A financial services dataset containing 1,056 transactions was provided with suspected fraud activity. However, the data was inconsistent and not reliable enough for decision-making.
I validated and cleaned the dataset, identified key fraud risk patterns, quantified financial exposure, and built executive dashboards to support smarter fraud prevention strategies.
- Fraud Rate: 10.61% (112 of 1,056 transactions)
- Total Fraud Exposure: $38,990
- Fraud transactions were 9.7x higher in value than legitimate transactions
- 10% of users generated over 80% of fraud incidents
This project demonstrates business-focused fraud analysis — transforming raw data into actionable risk intelligence.
The organization lacked visibility into:
- Which payment methods carried the highest fraud risk
- Whether fraud was random or concentrated
- Which product categories were being targeted
- How fraud patterns changed over time
- How to prioritize monitoring efforts
Without structured analysis, fraud prevention would remain reactive instead of strategic.
Before analysis, I ensured the dataset was reliable by:
- Removing duplicate records
- Handling missing values
- Standardizing fraud labels
- Validating numeric integrity (no negative transaction values)
- Enforcing consistent data types
Output: A clean, analysis-ready dataset.
Using structured SQL analysis, I identified:
-
High-risk payment methods
- Bank transfers had the highest fraud rate (12%)
-
Targeted product categories
- Electronics showed the highest fraud concentration
-
User-level fraud concentration
- A small group of users drove the majority of fraud
-
Time-based fraud patterns
- Fraud activity peaked during specific hours
This shifted the discussion from “how many fraud cases?” to
“where should we focus prevention efforts?”
Built interactive dashboards designed for business stakeholders:
- Fraud rate & total exposure KPIs
- Payment method risk comparison
- Category vulnerability breakdown
- User-level risk concentration
- Time-based fraud analysis
- 1,056 total transactions
- 112 fraudulent transactions
- $38,990 total fraud exposure
- Clear breakdown of fraud vs legitimate activity
- Category-level fraud distribution
- Fraud rate comparison by payment method
- Bank transfers ranked highest risk
- Volume vs fraud exposure visualization
- Interactive filtering for deeper analysis
- Fraud patterns by hour and day
- Category-level fraud heatmap
- Electronics identified as most targeted category
- Time-based trend visualization
- Fraud is concentrated, not random
- High-value transactions carry disproportionate risk
- Bank transfers require enhanced monitoring
- Electronics is the most vulnerable category
- A small user segment drives most fraud exposure
- Time-based monitoring can improve prevention efficiency
fraud-detection-eda/ ├── python_scripts/ ├── sql_queries/ ├── powerbi_reports/ └── README.md
- Python
- SQL
- PowerBI
- Git
- Strong data validation discipline
- Business-first analytical thinking
- Risk prioritization ability
- Clear communication for non-technical stakeholders
- End-to-end ownership from raw data to strategic recommendation