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Case Study

FraudGuard AI

Real-time transaction anomaly detection.

Client

NeoBank

Category

ai

99% Accuracy

Key Outcome

About The Project

The Brief

NeoBank needed a smarter, faster way to detect and block fraudulent credit card transactions in real-time to protect their customers and their bottom line.

The Challenge

Navigating Complexity

Legacy rule-based systems were missing sophisticated fraud rings while blocking legitimate user transactions (false positives), causing customer frustration and loss of trust. The manual review process was too slow for real-time payments.

Our Solution

Engineered for Scale

We built an ensemble ML model that analyzes thousands of data points per transaction (location, device, spending pattern) in under 100 milliseconds. We used Kafka for real-time data ingestion and Redis for low-latency feature retrieval.

Solution Visual
The Engine Room

Technologies Deployed

The precise tech stack engineered to deliver this solution.

Scikit-Learn
Scikit-Learn

Scikit-learn provides simple tools for data analysis.

Key Benefits:
• Algorithms: Classification, regression, clustering.
• Integration: Works with NumPy/Pandas.
• Efficiency: Fast and reliable.
• Open Source: Accessible to all.

Kafka
Kafka

Kafka is a distributed event streaming platform for real-time data pipelines.

Key Benefits:
• High Throughput: Handles massive data streams.
• Fault Tolerant: Distributed architecture.
• Real-Time: Event-driven processing.
• Scalable: Horizontally scalable.

FastAPI
FastAPI

FastAPI is a modern, high-performance Python web framework for building APIs with automatic validation and interactive documentation.

Key Benefits:
• High Performance: Built on Starlette and Pydantic for fast execution.
• Automatic Docs: Generates Swagger and ReDoc API documentation out of the box.
• Type Safety: Uses Python type hints for validation and clarity.
• Scalable: Ideal for microservices and AI/ML-driven backends.

Redis
Redis

Redis is an in-memory data structure store.

Key Benefits:
• Speed: Sub-millisecond latency.
• Versatility: Cache, queue, pub/sub.
• Structures: Lists, sets, hashes.
• Persistence: Optional disk saving.

The Impact

Delivering Tangible ROI

Fraud losses were cut by 90%. False positives dropped significantly, ensuring a smoother experience for genuine customers. The model continuously learns from new fraud patterns, staying ahead of attackers.

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