- Reads transaction data from Excel
- Calculates critical payment KPIs
- Identifies failure patterns
- Detects high-risk banks and payment methods
- Generates strategic business insights using Llama3 (via Ollama)
Payment failures directly impact:
- Conversion Rate
- Revenue
- Customer Experience
- Marketing ROI
- Operational Efficiency
This system helps identify:
- High-risk banks
- High-risk payment methods
- Top failure reasons
- Failure rate trends
- Revenue loss impact
- Total Orders
- Total Revenue
- Failed Orders
- Success Orders
- Payment Failure Rate
- Top Failed Bank
- Top Failed Payment Method
- Failure Reason Distribution
- Time Trend Distribution
The consolidated summary is sent to Llama3 to generate:
- Key Risk Areas
- Root Cause Insights
- Business Impact
- Actionable Recommendations
- Strategic Improvements
- Python
- Pandas
- Ollama
- Llama3
- Excel Data Processing
- openpyxl
1️⃣ Install Dependencies pip install -r requirements.txt 2️⃣ Install Ollama
Download from: https://ollama.com/
3️⃣ Pull Llama3 Model ollama pull llama3 4️⃣ Run Project failure_insights_AI.py
📈 Example Output Overall Failure Rate: 7.35%
Highest Failure Bank: XYZ Bank Top Failure Reason: Insufficient Funds
Implement intelligent payment retry logic and smart routing.
Reduce payment failure rate Improve checkout conversion Increase revenue recovery Optimize gateway routing Strengthen risk monitoring
eCommerce companies Payment gateway monitoring teams FinTech analytics teams Checkout optimization teams
Payment failure prediction model Real-time monitoring dashboard Power BI integration Automated email alerts Retry success rate analytics
Payment failures silently kill revenue. This project transforms raw payment data into AI-powered executive insights.