🧠 DARIA — Fraud Signal Detector ( Augmented Producer )

DARIA — Detection And Risk-Intelligence Agent — begins here.
This system powers real-time fraud simulation and streaming analysis across AWS + Web3 systems.

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⚡️ Overview

DARIA is a real-time fraud detection architecture built on
Amazon Kinesis Data Streams + KaggleHub + Python.

It ingests a Kaggle credit-card dataset, serializes each transaction,
and publishes them into sharded Kinesis streams for downstream analytics,
risk scoring, and eventually blockchain-backed audit trails.

🧩 This is where DARIA learns to “see” — synthetic data, real signals.

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🎯 Why This Exists

• Showcase a streaming-first fraud detection architecture (not batch).
• Demonstrate ordered, replayable shards and horizontal throughput.
• Provide a clean, reproducible producer pipeline anyone can point at their own stream.
• Bridge AWS ML + Web3, enabling on-chain logging and smart-contract-based rule enforcement.

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🧱 Core Concepts

Layer	Purpose
Kinesis Data Streams	Real-time event ingestion (ordered shards).
KaggleHub	Pulls public Kaggle datasets directly into the pipeline.
Augmented Transactions	Synthetic + SMOTE-balanced data from Tranche I.
Smart Contracts (future)	Run fraud-rule logic and immutable logging on-chain.
DARIA	The AI Agent orchestrating detection and risk intelligence.

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🚀 Quick Start

# 1.  Create and activate virtual environment
python -m venv .venv && source .venv/bin/activate

# 2.  Install dependencies
pip install -r requirements.txt

# 3.  Configure environment
cp .env.example .env      # update region / stream / creds

# 4.  Provision the stream
bash infra/create_stream.sh fraud-transactions-stream 2 us-east-1

# 5.  Run the producer
python -m src.producer      # publishes Kaggle (or augmented) transactions

# 6.  Optional: test a consumer
python -m src.consumer_demo # quick reader

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🧬 Data Lineage

Input → Augmentation → Stream

1. creditcard.csv from Kaggle
2. Augmented via SMOTE + Faker (see 02_data_augmentation_with_faker.ipynb)
3. Serialized into JSON payloads
4. Pushed into fraud-transactions-stream shards
5. Read downstream for analytics, rule evaluation, and ML training

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🌐 Web3 Integration (Upcoming)

DARIA’s risk events will soon publish to smart contracts that:

• Verify fraud-rule outcomes on-chain
• Append immutable audit logs
• Enable decentralized compliance tracing

AWS streams meet blockchain state — transparency by design.

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🧠 Roadmap

Phase	Focus
Tranche I	Data Augmentation (SMOTE + Faker) ✅
Tranche II	Real-time Streaming Producer (AWS Kinesis) ✅
Tranche III	Fraud-Rule Engine + Smart Contract Logging 🧩
Tranche IV	Model Serving + SageMaker Integration 🚀
Tranche V	DARIA as an Autonomous Risk Agent (AWS Bedrock + Web3) 🌌

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🪞 Vision

“DARIA doesn’t guess — she knows when something feels off.” — Naz Wright, DareDevTech

The goal isn’t just to detect fraud — it’s to teach machines the intuition of trust.

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🖋️ Author

Nazere Wright (@daredevtech) Full-Stack + AWS Machine Learning Engineer Building myth-driven, cloud-native intelligence systems.

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