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| import pandas as pd | |
| import joblib | |
| from config import TFIDF_VECTORIZER_PATH, MODEL_PATH | |
| # Sample Input Text | |
| test_text = """ | |
| Transaction ID: T123456789 | |
| Origin: India | |
| Designation: Manager | |
| Keywords: sanction, fraud, transaction | |
| Name: John Doe | |
| SWIFT Tag: XYZ123 | |
| Currency: USD | |
| Entity: ACME Corp | |
| Message: Urgent transaction request | |
| City: Mumbai | |
| Country: India | |
| State: Maharashtra | |
| Hit Type: Sanctions | |
| Record Matching String: match-123 | |
| WatchList Match String: sanction-456 | |
| Payment Sender: Sender ABC | |
| Payment Receiver: Receiver XYZ | |
| Swift Message Type: MT103 | |
| Text Sanction Data: suspected sanctions | |
| Matched Sanctioned Entity: Entity123 | |
| Red Flag Reason: Unusual transfer | |
| Risk Level: High | |
| Risk Score: 85.5 | |
| CDD Level: Enhanced | |
| PEP Status: Yes | |
| Sanction Description: SDN List | |
| Checker Notes: Needs further review | |
| Sanction Context: Context of sanction | |
| Maker Action: Escalated | |
| Customer Type: Individual | |
| Industry: Finance | |
| Transaction Type: Wire | |
| Transaction Channel: Online | |
| Geographic Origin: India | |
| Geographic Destination: Russia | |
| Risk Category: Category A | |
| Risk Drivers: Risky geography | |
| Alert Status: Open | |
| Investigation Outcome: Pending | |
| Source of Funds: Unknown | |
| Purpose of Transaction: Payment | |
| Beneficial Owner: Jane Roe | |
| """ | |
| # Load models | |
| vectorizer = joblib.load(TFIDF_VECTORIZER_PATH) | |
| model = joblib.load(MODEL_PATH) | |
| # Predict | |
| test_vec = vectorizer.transform([test_text]) | |
| prediction = model.predict(test_vec) | |
| print("Predicted Labels:", prediction[0]) | |