-chain)

Key Point

• Decisions are configurable and explainable

• No hard coupling to on-chain execution

---

7. Analytics, Audit & Explainability

BigQuery

• Stores:

  • Transactions

  • Features

  • Risk decisions

• Used for:

  • Model retraining

  • Compliance

  • Historical analysis

Optional: Gemini

• Produces human-readable explanations

• Improves trust and analyst productivity