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Aurora Bank Analysis

(Excel & Power BI)

Key Insights

  • High-Risk Customers: 53.45% of customers are "High Risk," with an average DTI ratio of 172.91% and debt of $79,710.58.

  • Credit Scores: 46.55% of customers have "Good" credit (670-739), while only 8.3% are "Excellent" (800+).

  • Age & Risk: Gen X (44-59) has the highest "High Risk" and "Extreme Risk" levels, needing targeted strategies.

  • Transaction Types: Chip transactions dominate at 67.53%, showing a preference for secure, in-person payments.

  • Errors: "Insufficient Balance" is the most common error, especially for Gen X customers, indicating financial strain.

  • Fraud: High transaction amounts and counts flagged several clients for "Potential Fraud," requiring stricter monitoring.

  • Revenue Sources: Money transfers, grocery stores, and wholesale clubs are top contributors to revenue.

  • Gen X Profitability: Gen X leads in transaction volume, averaging $42 per transaction, driving significant revenue.

  • Credit Card Use: Customers have an average credit limit of $14,350 and 3.07 cards, indicating high credit exposure.
     

Introduction

Welcome to the Aurora Bank Data Analysis! As a data analyst at one of the most dynamic financial institutions, I was tasked with uncovering critical insights from data on customers, transactions, cards, and merchant categories.

Key Findings

  • Peak Demand Periods: Ticket volumes are highest on weekdays (85%), especially on Mondays and Fridays, with peak hours at 3 PM. Weekend activity is significantly lower at 15%.

  • Geographical Trends: Germany, Italy, and Poland generate the highest ticket volumes, indicating a need for focused support in these regions.

  • Topic Popularity: "Product Setup" and "Pricing & Licensing" are the most common topics, accounting for a large share of tickets.

  • SLA Compliance: Most tickets meet SLA for first responses (87%), but SLA violations for resolutions remain a concern, particularly for agents like Connor Danielovitch and Nicola Wane.

  • Agent Performance: Sheela Cutten leads in resolving tickets within SLA, while Connor Danielovitch and Nicola Wane have the highest SLA violations.

  • Channel Effectiveness: Chat consistently achieves the highest customer satisfaction, while phone channels show variability with extreme highs and lows.

  • Revenue-Driving Topics: High-ticket topics such as "Product Setup" and "Pricing & Licensing" dominate, reflecting their importance in customer engagement.

  • Customer Satisfaction Trends: Chat satisfaction peaked in October (4.43) and February (4.5), while phone satisfaction varied significantly, reaching a low of 1.5 in August.

  • Resolution Delays: While first responses are timely, resolution times often exceed acceptable limits, indicating systemic inefficiencies.

  • Weekend Support Gaps: Reduced activity on weekends could lead to delays in addressing critical issues.

Data Analysis Focus Areas

  • Understand Customer Profiles: Explore customer demographics, financial health, and behaviors to unlock new opportunities for engagement.

  • Analyze Spending Trends: Uncover patterns and growth opportunities in transaction data.

  • Identify Risks: Spot potential red flags in rising debt, transaction errors, and fraudulent activity.
     

Recommendation

  • Segment High-Risk Customers: Focus on customers with high debt-to-income (DTI) ratios and poor credit scores for targeted financial counseling or repayment restructuring.

  • Enhance Customer Loyalty Programs: Leverage insights from credit score levels and demographics to create tailored rewards or incentives for loyal, low-risk customers.

  • Streamline Fraud Detection: Implement automated fraud detection systems based on flagged "Potential Fraud" cases, considering transaction thresholds and client ID grouping.

  • Increase Revenue Streams: Introduce premium financial services for customers in the "Excellent" and "Very Good" credit score categories to maximize profitability.

  • Expand Profitability Analysis: Regularly evaluate the profitability of various credit products and revise interest rates or terms to reflect risk levels.

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