Fintech / Risk
Credit Card Customer & Fraud Analytics
Identified fraud patterns and revenue concentration.
0.58%
Fraud Rate
$26.24M
Revenue Analyzed
94%
Active Customers
Business Problem
What was unclear
The business had large-scale transaction data but limited visibility into fraud patterns, customer value distribution, and merchant performance. Without structured analysis, risks and opportunities remained hidden.
Data & System Built
From sources to structure
Data sources
- Transactional data across customers and merchants
- Time-of-day and channel signals
- Category-level transaction metadata
System built
- 01Structured transaction data into a star schema.
- 02Analyzed customer, merchant, and category-level performance.
- 03Built time-based analysis for trend and anomaly detection.
Key Insights
What the data revealed
INSIGHT 01
Fraud is concentrated, not uniform
Certain time windows and channels showed higher risk.
INSIGHT 02
Small merchant group drives most revenue
A classic concentration pattern emerged.
INSIGHT 03
High-value customers are critical
Top customers contributed disproportionately to revenue.
INSIGHT 04
Category-level risk varies significantly
Some transaction categories had higher fraud exposure.
Impact / Outcome
What decisions it enabled
- →Identified fraud-prone areas.
- →Enabled targeted risk mitigation.
- →Improved understanding of customer value.
- →Supported better partner and merchant decisions.
Visual Layer
Where the system surfaces clarity
VIEW 01
Fraud trend over time
VIEW 02
Merchant revenue distribution
VIEW 03
Category risk comparison
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