""" Lead.AI Fraud Shield — Live Demo Explainable AI fraud detection for small business transactions. Visit https://www.lead-ai.us for a custom deployment. """ import gradio as gr import numpy as np import pandas as pd from sklearn.ensemble import RandomForestClassifier from sklearn.preprocessing import LabelEncoder import warnings warnings.filterwarnings("ignore") # ── Train a demo model on synthetic data ───────────────────────────────────── np.random.seed(42) n = 2000 amounts = np.concatenate([np.random.uniform(1, 500, 1700), np.random.uniform(500, 5000, 300)]) hours = np.concatenate([np.random.randint(8, 22, 1700), np.random.randint(0, 6, 300)]) freq_7d = np.concatenate([np.random.randint(1, 8, 1700), np.random.randint(10, 30, 300)]) is_new = np.concatenate([np.random.binomial(1, 0.2, 1700), np.random.binomial(1, 0.8, 300)]) intl = np.concatenate([np.random.binomial(1, 0.05, 1700), np.random.binomial(1, 0.6, 300)]) labels = np.concatenate([np.zeros(1700), np.ones(300)]) X = np.column_stack([amounts, hours, freq_7d, is_new, intl]) y = labels model = RandomForestClassifier(n_estimators=100, random_state=42) model.fit(X, y) MERCHANT_TYPES = ["Retail", "Restaurant", "Online Store", "Gas Station", "ATM / Cash", "Subscription Service", "Unknown"] RISK_REASONS = { "amount": ("Amount is unusually high for this merchant type", "Transaction amount is within normal range"), "hour": ("Transaction occurred outside normal business hours", "Transaction time is within normal hours"), "freq_7d": ("Unusually high transaction frequency in the last 7 days", "Transaction frequency is normal"), "is_new": ("Payment method was registered recently", "Established payment method with history"), "intl": ("International transaction detected", "Domestic transaction"), } def analyze_transaction(amount, merchant_type, hour, freq_7d, is_new_card, is_international): features = np.array([[amount, hour, freq_7d, int(is_new_card), int(is_international)]]) prob = model.predict_proba(features)[0][1] risk_pct = round(prob * 100, 1) if risk_pct < 25: level, color, verdict = "LOW", "🟢", "APPROVED" elif risk_pct < 60: level, color, verdict = "MEDIUM", "🟡", "REVIEW RECOMMENDED" else: level, color, verdict = "HIGH", "🔴", "FLAG FOR INVESTIGATION" # Plain-English explanation thresholds = { "amount": (amount > 800, amount, "$"), "hour": (hour < 6 or hour > 22, hour, "h"), "freq_7d": (freq_7d > 8, freq_7d, " txns/7d"), "is_new": (is_new_card, "", ""), "intl": (is_international, "", ""), } flags, clears = [], [] for key, (triggered, val, unit) in thresholds.items(): if triggered: flags.append(f"⚠️ {RISK_REASONS[key][0]}") else: clears.append(f"✓ {RISK_REASONS[key][1]}") explanation = f"## {color} Risk Level: {level} ({risk_pct}%)\n\n" explanation += f"**Verdict: {verdict}**\n\n" explanation += "---\n\n" explanation += "### Why This Score?\n\n" if flags: explanation += "**Risk Factors Detected:**\n" explanation += "\n".join(flags) + "\n\n" if clears: explanation += "**Factors Within Normal Range:**\n" explanation += "\n".join(clears) + "\n\n" explanation += "---\n\n" explanation += "> ⚠️ This is a demo system. " explanation += "Your production Lead.AI Fraud Shield will be trained on your actual transaction history.\n\n" explanation += "**[→ Get a Custom Fraud Shield for Your Business](https://www.lead-ai.us)**" bar = f"Risk Score: {'█' * int(risk_pct // 5)}{'░' * (20 - int(risk_pct // 5))} {risk_pct}%" return bar, explanation # ── Gradio UI ───────────────────────────────────────────────────────────────── with gr.Blocks( title="Lead.AI Fraud Shield", theme=gr.themes.Soft(primary_hue="red"), css=".footer { text-align:center; margin-top:20px; color:#666; }" ) as demo: gr.Markdown(""" # 🛡 Lead.AI Fraud Shield ### Explainable AI Fraud Detection for Small Businesses Enter a transaction below. The AI will score it for risk and explain exactly why — in plain English. > 💼 This is a live proof-of-concept. [Request a custom system →](https://www.lead-ai.us) """) with gr.Row(): with gr.Column(): gr.Markdown("### Transaction Details") amount = gr.Slider(1, 5000, value=120, step=1, label="Transaction Amount ($)") merchant = gr.Dropdown(MERCHANT_TYPES, value="Retail", label="Merchant Type") hour = gr.Slider(0, 23, value=14, step=1, label="Hour of Day (0=midnight, 14=2pm)") freq_7d_in = gr.Slider(1, 30, value=3, step=1, label="Transactions in Last 7 Days (same card)") is_new_card = gr.Checkbox(label="New Payment Method (registered < 7 days ago)") is_intl = gr.Checkbox(label="International Transaction") btn = gr.Button("🔍 Analyze Transaction", variant="primary", size="lg") with gr.Column(): gr.Markdown("### AI Analysis") score_bar = gr.Textbox(label="Risk Score", lines=1) result_md = gr.Markdown() btn.click( fn=analyze_transaction, inputs=[amount, merchant, hour, freq_7d_in, is_new_card, is_intl], outputs=[score_bar, result_md], ) gr.Examples( examples=[ [4800, "ATM / Cash", 2, 18, True, True], [45, "Restaurant", 12, 2, False, False], [299, "Online Store", 20, 5, False, False], [1500, "Unknown", 3, 15, True, True], ], inputs=[amount, merchant, hour, freq_7d_in, is_new_card, is_intl], label="Try These Examples", ) gr.Markdown(""" ---
🌐 www.lead-ai.us  |  💻 GitHub  |  🤗 Hugging Face

Need this customized for your business? Request a Custom Lead.AI Setup →
""") if __name__ == "__main__": demo.launch()