#Financial Fraud Detection and Analytics Project

Welcome to the Financial Fraud Detection and Analytics Project repository! This project showcases a robust data warehousing and analytics solution tailored for financial transaction analysis, with a focus on detecting fraudulent activities. Designed as a portfolio project, it highlights industry best practices in data engineering, analytics, and fraud detection to empower data-driven decision-making.

##Project Overview This project builds a modern data warehouse to consolidate and analyze financial transaction datasets, enabling the detection of fraudulent activities and generating actionable insights. By leveraging advanced data engineering techniques and SQL-based analytics, the project aims to uncover patterns of fraud, assess transaction behaviors, and provide stakeholders with critical financial metrics.

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###Project Requirements

Building a modern Data Warehouse (Data Engineering).

####Objectives

Develop a scalable data warehouse using SQL Server to integrate financial transaction data, enabling efficient fraud detection and analytical reporting.

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####Specifications

Data Sources: Import data from five datasets provided as CSV files:

cards_data: Credit/debit card details. mcc_codes: Merchant Category Codes for transaction categorization. train_fraud_labels: Labeled dataset indicating fraudulent transactions. transactions_data: Detailed transaction records. users_data: User profile information.

Data Quality: Perform data cleansing to address inconsistencies, missing values, and duplicates to ensure reliable analysis. Integration: Combine all datasets into a cohesive, user-friendly data model optimized for fraud detection and analytical queries. Script: Focus on processing the latest dataset; historical data archiving is not required. Documentation: Provide comprehensive documentation of the data model, including schema descriptions, to support business stakeholders and analytics teams.

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##BI: Analytics & Reporting (Data Analytics)

###Objectives

Develop SQL-based analytics to deliver insights into: Fraud Detection: Identify patterns and anomalies indicative of fraudulent transactions. Transaction Behavior: Analyze user spending patterns and merchant interactions. Financial Trends: Track key financial metrics to inform business strategies.

These insights enable stakeholders to proactively mitigate fraud risks and make informed financial decisions.

###Key Metrics • Fraud detection rates and false positives. • Transaction volume and value by merchant category. • User behavior trends across demographics. • High-risk transaction patterns.

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###Getting Started Prerequisites SQL Server (or compatible database system) Python (for data preprocessing, if applicable) SQL client (e.g., SSMS) for querying CSV file handling tools (e.g., Pandas, Excel)

Installation

Clone this repository:bash

git clone https://github.com/your-username/financial-fraud-detection.git

Import the provided CSV datasets (cards_data, mcc_codes, train_fraud_labels, transactions_data, users_data) into your SQL Server environment. Run the SQL scripts in the scripts/ directory to create the data warehouse schema and load the data. Execute the analytics queries in the analytics/ directory to generate insights.

Directory Structure

financial-fraud-detection

data/ # CSV datasets scripts/ # SQL scripts for data warehouse creation analytics/ # SQL queries for fraud detection and reporting docs/ # Data model documentation README.md # Project overview LICENSE # License file

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####Usage Data Warehouse Setup: Use the scripts in scripts/ to create tables, perform ETL processes, and integrate the five datasets. Refer to docs/data_model.md for a detailed schema description.

Fraud Detection and Analytics: Run the SQL queries in analytics/ to generate fraud detection reports and financial insights. Example queries include identifying high-risk transactions, analyzing merchant category trends, and profiling user behaviors.

Visualization: Export query results to visualization tools (e.g., Power BI, Tableau) for interactive dashboards.

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##License This project is licensed under the MIT License (LICENSE). You are free to use, modify, and share this project with proper attribution.

###About Me Hi there! I am Samwel Njogu Mwaniki, a passionate data engineering enthusiast dedicated to leveraging data to solve real-world challenges. With this project, I aim to demonstrate my expertise in building data pipelines, designing analytical models, and delivering actionable insights, particularly in the domain of financial fraud detection. My goal is to empower businesses and individuals with the tools to make informed, data-driven decisions. Feel free to reach out for collaboration or inquiries Email: [[email protected]]

Thank you for exploring this project! Contributions and feedback are always welcome.