Скачать или смотреть Real Time AI Fraud Detection with Spring, Kafka, and MongoDB

1. Purpose
Detect suspicious financial transactions in real time by comparing new activity to a user’s past behavior using AI embeddings, MongoDB Atlas Vector Search, and anomaly detection.

2. Key Technologies

MongoDB Atlas: Stores data, runs vector search, and uses Change Streams for real-time monitoring.

Apache Kafka: Streams synthetic transactions for ingestion.

Spring AI + OpenAI API: Creates embeddings for semantic similarity checks.

3. Fraud Detection Process

Generate AI embedding for each transaction.

Use vector search to compare with historical data.

Flag as fraud if no similar past transaction exists, similar ones were fraudulent, or spending deviates from norms.

4. AI Embeddings
Numerical vectors representing transactions, enabling semantic similarity and efficient vector search.

5. MongoDB Change Streams
Push real-time updates on new transactions without polling, improving speed and reducing overhead.

6. Limitations & Solutions

Limitation: No batching in vector search → slows high-throughput detection.

Fixes: Atlas Triggers, caching, local similarity checks, pre-search scoring, or hybrid strategies.

7. Synthetic Data

Profiles: Simulated users with spending patterns, trusted merchants, and currencies.

Transactions: 10% chance of anomalies; injected via Kafka for testing.

8. Kafka Setup
Configured producer-consumer model: producer sends synthetic transactions to “transactions” topic; consumer ingests into MongoDB for detection.
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