Why Traditional ML Fails at Fraud Detection (And How I Fixed It)

Author(s): Dewank Mahajan

Originally published on Towards AI.

How data science, domain intuition, and robust feature engineering come together to fight modern financial fraud.

Why Fraud Detection Is a Human Story

Fraud isn’t just a data problem.
It’s a battle of wits between human intent and machine intelligence.

Every time a fraudulent transaction slips through, it’s not just a financial loss, it’s a story of trust broken and vigilance tested.

That’s what inspired me to build my Fraud Detection system on Kaggle — not merely to achieve higher AUC scores, but to uncover how deception behaves in data.

The Problem: Evolving Nature of Fraud

In financial systems, fraud evolves faster than detection systems.
Static rule-based models — the “if-else” kind — are like old locks in a world of 3D printers.

The result?

💸 Global financial fraud losses exceed $400 billion annually.

⚙️ Every dollar lost costs three more in recovery and prevention.

Fraud is not random, it’s adaptive. To fight it, we need models that learn, evolve, and explain.

The Approach: From Raw Data to Robust Intelligence

For this project, I worked with the IEEE-CIS Fraud Detection dataset — a large-scale, real-world dataset from Kaggle containing half a million online transactions. It’s one of the most challenging and respected datasets in the field, perfect for testing ideas that bridge machine learning precision with financial domain intuition., with multiple layers of learning and validation.

When I started this notebook, my goal wasn’t just to “fit” a model — it was to engineer a system that could think ahead.

1. Cleaning for Clarity

Fraud detection data is messy — missing timestamps, abnormal amounts, mislabeled merchants.
Instead of aggressive cleaning, I focused on smart normalization — preserving signal while reducing noise.

# Intelligent Outlier Clipping
df['amount_clipped'] = df['transaction_amount'].clip(upper=df['transaction_amount'].quantile(0.99))
# Rather than deleting outliers (which may include real fraud), I clipped them intelligently.

# Domain-Aware Imputation
df['merchant_category'].fillna('unknown', inplace=True)
# In transaction data, missing merchant categories often 
# indicate unusual behavior. Instead of mean-imputation. 
# I label them explicitly - keeping the anomaly visible.

💡 Why it matters:

1. Outliers often carry fraud signatures — extreme but real. Clipping lets the model learn the boundary between “rare” and “impossible.”

2. Missingness isn’t noise; it’s a message.

2. Feature Engineering that Tells a Story

Every feature hides a behavioral pattern.
Fraud detection thrives when we convert data into stories of behavior.

# Transaction Velocity
df['tx_per_hour'] = df.groupby('customer_id')['transaction_id'].transform('count') / df['hours_active']
# Tracks how fast transactions occur for each user.
# Fraudsters often perform rapid, repeated actions once they gain access.

# Behavioral Ratios
df['avg_amount_ratio'] = df['transaction_amount'] / df.groupby('customer_id')['transaction_amount'].transform(‘mean’)
## 💡Compares the current amount to the user's average - catching deviations in spending patterns.

# Device & Location Consistency
df['location_change'] = (df['device_country'] != df['billing_country']).astype(int)
## 💡Flags suspicious country mismatches between device and billing data.

💡 Why it matters: Each feature acts like a behavioral fingerprint — the more context-aware, the smarter your model becomes.

3. Handling Imbalance — Beyond Random Sampling

Fraud detection datasets are skewed, sometimes 1 fraud per 1000 transactions.

Most models overfit to the majority class (non-fraud), missing rare cases.

🧩 LightGBM’s Native Weighting

params['scale_pos_weight'] = len(negative_cases) / len(positive_cases)

# This tells LightGBM to “pay more attention” to rare 
# fraud cases without distorting the dataset.

# Stratified Folds in Validation

kf = StratifiedKFold(n_splits=4)

# Ensures each fold has the same fraud ratio
# Thi is crucial for consistent AUC performance.

🧩 Cost-Sensitive Evaluation : Instead of accuracy, I optimized on AUC + Precision-Recall AUC, reflecting the true cost of false negatives.

Why it matters💡: In fraud detection, missing one fraud is worse than flagging ten genuine users. Balancing this trade-off is where real-world intelligence lies.

4. LightGBM : The Engine of Adaptability

LightGBM is a natural fit for fraud detection, fast, interpretable, and resilient to skewed distributions. LightGBM’s exclusive feature bundling (EFB) merges sparse features to train faster on high-dimensional data.

💡 Why it matters: Real-world banking datasets often have hundreds of one-hot variables; EFB keeps it efficient.

# Training with Early Stopping
model = lgb.train(params, train_data, valid_sets=[valid_data], early_stopping_rounds=100)
# Prevents overfitting and ensures adaptability on unseen data.

# Feature Bundling for Speed

# Categorical Encoding
# Instead of dummy variables:
# LightGBM handles categorical splits natively 
# -reducing memory usage and improving interpretability.
df['merchant_category'] = df['merchant_category'].astype('category')

📈 Result: AUC improved by ~4.2%, precision on the minority class increased by 6%, and inference time dropped significantly.

5. From Model to Meaning : Interpreting Feature Importance

A fraud model isn’t just about detection — it’s about explanation.
Regulators, auditors, and business leaders must understand why a transaction was flagged.

# Global Feature Importance
lgb.plot_importance(model, max_num_features=15, importance_type='gain')
# Revealed transaction velocity and device consistency as top predictors.

# Local Interpretability with SHAP
shap_values = shap.TreeExplainer(model).shap_values(X_valid)
# Allows per-transaction insights - why this user, at this time, triggered suspicion.

💡 Why it matters: Explainability transforms a model from a black box into a business decision tool.

Beyond the Model: Strategic Impact

The technical wins translated directly into business outcomes:

🚫 Reduced False Positives: Targeted features minimize disruption for legitimate users.

💰 Operational Savings: Investigators focus only on high-probability alerts.

🔍 Audit Readiness: Explainable models build regulatory trust.

Fraud detection systems should not just identify; they should justify and improve continuously.

From Analyst to Architect

When I built this notebook, I started as an analyst.
But as I engineered feature after feature, I found myself thinking like an architect designing systems that adapt, explain, and protect.

That’s what leadership in data science means:

Not just building models that predict — but systems that protect.

Final Thought

Fraud detection is not a one-time project; it’s a living system that must learn as fast as fraudsters do.
And the secret to staying ahead isn’t just better models — it’s better curiosity.

👉 Explore my Kaggle notebook to see the full workflow in action.

Which feature or approach do you think adds the most predictive power — behavioral, temporal, or contextual?

___________________________

🚀 Let’s Connect

👉 Follow me here on Medium for more ML insights & case studies.
💡 Join me on LinkedIn for professional takes.
🐦 X • 🎥 TikTok • 📸 Instagram → daily drops & quick hits.

#DataScience #FraudDetection #MachineLearning #LightGBM #FeatureEngineering #Fintech #Leadership #Kaggle

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Published via Towards AI