📦 Revenue Leakage Analysis & Risk Identification

Analyzed transactional data to identify patterns in delayed payments and high-risk customers, uncovering ₹79K+ in potential revenue leakage using SQL and Python.

Analyzed 25,000+ transaction records to identify patterns in delayed payments and high-risk customers, uncovering ₹79K+ in potential revenue leakage using SQL and Python.

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📊 Problem Statement

Businesses often face hidden revenue losses due to delayed payments, customer defaults, and lack of visibility into high-risk transactions.

This project focuses on analyzing transaction data to identify patterns that contribute to revenue leakage and financial risk.

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⚙️ Solution Approach

• Cleaned and processed transactional data using Python (Pandas)
• Used SQL queries to identify delayed payments and high-risk customer segments
• Applied rule-based logic to flag potential revenue leakage cases
• Segmented customers based on payment behavior and risk patterns

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🛠 Tech Stack

• Python (Pandas)
• SQL
• Data Analysis

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📈 Key Insights

• Identified ₹79K+ potential revenue leakage from delayed payments and high-risk transactions
• Top 20% of customers contributed to the majority of revenue leakage
• Delayed payment behavior strongly correlated with potential defaults

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🎯 Business Value

• Helps identify high-risk customers early
• Enables better credit and payment monitoring
• Supports data-driven decision-making to reduce financial losses

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📁 Project Structure

/data → raw and processed datasets
/notebooks → exploratory analysis
/scripts → data cleaning and transformation

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🚀 Future Improvements

• Automate detection pipeline using AWS
• Build dashboard for real-time monitoring