Fraud Detection & Risk Monitoring System for Fintech Client
Umair Nawaz
Real-time fraud detection dashboard analyzing 50K+ transactions, highlighting fraud trends, risk patterns.
Dashboard Walkthrough (Real-Time Fraud Monitoring)
Fraud Detection & Risk Monitoring System for Fintech Client | SQL & Power BI
This project simulates a real-world fintech fraud detection scenario based on transaction data.
Fraud analysis was manual, delayed, and inconsistent — increasing financial risk and limiting visibility into high-risk patterns.
I designed and implemented a real-time fraud detection and reporting system to solve this.
PROBLEM
The client faced multiple operational challenges:
No centralized fraud reporting system
Inability to detect fraud patterns in real time
High dependency on raw CSV data and manual analysis
Delayed fraud identification increasing financial exposure
As a result, fraud detection was reactive instead of proactive.
SOLUTION
I built an end-to-end fraud analytics system combining ETL, database optimization, and real-time dashboards:
Converted raw CSV data into a structured SQL Server database
Cleaned and validated data (duplicates, missing values, fraud flags)
Automated data pipeline for continuous updates
Connected Power BI using DirectQuery for real-time reporting
Designed interactive dashboards to monitor fraud trends and risks
Data transformation and validation in SQL Server to ensure accurate fraud detection.
Automated ETL pipeline converting raw transaction data into real-time fraud insights.
KEY INSIGHTS
Analyzed 50,000+ transactions, identifying 1,021 fraudulent cases (~2.04%)
Detected 5.12M in fraudulent transaction value, highlighting high financial risk
Identified high-risk merchant categories and regions contributing to fraud
Fraud patterns varied significantly across transaction modes and device types
These insights enabled targeted fraud monitoring and prevention strategies.
FRAUD DETECTION & PREVENTION IMPACT
The solution improved fraud detection and prevention capabilities:
Enabled real-time fraud detection (eliminating delays from manual analysis)
Improved fraud visibility by 2x through centralized reporting dashboards
Helped identify high-risk transactions early, reducing potential financial loss
Established a scalable system for continuous fraud monitoring and prevention
This shifted the client’s approach from reactive detection → proactive risk management
BUSINESS IMPACT (WITH %)
Fraud detection speed improved by ~100% (real-time vs manual delays)
Data accuracy improved through automated cleaning and validation
Decision-making improved with centralized insights across transactions
Enabled faster identification of fraud patterns and anomalies
WHO THIS IS FOR
Fintech companies
Payment processing systems
E-commerce platforms
Risk & fraud analytics teams
TOOLS
SQL Server | Power BI | Excel
Project Resources
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Posted Apr 2, 2026
Built a real-time fraud detection system analyzing 50K+ transactions, improving fraud visibility and enabling faster risk detection using SQL & Power BI.
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