Delete fund-monitoring.py

fund-monitoring.py

@@ -1,334 +0,0 @@
-def render_fund_monitoring(transactions_df):
-    """
-    Render the AI-powered fund monitoring page.
-    
-    This feature helps startups monitor spending, detect fraudulent transactions,
-    and maintain investor trust through AI-powered analysis.
-    """
-    st.markdown("<h1 class='main-header'>Investor Fund Monitoring</h1>", unsafe_allow_html=True)
-    st.markdown("<p class='sub-header'>AI-powered fraud detection and spending analysis</p>", unsafe_allow_html=True)
-    
-    # How AI helps with fund monitoring
-    with st.expander("ℹ️ How AI enhances fund monitoring"):
-        st.markdown("""
-        ### How AI Powers Your Fund Monitoring
-        
-        The fund monitoring system uses AI to help maintain investor trust and optimize spending:
-        
-        - **Anomaly Detection**: Our AI models identify unusual transactions that don't match typical startup spending patterns
-        - **Risk Scoring**: Each transaction is assigned a risk score based on multiple factors like amount, category, vendor, and description
-        - **Pattern Recognition**: The system identifies potentially concerning spending trends across categories over time
-        - **Fraud Prevention**: AI algorithms flag transactions that match known patterns of misuse before they become issues
-        - **Investor-Ready Reporting**: Generate reports that demonstrate responsible financial stewardship to investors
-        
-        This helps founders maintain investor trust, prevent misuse of funds, and create transparency in financial operations.
-        """)
-    
-    st.write("Monitor your startup's spending to maintain investor trust and ensure proper fund usage. Our AI algorithms automatically flag suspicious transactions and identify spending patterns.")
-    
-    # AI insights for fund monitoring
-    insights_key = f"fund_monitoring_{date.today().isoformat()}"
-    if insights_key not in st.session_state.insights_cache:
-        insights = generate_ai_response("""
-        You are a financial fraud detection expert. Provide 2-3 critical spending patterns that investors typically look for when monitoring startup fund usage.
-        Format as brief bullet points focused on maintaining investor trust.
-        """, simulate=True)
-        st.session_state.insights_cache[insights_key] = insights
-    
-    with st.expander("🔍 AI Monitoring Insights", expanded=True):
-        st.markdown("<span class='ai-badge'>AI-Generated Insights</span>", unsafe_allow_html=True)
-        st.markdown(st.session_state.insights_cache[insights_key])
-    
-    # Process transactions to detect suspicious ones with AI enhancement
-    processed_df = detect_suspicious_transactions(transactions_df)
-    
-    # Summary metrics
-    total_transactions = len(processed_df)
-    suspicious_transactions = processed_df[processed_df['Suspicious']].copy()
-    suspicious_count = len(suspicious_transactions)
-    suspicious_amount = suspicious_transactions['Amount'].sum()
-    total_amount = processed_df['Amount'].sum()
-    
-    col1, col2, col3, col4 = st.columns(4)
-    
-    with col1:
-        st.markdown(f"""
-        <div class='metric-card'>
-            <p class='metric-label'>Total Transactions</p>
-            <p class='metric-value'>{total_transactions}</p>
-        </div>
-        """, unsafe_allow_html=True)
-    
-    with col2:
-        flagged_percent = suspicious_count/total_transactions*100 if total_transactions > 0 else 0
-        status = "danger-metric" if flagged_percent > 10 else ("warning-metric" if flagged_percent > 5 else "good-metric")
-        st.markdown(f"""
-        <div class='metric-card'>
-            <p class='metric-label'>Flagged Transactions</p>
-            <p class='metric-value {status}'>{suspicious_count} ({flagged_percent:.1f}%)</p>
-        </div>
-        """, unsafe_allow_html=True)
-    
-    with col3:
-        amount_percent = suspicious_amount/total_amount*100 if total_amount > 0 else 0
-        status = "danger-metric" if amount_percent > 15 else ("warning-metric" if amount_percent > 7 else "good-metric")
-        st.markdown(f"""
-        <div class='metric-card'>
-            <p class='metric-label'>Flagged Amount</p>
-            <p class='metric-value {status}'>${suspicious_amount:,.0f} ({amount_percent:.1f}%)</p>
-        </div>
-        """, unsafe_allow_html=True)
-    
-    with col4:
-        avg_risk = suspicious_transactions['Risk_Score'].mean() if not suspicious_transactions.empty else 0
-        status = "danger-metric" if avg_risk > 50 else ("warning-metric" if avg_risk > 30 else "good-metric")
-        st.markdown(f"""
-        <div class='metric-card'>
-            <p class='metric-label'>Average Risk Score</p>
-            <p class='metric-value {status}'>{avg_risk:.1f}/100</p>
-        </div>
-        """, unsafe_allow_html=True)
-    
-    # Tabs for different views
-    tab1, tab2 = st.tabs(["Flagged Transactions", "All Transactions"])
-    
-    with tab1:
-        if suspicious_count > 0:
-            # Add risk score visualization (color coded)
-            suspicious_view = suspicious_transactions.copy()
-            
-            # Format for display
-            def colorize_risk(val):
-                color = "red" if val > 50 else ("orange" if val > 30 else "blue")
-                return f'background-color: {color}; color: white; font-weight: bold'
-            
-            # Apply styling
-            styled_suspicious = suspicious_view.style.applymap(
-                lambda x: colorize_risk(x) if x > 0 else '', 
-                subset=['Risk_Score']
-            )
-            
-            st.dataframe(
-                suspicious_view[['Date', 'Category', 'Vendor', 'Amount', 'Description', 'Risk_Score', 'Reason']], 
-                use_container_width=True
-            )
-            
-            # Get AI analysis of suspicious transactions
-            fraud_key = f"fraud_{date.today().isoformat()}"
-            if fraud_key not in st.session_state.insights_cache:
-                fraud_analysis = get_fraud_analysis(suspicious_transactions)
-                st.session_state.insights_cache[fraud_key] = fraud_analysis
-            
-            st.markdown("<div class='advisor-card'>", unsafe_allow_html=True)
-            st.markdown("<span class='ai-badge'>AI Fraud Analysis</span>", unsafe_allow_html=True)
-            st.markdown(f"<p class='advice-text'>{st.session_state.insights_cache[fraud_key]}</p>", unsafe_allow_html=True)
-            st.markdown("</div>", unsafe_allow_html=True)
-            
-            # Action buttons
-            st.subheader("Recommended Actions")
-            
-            col1, col2, col3 = st.columns(3)
-            with col1:
-                if st.button("🔍 Investigate All Flagged"):
-                    st.session_state.investigation_started = True
-            with col2:
-                if st.button("📝 Generate Investor Report"):
-                    st.session_state.report_generated = True  
-            with col3:
-                if st.button("✅ Mark Reviewed"):
-                    st.session_state.marked_reviewed = True
-            
-            # Simulate action responses
-            if 'investigation_started' in st.session_state and st.session_state.investigation_started:
-                st.success("Investigation initiated for all flagged transactions. Your financial team will be notified.")
-            
-            if 'report_generated' in st.session_state and st.session_state.report_generated:
-                st.success("Investor report generated and ready for review before sending.")
-                
-            if 'marked_reviewed' in st.session_state and st.session_state.marked_reviewed:
-                st.success("All transactions marked as reviewed. Status will be updated in the system.")
-        else:
-            st.success("No suspicious transactions detected by our AI system. Your spending appears to be normal for a startup at your stage.")
-    
-    with tab2:
-        st.dataframe(processed_df[['Date', 'Category', 'Vendor', 'Amount', 'Description', 'Suspicious', 'Risk_Score']], 
-                    use_container_width=True)
-    
-    # Spending patterns
-    st.subheader("Spending Pattern Analysis")
-    
-    # Category breakdown
-    category_spending = processed_df.groupby('Category')['Amount'].sum().reset_index()
-    
-    col1, col2 = st.columns(2)
-    
-    with col1:
-        fig = px.bar(category_spending, x='Category', y='Amount',
-                    title="Spending by Category",
-                    labels={'Amount': 'Total Spent ($)'},
-                    color='Amount',
-                    color_continuous_scale='Blues')
-        fig.update_layout(
-            height=400,
-            plot_bgcolor='rgba(240,247,255,0.8)',
-            xaxis_title="Category",
-            yaxis_title="Amount Spent ($)",
-            font=dict(family="Arial, sans-serif", size=12),
-            margin=dict(l=20, r=20, t=40, b=20),
-        )
-        st.plotly_chart(fig, use_container_width=True)
-    
-    with col2:
-        # AI spending pattern analysis
-        spending_key = f"spending_pattern_{date.today().isoformat()}"
-        if spending_key not in st.session_state.insights_cache:
-            spending_pattern_analysis = generate_ai_response("""
-            You are a startup spending analyst. Review the spending patterns and provide 3 key insights about:
-            1. Categories that appear to have unusually high spending
-            2. Potential areas where spending could be optimized
-            3. Changes in spending patterns that investors might find concerning
-            
-            Format as concise, actionable bullet points.
-            """, simulate=True)
-            st.session_state.insights_cache[spending_key] = spending_pattern_analysis
-        
-        st.markdown("<div class='insight-card'>", unsafe_allow_html=True)
-        st.markdown("<span class='ai-badge'>AI Spending Analysis</span>", unsafe_allow_html=True)
-        st.markdown(st.session_state.insights_cache[spending_key])
-        st.markdown("</div>", unsafe_allow_html=True)
-    
-    # Time series of spending
-    processed_df['Date'] = pd.to_datetime(processed_df['Date'])
-    processed_df['Week'] = processed_df['Date'].dt.isocalendar().week
-    weekly_spending = processed_df.groupby(['Week', 'Category'])['Amount'].sum().reset_index()
-    
-    fig = px.line(weekly_spending, x='Week', y='Amount', color='Category',
-                title="Weekly Spending Trends",
-                labels={'Amount': 'Amount Spent ($)'},
-                color_discrete_sequence=px.colors.qualitative.Bold)
-    fig.update_layout(
-        height=400,
-        plot_bgcolor='rgba(240,247,255,0.8)',
-        xaxis_title="Week",
-        yaxis_title="Amount Spent ($)",
-        font=dict(family="Arial, sans-serif", size=12),
-        margin=dict(l=20, r=20, t=40, b=20),
-    )
-    st.plotly_chart(fig, use_container_width=True)
-    
-    # AI-powered spending controls recommendation
-    st.subheader("AI-Recommended Spending Controls")
-    
-    # Get AI recommendations for spending controls
-    controls_key = f"spending_controls_{date.today().isoformat()}"
-    if controls_key not in st.session_state.insights_cache:
-        controls_recommendations = generate_ai_response("""
-        You are a financial controls expert for startups. Based on the spending patterns and suspicious transactions, 
-        recommend 3-4 specific spending controls that the startup should implement to prevent misuse of funds.
-        
-        For each control, provide:
-        1. A clear policy statement
-        2. Implementation steps
-        3. Expected impact
-        
-        Format as concise, actionable recommendations.
-        """, simulate=True)
-        st.session_state.insights_cache[controls_key] = controls_recommendations
-    
-    st.markdown("<div class='advisor-card'>", unsafe_allow_html=True)
-    st.markdown("<span class='ai-badge'>AI Control Recommendations</span>", unsafe_allow_html=True)
-    st.markdown(f"<p class='advice-text'>{st.session_state.insights_cache[controls_key]}</p>", unsafe_allow_html=True)
-    st.markdown("</div>", unsafe_allow_html=True)
-    
-    # Call-to-action
-    st.info("📅 Need help implementing financial controls? [Book a session](#book-a-session) with our AI financial advisor.")
-
-def detect_suspicious_transactions(transactions_df):
-    """AI-enhanced suspicious transaction detection."""
-    df = transactions_df.copy()
-    
-    # Define thresholds for each category
-    category_thresholds = {
-        "Travel": 3000,
-        "Marketing": 10000,
-        "Office": 7000,
-        "Software": 6000,
-        "Consulting": 5000,
-        "Legal": 6000
-    }
-    
-    # Define suspicious terms
-    suspicious_terms = ['luxury', 'cruise', 'premium', 'personal', 'gift']
-    
-    # Add suspicious column
-    df['Suspicious'] = False
-    df['Reason'] = ""
-    df['Risk_Score'] = 0
-    
-    # Check for suspicious patterns
-    for idx, row in df.iterrows():
-        reasons = []
-        risk_score = 0
-        
-        # Check if amount exceeds category threshold
-        if row['Category'] in category_thresholds:
-            if row['Amount'] > category_thresholds[row['Category']]:
-                reasons.append(f"Amount exceeds typical spending for {row['Category']}")
-                risk_score += 30
-                
-                # Higher risk for significantly exceeding threshold
-                excess_percentage = (row['Amount'] - category_thresholds[row['Category']]) / category_thresholds[row['Category']] * 100
-                if excess_percentage > 100:  # More than double the threshold
-                    risk_score += 20
-        
-        # Check for suspicious vendors or descriptions
-        if any(term in str(row['Vendor']).lower() for term in suspicious_terms):
-            reasons.append(f"Vendor name contains suspicious term")
-            risk_score += 25
-        
-        if any(term in str(row['Description']).lower() for term in suspicious_terms):
-            reasons.append(f"Description contains suspicious term")
-            risk_score += 20
-        
-        # Check for rounded amounts (potential indicator of estimation/fabrication)
-        if row['Amount'] % 1000 == 0 and row['Amount'] > 3000:
-            reasons.append(f"Suspiciously round amount")
-            risk_score += 15
-        
-        # Mark as suspicious if risk score is high enough
-        if risk_score >= 30:
-            df.at[idx, 'Suspicious'] = True
-            df.at[idx, 'Reason'] = "; ".join(reasons)
-            df.at[idx, 'Risk_Score'] = risk_score
-    
-    # Sort by risk score
-    df = df.sort_values(by='Risk_Score', ascending=False)
-    
-    return df
-
-def get_fraud_analysis(transactions_df):
-    """Get AI analysis of potentially fraudulent transactions."""
-    suspicious_df = transactions_df[transactions_df['Suspicious']].copy()
-    
-    if len(suspicious_df) == 0:
-        return "No suspicious transactions detected."
-    
-    transactions_text = suspicious_df[['Date', 'Category', 'Vendor', 'Amount', 'Description', 'Risk_Score']].to_string(index=False)
-    
-    prompt = f"""
-    You are a financial forensics expert specializing in startup spending oversight.
-    Review these flagged transactions:
-
-    {transactions_text}
-
-    Provide a detailed analysis:
-    1. Identify the most concerning transactions and explain why
-    2. Calculate the total financial impact of these suspicious transactions
-    3. Identify spending patterns or potential policy violations
-    4. Recommend specific actions the startup should take immediately
-    5. Suggest controls to prevent similar issues in the future
-
-    Be specific about which transactions are most concerning and why investors would have questions.
-    """
-    
-    return generate_ai_response(prompt)