Payment Processing Operations Optimization

Identifying $3.8M in fraud-related revenue risk across payment channels

Built for fintech operations analysis - demonstrating data-driven process optimization and risk management.

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Business Problem

Payment fraud creates direct revenue loss and damages customer trust. For fintech companies processing millions in daily volume, even small fraud rates compound into high operational costs. This analysis identifies high-risk payment categories and quantifies potential savings through targeted fraud prevention.

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Dataset

• Source: BankSim - Synthetic Financial Dataset (Kaggle)
• Scale: 594,643 transactions
• Volume: $22.5M processed
• Categories: 15 payment types (leisure, travel, food, transportation, etc.)

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Key Findings

Operational Risk by Payment Category

Payment Category	Fraud Rate	Risk Level
Leisure	95.0%	CRITICAL
Travel	79.4%	CRITICAL
Sports & Toys	49.5%	HIGH
Hotel Services	31.4%	HIGH
Other Services	25.0%	MEDIUM
Food, Contents, Transportation	0.0%	LOW

Financial Impact

• Total Volume Processed: $22,531,104
• Fraud Incidents: 7,200 (1.21% of transactions)
• Revenue at Risk: $3,822,671 (17% of total volume)
• Annual Savings Potential: $1,146,801 (with 30% fraud reduction)

Critical Insights

1. Top 3 categories account for 85%+ fraud → Immediate intervention opportunity
2. Leisure/Travel transactions 20x riskier than average → Requires enhanced monitoring
3. $3.8M fraud volume from just 1.2% of transactions → High-value fraud pattern
4. 5 categories have 0% fraud → Proof that effective controls work

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Operational Recommendations

Immediate (Week 1)

• Suspend auto-approval for Leisure & Travel categories >$500
• Implement manual review for these high-risk channels
• Expected impact: 40-50% fraud reduction in these categories

Short-term (30 Days)

• Build real-time alerting for transactions in the top 3 risk categories
• A/B test enhanced verification (2FA, biometrics) on Travel payments
• Partner review: Assess vendor fraud prevention capabilities

Long-term (90 Days)

• Deploy ML fraud model trained on category + amount + user behavior
• Optimize approval rules by category (not one-size-fits-all)
• Target outcome: 30% overall fraud reduction = $1.1M annual savings

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Technical Approach

Stack: Python, Pandas, Matplotlib, Seaborn, Kaggle API

Analysis Pipeline:

1. Automated data ingestion via Kaggle API
2. Statistical analysis across 594K transactions
3. Risk stratification by payment category
4. Revenue impact quantification
5. Automated visualization generation

Key Metrics:

• Fraud rate by category
• Transaction volume analysis
• Revenue at risk calculation
• Savings opportunity modeling

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Visualizations

Operations Dashboard

Volume Analysis

🔄 Reproducibility

git clone https://github.com/erikroa/finsys-payment.git
cd finsys-payment
python -m venv venv
venv\Scripts\activate  # Windows
pip install -r requirements.txt
python analysis.py