Fraud detection using reports and dashboards:

 

💡 Interview Answer: Fraud Detection — Dashboard Scenario

"In one of my previous data analysis projects, I worked on building a fraud detection monitoring dashboard for a financial services client. The goal was to identify abnormal transaction patterns and flag suspicious customer behavior in real time."

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⚡ Scenario: Detecting Fraudulent Transactions

Business Context:
A retail bank was facing frequent cases of credit card fraud, identity theft, and account takeovers. My responsibility was to design an interactive dashboard that would help both analysts and fraud investigators detect early signs of fraudulent behavior.

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📊 Key Fraud Detection Visualizations:

1. Transaction Heatmap (Time vs Amount)
   → Use: Highlights unusual spikes in transaction volume or value, especially outside normal business hours.
   💡 Example: A customer usually spends under $500 per day, suddenly five $3000 transactions happen between 2 AM and 4 AM.

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2. Geographical Inconsistency Map
   → Use: Tracks logins and transactions from geographically impossible locations.
   💡 Example: Card used in London at 3 PM, and in New York at 4 PM — flag as impossible travel.

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3. Velocity Pattern Chart (Bar or Line Chart)
   → Use: Monitors the number of transactions per customer in short intervals.
   💡 Example: 20 transactions attempted in 5 minutes — usually a red flag for bot or script-based fraud.

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4. Rule-Based Anomaly Table
   → Use: Displays transactions breaking pre-defined rules:

   • Amount exceeds average by 3x.

   • Location mismatched from usual pattern.

   • New device or IP address.

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5. Account Risk Score (Gauge or Indicator)
   → Use: Calculated using weighted logic (based on amount, location, frequency, device) to give a risk level:

   • Green: Normal

   • Yellow: Suspicious

   • Red: Highly Suspicious

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6. Fraud Trend Over Time (Line Chart)
   → Use: Shows how fraud attempts evolve over weeks/months, especially after launching new preventive controls.
   💡 Example: Sudden dip after blocking foreign IP ranges; sudden spike during holiday sales.

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🧠 Insight Example:

"For instance, by combining velocity patterns and geo-inconsistency maps, we caught a case where a single user made 18 transactions from multiple countries within 2 hours, which was impossible for human activity. The dashboard automatically flagged this for investigation, and the fraud team prevented further loss."

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🧾 Why This Dashboard Was Effective:

• Enabled real-time alerting for suspicious activity.

• Combined transaction behavior analysis with geographical patterns.

• Helped fraud teams prioritize cases based on risk score.

• Provided historical fraud trends for management to evaluate the success of prevention strategies.

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💡 Extra Tip:

If the interviewer asks you to visualize it, sketch this:

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| Fraud Detection Dashboard                         |
|---------------------------------------------------|
| Heatmap: Hour vs Transaction Amount               |
| Map: Customer Location Activity                   |
| Line Chart: Transaction Velocity Over Time        |
| Table: Rule Violations                            |
| Gauge: Account Risk Score                         |
-----------------------------------------------------

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✅ If you like, I can also help you prepare a quick mock-up for this in Power BI or create a diagram for your interview notes. Want me to prepare one?

💡 Interview Answer: Telecom Fraud Detection Scenario (Airtel)

"In one of my recent projects, I designed a reporting and visualization framework for detecting fraudulent activities in telecom operations — specifically for a large telecom provider like Airtel. The goal was to monitor and flag irregular customer behavior, prevent revenue leakage, and help the fraud investigation team act proactively."

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📱 Scenario: SIM Swap & International Call Fraud

Business Context:
Telecom fraud can take many forms: SIM swap fraud, subscription fraud, international revenue share fraud (IRSF), call forwarding scams, and fake identity usage for new connections.
In this case, the project focused on detecting SIM swap fraud and international call anomalies.

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📊 Key Visualizations and Reports Designed:

1. SIM Swap Detection (Event Sequence Report)
   → Use: Detects cases where a SIM card is swapped and large transactions or account changes happen immediately after.
   💡 Example: A SIM is swapped at 10:15 AM and international calls are made at 10:30 AM — a typical fraud indicator.

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2. International Call Anomaly Chart (Bar / Line)
   → Use: Displays sudden spikes in international call duration or frequency from a single number.
   💡 Example: A prepaid customer usually makes local calls, but suddenly makes 20 international calls to high-risk destinations (like satellite phones or premium-rate numbers) within 2 hours.

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3. Customer Activity Heatmap (Time vs Usage)
   → Use: Highlights unusual call, SMS, or data usage patterns beyond normal business hours or customer profile.
   💡 Example: A user with an average of 5 daily calls suddenly makes 100+ calls after midnight.

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4. Geo-Location Drift Analysis (Map Visualization)
   → Use: Detects suspicious SIM or device movement between locations inconsistent with usual usage.
   💡 Example: A SIM registers in Delhi at 2 PM, and shows usage from Mumbai 30 minutes later.

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5. Subscription Fraud Table (Rule-Based List)
   → Use: Flags customers who activate new numbers, use large data packages or make high-value international calls within the first few hours — a classic fraud pattern.
   💡 Example: New user -> SIM activation -> immediate high-value international usage.

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🧠 Insight Example:

"For instance, the dashboard once revealed a sudden spike in international call duration immediately after a SIM swap. Upon further investigation, it was confirmed that the customer’s identity had been compromised, and the fraudster was exploiting international revenue share fraud. Early detection through the dashboard allowed us to block the number and prevent significant revenue leakage."

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🎯 Why This Dashboard Was Valuable:

• Provided real-time monitoring of high-risk behaviors.

• Helped fraud teams prioritize investigations based on risk indicators.

• Used geospatial analysis for verifying user location anomalies.

• Combined customer profile history with sudden behavior deviation.

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💡 How I Structured the Dashboard:

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| Telecom Fraud Detection Dashboard (Airtel)           |
|------------------------------------------------------|
| Table: SIM Swap → Activity Time Gap Analysis         |
| Line Chart: International Calls Over Time            |
| Heatmap: Calls / Data Usage by Hour                  |
| Map: Customer Location Movement                      |
| Table: Subscription Fraud Risk Scores                |
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