bert-base-bank-mail-fraud-detector

🕵️‍♂️ BERT-Base Bank Mail & Phishing Fraud Detector

This model is a fine-tuned version of bert-base-uncased optimized specifically for identifying binary classification boundaries between legitimate business communication and malicious phishing/fraudulent email templates.

Developed as a highly resilient baseline security layer, this model successfully resolves typical representation convergence bugs (NaN log updates) found in larger architectures by utilizing strict token length packing bounds and zero-division safeguards during training evaluation.

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📊 Core Production Performance Metrics

The model achieved the following performance metrics on the validation dataset split ($20%$ isolated test pool) during its multi-epoch convergence sequence:

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🛠️ Dataset & Sanitization Pipeline

The architecture was trained using automated multi-CSV pipelines derived from the consolidated phishing email data streams. The configuration features:

• Strict Deduplication: Eliminates duplicate message structures to neutralize model over-fitting loops.
• Type Enforcement: Forceful numeric mapping filters out corrupt rows or loose strings, locking target labels into strict 64-bit integer shapes.
• Optimized Context Windows: Sequences packed at a maximum length of 128 tokens maintain tight gradient calculations and high processing throughput.

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🚀 Live Operational Inference Usage

To deploy this model downstream within your automated agentic or security response routing pipelines, you can load the fine-tuned parameters directly using the Hugging Face transformers framework:

import torch
import numpy as np
from transformers import AutoTokenizer, AutoModelForSequenceClassification

model_id = "sourav-paramanya/bert-base-bank-mail-fraud-detector"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)

def scan_incoming_email(email_text):
    inputs = tokenizer(email_text, return_tensors="pt", padding=True, truncation=True, max_length=512)
    
    with torch.no_grad():
        outputs = model(**inputs)
        
    logits = outputs.logits
    probabilities = torch.softmax(logits, dim=1).numpy()[0]
    predicted_class = np.argmax(probabilities)
    
    labels_map = {0: "Legitimate (Safe) ✅", 1: "FRAUD / PHISHING DETECTED 🚨"}
    
    print(f"Analysis Verdict : {labels_map[predicted_class]}")
    print(f"Confidence Matrix: Safe -> {probabilities[0]:.2%}, Fraud -> {probabilities[1]:.2%}")

# Live test scenario
scan_incoming_email("URGENT: Your online business portal access is frozen. Click here to confirm identity immediate.")