Banking and Finance Applications 🏦🔐

In banking, data is money. Banks use data mining to answer two critical questions every second: "Is this loan safe?" and "Is this transaction a theft?"

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1. Credit Risk & Scoring

When a bank gives a loan, they are "buying" a risk. To manage this, they use Predictive Modeling:

• The Data: Past 5 years of salary, rent/EMI payments, credit card utilization, and "Social" indicators.
• The Scorecard: A point-based system. If your score is 750+, you are "Auto-Approved." If it's 300-500, you are "Rejected."
• Impact: This allows banks to process 10,000 loan applications an hour without a human manager looking at every single one.

2. Fraud Detection in Real-Time

Fraud detection uses Anomaly Detection (finding the "Odd One Out").

• Rule-Based: "If transaction > ₹50,000 AND in a new country, block it."
• AI-Based: The system learns your "Digital Breath"—the time of day you shop, the stores you like, and even how fast you type your PIN. If a transaction doesn't "feel" like you, it is flagged.
• Velocity Checks: Identifying if a card is being used 50 times in 5 minutes (a sign of a bot attack).

3. AML (Anti-Money Laundering)

Banks are the "Police" of the financial world. Data mining helps find Illegal Money Flow.

• Structuring (Smurfing): Finding people who deposit ₹49,000 ten times to avoid the ₹50,000 tax reporting limit.
• Layering: Identifying money moving through 10 different accounts across 5 countries in 1 hour.
• Red Flags: High-volume cash deposits into a "Dormant" (inactive) account.

Summary

• Credit Scoring replaces human "guessing" with statistical evidence.
• Fraud Detection uses real-time patterns to stop identity theft and bot attacks.
• AML Systems protect the global economy from criminal and terrorist funding.
• False Positives are the main metric for "tuning" a banking BI engine.

Quiz Time! 🎯

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