FINANCIAL TRANSACTION PERFORMANCE ANALYTICS

📊 Executive Summary

This report analyzes financial transaction performance within the 10:00–11:00 time window, focusing on transaction volume, value, fraud rates, success rates, device usage, network performance, and failure patterns. The analysis reveals moderate transaction success, relatively high fraud rates, and performance variations across transaction types, devices, and network slices. These insights highlight operational risks and opportunities to improve transaction reliability, fraud control, and customer experience.

📝 Business Problem

Financial service providers must ensure high transaction success, low fraud exposure, and optimal system performance across devices and network infrastructure. The current challenge is identifying where failures, fraud, and latency are most pronounced during peak operational periods and determining how to address them to protect revenue, trust, and service quality.

🔍 Key Insights

Transaction Volume & Value

• Total Transactions: ~1.0K

• Total Transaction Value: ~771.17K
• Indicates healthy activity within the one-hour window, making performance issues during this period business-critical.

Fraud & Success Rates

• Fraud Rate: 48.1%
• Success Rate: 48.7%
• Nearly half of transactions are either fraudulent or unsuccessful, signaling significant risk exposure and operational inefficiency.

Fraud by Transaction Type

• Transfers and deposits show higher fraud rates compared to withdrawals.
• Suggests targeted fraud controls are required for specific transaction categories.

Device-Based Transactions

• Desktop transactions slightly exceed mobile transactions.
• However, failure rates differ by device, indicating possible UX or system optimization gaps.

Network Slice Performance

• Latency: Slice 2 performs best (lowest latency), while Slice 1 and Slice 3 show higher delays.
• Failures: Slice 2 records the highest failure count, despite lower latency, implying stability or routing issues rather than speed alone.

Fraud Trend (10am–11am)

• Fraud activity is higher earlier in the hour and declines toward 11am.
• Suggests time-based fraud patterns that could be leveraged for predictive monitoring.

🎯 Methodology

Aggregated transactional data for the 10am–11am window.

Descriptive analytics using KPIs (transaction count, value, success rate, fraud rate).

Comparative analysis by:

• Transaction type
• Device (mobile vs desktop)
• Network slice

Trend analysis to observe fraud behavior over time.

Visualization-driven insights using dashboards (charts, pies, and line graphs).

🛠️ Skills Applied

• Data Analysis & Interpretation
• KPI Design and Monitoring
• Fraud Analytics
• Performance & Network Analysis
• Data Visualization (BI dashboards)
• Business Insight Translation

💡 Recommendations

Strengthen Fraud Controls

• Implement enhanced monitoring for transfers and deposits.
• Apply time-based fraud detection rules, especially earlier in peak hours.

Improve Transaction Success Rate

• Investigate root causes of failed transactions by device and network slice.
• Prioritize fixes on high-volume failure points.

Optimize Network Slices

• Review Slice 2 for reliability issues despite low latency.
• Balance traffic loads across slices to reduce failures.

Device Experience Optimization

• Analyze mobile vs desktop failure drivers (UI, connectivity, authentication).
• Improve mobile resilience given its growing transaction share.

Operational Monitoring

• Introduce real-time alerts for spikes in fraud or failures during peak periods.

🏁 Conclusion

The 10am–11am transaction window shows strong activity but is undermined by high fraud and low success rates. Performance varies notably across transaction types, devices, and network slices. By strengthening targeted fraud controls, improving network reliability, and optimizing device performance, the organization can significantly enhance transaction success, reduce risk, and improve customer trust and operational efficiency.