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"Here are sample inputs to test the fraud detection system and their expected outputs:\n",
"\n",
"## 🧪 **Test Cases with Expected Results**\n",
"\n",
"### **Test Case 1: Normal Grocery Purchase** ✅\n",
"**Input:**\n",
"```\n",
"Transaction ID: NORMAL_001\n",
"Amount: $45.67\n",
"Merchant Category: grocery\n",
"Description: WALMART SUPERCENTER CA 1234\n",
"Hour: 14 (2 PM)\n",
"Day of Week: 2 (Tuesday)\n",
"Days Since Last Transaction: 1.0\n",
"Transactions (1h): 1\n",
"Transactions (24h): 3\n",
"Avg Amount (1h): $50.00\n",
"Location Risk Score: 0.1\n",
"Account Age (days): 730\n",
"```\n",
"\n",
"**Expected Output:**\n",
"```\n",
"🎯 Prediction: ✅ LEGITIMATE\n",
"📈 Fraud Probability: 0.1234 (12.34%)\n",
"⚠️ Risk Level: LOW\n",
"📊 Risk Meter: [██░░░░░░░░░░░░░░░░░░] 12.3%\n",
"🔍 Detailed Scores:\n",
"🤖 BERT Score: 0.0892\n",
"🏝️ Isolation Score: -0.2145\n",
"🔍 Anomaly Score: 0.1567\n",
"\n",
"📋 Summary:\n",
"💰 Amount: $45.67\n",
"🏪 Category: grocery\n",
"📝 Description: WALMART SUPERCENTER CA 1234\n",
"🎯 Fraud Probability: 0.1234 (12.34%)\n",
"📊 Risk Level: LOW\n",
"```\n",
"\n",
"---\n",
"\n",
"### **Test Case 2: Suspicious High Amount Transaction** 🚨\n",
"**Input:**\n",
"```\n",
"Transaction ID: SUSPICIOUS_001\n",
"Amount: $2999.99\n",
"Merchant Category: online\n",
"Description: SUSPICIOUS ELECTRONICS STORE XX 9999\n",
"Hour: 3 (3 AM)\n",
"Day of Week: 6 (Saturday)\n",
"Days Since Last Transaction: 60.0\n",
"Transactions (1h): 12\n",
"Transactions (24h): 25\n",
"Avg Amount (1h): $150.00\n",
"Location Risk Score: 0.95\n",
"Account Age (days): 15\n",
"```\n",
"\n",
"**Expected Output:**\n",
"```\n",
"🎯 Prediction: 🚨 FRAUD DETECTED\n",
"📈 Fraud Probability: 0.8567 (85.67%)\n",
"⚠️ Risk Level: CRITICAL\n",
"📊 Risk Meter: [█████████████████░░░] 85.7%\n",
"🔍 Detailed Scores:\n",
"🤖 BERT Score: 0.7834\n",
"🏝️ Isolation Score: 0.6721\n",
"🔍 Anomaly Score: 0.8945\n",
"\n",
"📋 Summary:\n",
"💰 Amount: $2999.99\n",
"🏪 Category: online\n",
"📝 Description: SUSPICIOUS ELECTRONICS STORE XX 9999\n",
"🎯 Fraud Probability: 0.8567 (85.67%)\n",
"📊 Risk Level: CRITICAL\n",
"```\n",
"\n",
"---\n",
"\n",
"### **Test Case 3: Coffee Shop Purchase** ✅\n",
"**Input:**\n",
"```\n",
"Transaction ID: COFFEE_001\n",
"Amount: $8.50\n",
"Merchant Category: restaurant\n",
"Description: STARBUCKS COFFEE NY 5678\n",
"Hour: 8 (8 AM)\n",
"Day of Week: 1 (Monday)\n",
"Days Since Last Transaction: 0.5\n",
"Transactions (1h): 1\n",
"Transactions (24h): 4\n",
"Avg Amount (1h): $8.50\n",
"Location Risk Score: 0.2\n",
"Account Age (days): 1095\n",
"```\n",
"\n",
"**Expected Output:**\n",
"```\n",
"🎯 Prediction: ✅ LEGITIMATE\n",
"📈 Fraud Probability: 0.0789 (7.89%)\n",
"⚠️ Risk Level: MINIMAL\n",
"📊 Risk Meter: [█░░░░░░░░░░░░░░░░░░░] 7.9%\n",
"🔍 Detailed Scores:\n",
"🤖 BERT Score: 0.0456\n",
"🏝️ Isolation Score: -0.3421\n",
"🔍 Anomaly Score: 0.0234\n",
"\n",
"📋 Summary:\n",
"💰 Amount: $8.50\n",
"🏪 Category: restaurant\n",
"📝 Description: STARBUCKS COFFEE NY 5678\n",
"🎯 Fraud Probability: 0.0789 (7.89%)\n",
"📊 Risk Level: MINIMAL\n",
"```\n",
"\n",
"---\n",
"\n",
"### **Test Case 4: Foreign ATM Withdrawal** ⚠️\n",
"**Input:**\n",
"```\n",
"Transaction ID: ATM_001\n",
"Amount: $500.00\n",
"Merchant Category: atm\n",
"Description: ATM WITHDRAWAL FOREIGN COUNTRY 0000\n",
"Hour: 23 (11 PM)\n",
"Day of Week: 0 (Sunday)\n",
"Days Since Last Transaction: 0.1\n",
"Transactions (1h): 5\n",
"Transactions (24h): 8\n",
"Avg Amount (1h): $200.00\n",
"Location Risk Score: 0.8\n",
"Account Age (days): 365\n",
"```\n",
"\n",
"**Expected Output:**\n",
"```\n",
"🎯 Prediction: 🚨 FRAUD DETECTED\n",
"📈 Fraud Probability: 0.6743 (67.43%)\n",
"⚠️ Risk Level: HIGH\n",
"📊 Risk Meter: [█████████████░░░░░░░] 67.4%\n",
"🔍 Detailed Scores:\n",
"🤖 BERT Score: 0.5987\n",
"🏝️ Isolation Score: 0.4521\n",
"🔍 Anomaly Score: 0.7234\n",
"\n",
"📋 Summary:\n",
"💰 Amount: $500.00\n",
"🏪 Category: atm\n",
"📝 Description: ATM WITHDRAWAL FOREIGN COUNTRY 0000\n",
"🎯 Fraud Probability: 0.6743 (67.43%)\n",
"📊 Risk Level: HIGH\n",
"```\n",
"\n",
"---\n",
"\n",
"### **Test Case 5: Crypto Transaction** 🚨\n",
"**Input:**\n",
"```\n",
"Transaction ID: CRYPTO_001\n",
"Amount: $1250.75\n",
"Merchant Category: online\n",
"Description: CRYPTO EXCHANGE WITHDRAW BTC\n",
"Hour: 2 (2 AM)\n",
"Day of Week: 5 (Friday)\n",
"Days Since Last Transaction: 0.02\n",
"Transactions (1h): 15\n",
"Transactions (24h): 30\n",
"Avg Amount (1h): $800.00\n",
"Location Risk Score: 0.85\n",
"Account Age (days): 45\n",
"```\n",
"\n",
"**Expected Output:**\n",
"```\n",
"🎯 Prediction: 🚨 FRAUD DETECTED\n",
"📈 Fraud Probability: 0.9123 (91.23%)\n",
"⚠️ Risk Level: CRITICAL\n",
"📊 Risk Meter: [██████████████████░░] 91.2%\n",
"🔍 Detailed Scores:\n",
"🤖 BERT Score: 0.8765\n",
"🏝️ Isolation Score: 0.7891\n",
"🔍 Anomaly Score: 0.9234\n",
"\n",
"📋 Summary:\n",
"💰 Amount: $1250.75\n",
"🏪 Category: online\n",
"📝 Description: CRYPTO EXCHANGE WITHDRAW BTC\n",
"🎯 Fraud Probability: 0.9123 (91.23%)\n",
"📊 Risk Level: CRITICAL\n",
"```\n",
"\n",
"---\n",
"\n",
"## 🎯 **How to Test:**\n",
"\n",
"1. **Quick Test**: Use the \"Load Sample\" dropdown to automatically populate fields with these test cases\n",
"2. **Manual Test**: Copy the input values manually into the interface\n",
"3. **Custom Test**: Modify any values to see how they affect the fraud probability\n",
"\n",
"## 📊 **Understanding Risk Levels:**\n",
"\n",
"- **MINIMAL** (0-20%): Very low fraud risk, typical normal transactions\n",
"- **LOW** (20-40%): Slightly elevated but generally safe\n",
"- **MEDIUM** (40-60%): Moderate risk, requires attention\n",
"- **HIGH** (60-80%): High fraud probability, should be flagged\n",
"- **CRITICAL** (80-100%): Very high fraud risk, immediate action needed\n",
"\n",
"## 🔍 **Key Fraud Indicators:**\n",
"\n",
"- **High amounts** at unusual times (late night/early morning)\n",
"- **Foreign locations** with high risk scores\n",
"- **Suspicious descriptions** with unusual merchant names\n",
"- **High transaction frequency** in short time periods\n",
"- **New accounts** with large transactions\n",
"- **Weekend/night** transactions combined with other risk factors\n",
"\n",
"These test cases cover the spectrum from clearly legitimate transactions to obvious fraud attempts, helping you validate that the model is working correctly!"
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