MichaelGelshtein/fraud-detection-eda

PaySim Financial Fraud Detection — EDA & Dataset Analysis

Presentation Video

Dataset Overview

This project analyzes the PaySim Financial Fraud Detection dataset from Kaggle (source: chitwanmanchanda/fraudulent-transactions-data). The dataset contains simulated mobile money transactions with fraud labels, representing 100,000 randomly sampled transactions (random_state=42) from the original 6,362,620 rows.

Key characteristics:

• Total records: 100,000 transactions
• Target variable: isFraud (0 = legitimate, 1 = fraudulent)
• Data quality issues found & resolved: 200 missing amount values, 300 missing oldbalanceOrg values, 500 duplicate rows, 400 inconsistent type formatting
• Class imbalance: Fraud is extremely rare (~0.14% of all transactions)

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Research Question

"How do specific economic indicators, such as transaction types and sudden inconsistencies between account balances, act as financial fingerprints to accurately predict fraudulent activity, and can identifying these logical gaps in the data lead to more efficient and cost-effective detection systems?"

This project investigates whether patterns in transaction characteristics—particularly transaction type, amount magnitude, and account balance behavior—can serve as reliable fraud indicators without relying on demographic or user-level features.

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Features

Feature	Type	Description
step	int	Time step of the transaction (1-743, representing hours in simulation)
type	str	Transaction type: CASH_IN, CASH_OUT, DEBIT, PAYMENT, TRANSFER
amount	float	Transaction amount (in currency units)
oldbalanceOrg	float	Original account balance before transaction
newbalanceOrig	float	Original account balance after transaction
oldbalanceDest	float	Destination account balance before transaction
newbalanceDest	float	Destination account balance after transaction
isFraud	int	Binary fraud label (0 = legitimate, 1 = fraudulent)
isFlaggedFraud	int	Whether transaction was flagged by bank's system
amount_log	float	Log-transformed amount (log1p) for analysis

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EDA Methodology

1. Data Loading & Cleaning

Issues introduced to simulate real-world financial data quality problems:

• 200 missing values in amount (simulating incomplete transaction records)
• 300 missing values in oldbalanceOrg (simulating balance data not captured at logging time)
• 500 duplicate rows (simulating double-logging errors in the payment system)
• 400 rows with inconsistent casing in type (simulating data entry errors)

Cleaning steps applied:

• Missing amount → dropped rows: A transaction with no amount is unusable and cannot be safely imputed
• Missing oldbalanceOrg → filled with median: Median chosen over mean because the amount column is right-skewed; median is a robust estimator
• Duplicates → drop_duplicates(): Removed all 500 duplicate rows to prevent inflated counts
• type inconsistency → .str.upper().str.strip(): Standardized all 5 transaction types to uppercase to prevent transfer and TRANSFER being treated as separate categories

2. Outlier Detection & Treatment

Applied Interquartile Range (IQR) method on amount feature. 5,358 outliers detected (5.4% of data). Decision: retained all outliers because fraud transactions often occur at extreme values.

Applied log1p transformation to amount → amount_log for cleaner visualization.

3. Descriptive Statistics

Calculated summary statistics by fraud status. Key insight: fraud transactions are drastically larger than legitimate ones — average fraud amount ~1,340,000 vs. legitimate ~179,000 (ratio: 7.5x larger).

4. Correlation Analysis

Generated correlation heatmap of all numerical features to identify relationships between balance features and fraud status.

5. Transaction Type Distribution

Visualized how transactions are distributed across all 5 types.

6. Fraud by Transaction Type (Raw Count)

Showed where fraud actually occurs across transaction types before normalizing for volume.

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Key Findings

Overall Fraud Characteristics

• Fraud rate: 0.14% of all transactions (142 fraudulent out of 100,000)
• Class imbalance: Highly skewed toward legitimate transactions
• Amount severity: Fraudulent transactions are 7.5x larger on average
• Bank's detection: Only 0.56% of fraudulent transactions flagged (isFlaggedFraud=1)

Transaction Type Distribution

• Most common type: TRANSFER (35%)
• PAYMENT transactions: 31%
• CASH_OUT: 24%
• DEBIT: 8%
• CASH_IN: 2%

Fraud Distribution by Type

• TRANSFER: 0.99% fraud rate (77 frauds out of 7,806 transfers)
• CASH_OUT: 0.30% fraud rate (65 frauds out of 23,235 cash-outs)
• PAYMENT: 0% fraud rate (0 frauds)
• DEBIT: 0% fraud rate (0 frauds)
• CASH_IN: 0% fraud rate (0 frauds)

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Four Research Questions & Insights

Research Q1: "Do high-value transactions carry higher fraud risk?"

Finding: Yes, fraud transactions occupy the extreme upper tail of the amount distribution. The highest-value transactions show disproportionately high fraud rates, indicating that transaction magnitude is a critical risk indicator.

Conclusion: Amount is a strong univariate fraud signal. Fraudsters tend to target high-value transfers to maximize stolen funds per transaction.

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Research Q2: "Do fraudsters drain the sender's account to zero?"

Finding:

• Fraudsters drain the sender account to zero in ~96% of fraud cases
• Legitimate transactions show account depletion in only ~0.3% of cases
• This is a massive logical inconsistency: legitimate users rarely empty accounts, but fraudsters consistently do

Conclusion: Balance depletion is one of the strongest fraud indicators in the dataset. This "emptying the well" behavior is a logical fingerprint of fraudulent activity — attackers maximize extraction and disappear.

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Research Q3: "Are certain transaction types fraud-free?"

Finding:

• TRANSFER and CASH_OUT are the only types with any fraud (0.99% and 0.30% respectively)
• PAYMENT, DEBIT, and CASH_IN have a 0% fraud rate in this dataset
• Fraud is concentrated in transaction types that move money away from the original account

Conclusion: Transaction type is a critical categorical predictor. A simple rule-based filter (flag TRANSFER & CASH_OUT only) would catch 100% of fraud with minimal false positives.

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Research Q4: "Is fraud correlated with transaction amount ranges?"

Finding:

• Small (<1K): ~0.04% fraud rate
• Medium (1K–10K): ~0.10% fraud rate
• Large (10K–100K): ~0.27% fraud rate
• Very Large (>100K): ~1.08% fraud rate
• Clear monotonic increase: Larger amount buckets have exponentially higher fraud risk

Conclusion: Amount binning creates a simple but effective fraud scoring feature. The largest transactions contain ~8x more fraud than the smallest, making amount a strong standalone predictor.

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Key Decisions & Rationale

Why keep outliers?

Fraud is inherently an outlier behavior. Removing high-value transactions would eliminate the most suspicious cases. Decision: Retain all 5,358 outliers because they represent the exact transactions we want to catch.

Why log-transform the amount?

Raw amounts span from 0 to millions, creating extreme skewness. Log transformation makes distributions more interpretable, reduces visual dominance of extreme values, and improves visualization clarity without losing information.

Why 100K sample?

The original dataset has 6.3M rows. A 100K random sample maintains the representative fraud rate (~0.14%), reduces computational overhead, and is sufficient for EDA. Reproducibility is ensured with seed=42.

Why focus on these 4 research questions?

Each addresses a different dimension of fraud detection logic — amount risk, balance consistency, transaction-type filtering, and amount-bin stratification. Together they reveal that fraud has multiple independent signals that all point in the same direction.

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Conclusion

This EDA reveals that financial fraud leaves multiple consistent fingerprints in transaction data. Rather than relying on complex models or external features, we can identify fraud through transaction type filtering (TRANSFER & CASH_OUT only), amount thresholds (very large transactions are far more likely to be fraud), and balance logic checks (account depletion to zero is virtually diagnostic).

These insights suggest that cost-effective fraud detection is possible through simple rule-based systems with high precision, tiered monitoring for high-risk transaction types, and real-time balance anomaly detection. Future work could focus on ensemble methods combining these signals, time-series analysis, and network-level features to achieve even higher detection rates.

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Dataset source: Kaggle — PaySim Financial Fraud Detection
Sample method: Random sampling, n=100,000, random_state=42
Analysis date: April 2026

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This project was created for educational purposes only and is submitted as part of a Data Science course assignment at Reichman University.