Input Features (50+ dimensions)
      ↓
Feature Engineering Pipeline
      ↓
┌─────────────────────────────────────────────────┐
│              Ensemble Model                      │
│                                                  │
│  ┌────────────────────────────────────────┐   │
│  │   Model 1: Gradient Boosted Trees       │   │
│  │   (XGBoost)                             │   │
│  │   - Best for: Categorical decisions     │   │
│  │   - Weight: 0.40                        │   │
│  └────────────────────────────────────────┘   │
│                                                  │
│  ┌────────────────────────────────────────┐   │
│  │   Model 2: Deep Neural Network          │   │
│  │   - Architecture: 3 hidden layers       │   │
│  │   - Best for: Non-linear patterns       │   │
│  │   - Weight: 0.35                        │   │
│  └────────────────────────────────────────┘   │
│                                                  │
│  ┌────────────────────────────────────────┐   │
│  │   Model 3: Random Forest                │   │
│  │   - 500 trees, max depth 15             │   │
│  │   - Best for: Feature interactions      │   │
│  │   - Weight: 0.25                        │   │
│  └────────────────────────────────────────┘   │
│                                                  │
└─────────────────────────────────────────────────┘
      ↓
Weighted Voting & Confidence Calculation
      ↓
Output Predictions:
  ├─ Primary Strategy (with probability)
  ├─ Fallback Strategies (ranked)
  ├─ Optimal Processor
  ├─ Expected Success Rate (%)
  ├─ Estimated Time to Recovery (hours)
  └─ Confidence Score (0-1)

import xgboost as xgb

model_xgb = xgb.XGBClassifier(
    objective='multi:softprob',
    num_class=5,  # 5 recovery strategies
    max_depth=10,
    learning_rate=0.1,
    n_estimators=200,
    subsample=0.8,
    colsample_bytree=0.8,
    reg_alpha=0.1,
    reg_lambda=1.0,
    random_state=42
)

import tensorflow as tf
from tensorflow import keras

model_nn = keras.Sequential([
    keras.layers.Dense(128, activation='relu', input_shape=(feature_dim,)),
    keras.layers.Dropout(0.3),
    keras.layers.BatchNormalization(),
    
    keras.layers.Dense(64, activation='relu'),
    keras.layers.Dropout(0.2),
    keras.layers.BatchNormalization(),
    
    keras.layers.Dense(32, activation='relu'),
    keras.layers.Dropout(0.1),
    
    # Multi-output head
    keras.layers.Dense(5, activation='softmax', name='strategy'),
    keras.layers.Dense(1, activation='sigmoid', name='success_probability')
])

model_nn.compile(
    optimizer=keras.optimizers.Adam(learning_rate=0.001),
    loss={
        'strategy': 'categorical_crossentropy',
        'success_probability': 'binary_crossentropy'
    },
    loss_weights={'strategy': 0.6, 'success_probability': 0.4},
    metrics=['accuracy']
)

from sklearn.ensemble import RandomForestClassifier

model_rf = RandomForestClassifier(
    n_estimators=500,
    max_depth=15,
    min_samples_split=10,
    min_samples_leaf=5,
    max_features='sqrt',
    bootstrap=True,
    oob_score=True,
    random_state=42,
    n_jobs=-1
)

interface ModelFeatures {
  // Failure Characteristics (10 features)
  failure_category_encoded: number[];      // One-hot encoded (9 categories)
  failure_severity: number;                // 0-1 score
  processor_specific_code: number;         // Hashed processor code
  time_since_failure: number;              // Hours
  
  // Customer Characteristics (15 features)
  customer_lifetime_value: number;         // Normalized LTV
  customer_risk_score: number;             // 0-1 fraud risk
  customer_tenure_days: number;            // Account age
  total_successful_payments: number;
  total_failed_payments: number;
  customer_success_rate: number;           // Historical %
  avg_payment_amount: number;              // Historical average
  payment_frequency: number;               // Payments per month
  recency_last_payment: number;            // Days since last payment
  preferred_payment_method: number[];      // One-hot encoded
  customer_segment: number;                // Encoded segment (VIP, regular, new)
  
  // Payment Characteristics (12 features)
  payment_amount_normalized: number;       // Log-normalized amount
  amount_to_avg_ratio: number;             // Current / historical avg
  currency_encoded: number[];              // One-hot encoded
  payment_method_type: number[];           // One-hot encoded
  card_bin_reputation: number;             // 0-1 score for card BIN
  card_brand_encoded: number[];            // One-hot encoded
  card_expiry_months_remaining: number;
  is_subscription: boolean;
  subscription_period: number;             // Encoded period
  
  // Contextual Features (8 features)
  hour_of_day: number;                     // 0-23
  day_of_week: number;                     // 0-6
  is_weekend: boolean;
  is_business_hours: boolean;
  merchant_category: number[];             // One-hot encoded
  geographic_region: number[];             // One-hot encoded
  
  // Historical Performance Features (10 features)
  processor_success_rate_category: number; // Historical % for this category
  processor_success_rate_overall: number;  // Overall historical %
  processor_avg_response_time: number;     // Milliseconds
  strategy_success_rate_category: number;  // Historical % for this category
  similar_transactions_success_rate: number; // KNN-based similarity
  failure_category_recovery_rate: number;  // Historical category recovery %
  time_of_day_success_rate: number;        // Historical success by hour
  merchant_recovery_rate: number;          // Merchant-specific historical %
  global_recovery_rate: number;            // Platform-wide baseline
  seasonality_factor: number;              // Seasonal adjustment
}

class FeatureEngineer:
    def __init__(self):
        self.scalers = {}
        self.encoders = {}
        self.feature_stats = {}
    
    def fit_transform(self, raw_data):
        """Transform raw transaction data into model features"""
        features = {}
        
        # 1. Categorical Encoding
        features['failure_category'] = self._one_hot_encode(
            raw_data['failure_category'],
            categories=FAILURE_CATEGORIES
        )
        
        # 2. Numerical Normalization
        features['payment_amount_normalized'] = self._log_normalize(
            raw_data['payment_amount']
        )
        
        # 3. Temporal Features
        features['hour_of_day'] = self._extract_hour(raw_data['timestamp'])
        features['day_of_week'] = self._extract_day_of_week(raw_data['timestamp'])
        features['is_weekend'] = features['day_of_week'].isin([5, 6])
        
        # 4. Customer Aggregations
        features['customer_success_rate'] = self._calculate_historical_rate(
            raw_data['customer_id'],
            success_col='is_successful'
        )
        
        # 5. BIN Reputation Lookup
        features['card_bin_reputation'] = self._lookup_bin_reputation(
            raw_data['card_bin']
        )
        
        # 6. Similarity Features
        features['similar_transactions_success_rate'] = self._knn_similarity(
            raw_data,
            k=50
        )
        
        # 7. Interaction Features
        features['amount_to_avg_ratio'] = (
            raw_data['payment_amount'] / 
            features['avg_payment_amount']
        )
        
        return features
    
    def _one_hot_encode(self, series, categories):
        """One-hot encoding with handling for unknown categories"""
        encoder = OneHotEncoder(
            categories=[categories],
            handle_unknown='ignore',
            sparse=False
        )
        return encoder.fit_transform(series.values.reshape(-1, 1))
    
    def _log_normalize(self, series):
        """Log transformation with standardization"""
        log_values = np.log1p(series)  # log(1 + x) to handle zeros
        return (log_values - log_values.mean()) / log_values.std()
    
    def _calculate_historical_rate(self, customer_ids, success_col):
        """Calculate per-customer historical success rates"""
        # Implementation would query historical database
        pass
    
    def _knn_similarity(self, data, k=50):
        """Find k similar transactions and compute their success rate"""
        # Use KNN on feature space to find similar historical transactions
        pass

interface TrainingDataset {
  // Minimum samples for initial training
  minimumSamples: 50000;
  
  // Minimum samples per class
  minimumPerStrategy: 5000;
  
  // Data collection period
  historicalWindow: '6 months';
  
  // Data balance requirements
  classDistribution: {
    alternative_processor: 0.30,
    delayed_retry: 0.25,
    alternative_payment_method: 0.20,
    installments: 0.15,
    not_recoverable: 0.10
  };
}

class ModelTrainer:
    def __init__(self):
        self.models = {
            'xgboost': None,
            'neural_network': None,
            'random_forest': None
        }
        self.ensemble_weights = [0.40, 0.35, 0.25]
    
    def train_ensemble(self, X_train, y_train, X_val, y_val):
        """Train all models in ensemble"""
        
        # 1. Train XGBoost
        print("Training XGBoost...")
        self.models['xgboost'] = self._train_xgboost(
            X_train, y_train, X_val, y_val
        )
        
        # 2. Train Neural Network
        print("Training Neural Network...")
        self.models['neural_network'] = self._train_neural_network(
            X_train, y_train, X_val, y_val
        )
        
        # 3. Train Random Forest
        print("Training Random Forest...")
        self.models['random_forest'] = self._train_random_forest(
            X_train, y_train, X_val, y_val
        )
        
        # 4. Optimize ensemble weights
        print("Optimizing ensemble weights...")
        self.ensemble_weights = self._optimize_weights(
            X_val, y_val
        )
        
        # 5. Evaluate ensemble
        val_accuracy = self._evaluate_ensemble(X_val, y_val)
        print(f"Validation accuracy: {val_accuracy:.4f}")
        
        return self.models
    
    def _train_xgboost(self, X_train, y_train, X_val, y_val):
        model = xgb.XGBClassifier(
            objective='multi:softprob',
            num_class=5,
            max_depth=10,
            learning_rate=0.1,
            n_estimators=200,
            subsample=0.8,
            colsample_bytree=0.8,
            reg_alpha=0.1,
            reg_lambda=1.0,
            random_state=42
        )
        
        model.fit(
            X_train, y_train,
            eval_set=[(X_val, y_val)],
            early_stopping_rounds=20,
            verbose=10
        )
        
        return model
    
    def _train_neural_network(self, X_train, y_train, X_val, y_val):
        # Convert labels to categorical
        y_train_cat = tf.keras.utils.to_categorical(y_train, num_classes=5)
        y_val_cat = tf.keras.utils.to_categorical(y_val, num_classes=5)
        
        model = self._build_neural_network(X_train.shape[1])
        
        # Training callbacks
        callbacks = [
            keras.callbacks.EarlyStopping(
                monitor='val_loss',
                patience=10,
                restore_best_weights=True
            ),
            keras.callbacks.ReduceLROnPlateau(
                monitor='val_loss',
                factor=0.5,
                patience=5,
                min_lr=1e-6
            )
        ]
        
        model.fit(
            X_train, y_train_cat,
            validation_data=(X_val, y_val_cat),
            epochs=100,
            batch_size=256,
            callbacks=callbacks,
            verbose=1
        )
        
        return model
    
    def _optimize_weights(self, X_val, y_val):
        """Use grid search to find optimal ensemble weights"""
        from scipy.optimize import minimize
        
        def objective(weights):
            predictions = self._ensemble_predict(X_val, weights)
            accuracy = accuracy_score(y_val, predictions)
            return -accuracy  # Minimize negative accuracy
        
        # Constraint: weights sum to 1
        constraints = {'type': 'eq', 'fun': lambda w: np.sum(w) - 1}
        bounds = [(0, 1) for _ in range(3)]
        
        result = minimize(
            objective,
            x0=[0.33, 0.33, 0.34],
            method='SLSQP',
            bounds=bounds,
            constraints=constraints
        )
        
        return result.x

interface TrainingSchedule {
  // Initial training
  initial: {
    data_collection_period: '6 months';
    minimum_samples: 50000;
    training_frequency: 'one-time';
  };
  
  // Ongoing retraining
  retraining: {
    frequency: 'weekly';
    incremental_update: true;
    full_retrain_frequency: 'monthly';
    trigger_conditions: {
      accuracy_drop: 0.05;  // Retrain if accuracy drops 5%
      new_samples_threshold: 10000;  // Retrain after 10k new samples
      distribution_shift: 0.1;  // Retrain if data distribution shifts
    };
  };
  
  // A/B testing
  ab_testing: {
    new_model_rollout: 'gradual';
    initial_traffic_percentage: 0.05;
    ramp_up_duration: '2 weeks';
    success_criteria: {
      accuracy_improvement: 0.02;  // 2% improvement required
      no_performance_degradation: true;
    };
  };
}

class RecoveryPredictor {
  private models: EnsembleModels;
  private featureEngineer: FeatureEngineer;
  
  async predict(
    failureContext: FailureContext,
    customerContext: CustomerContext,
    paymentContext: PaymentContext
  ): Promise<RecoveryPrediction> {
    
    // 1. Feature engineering
    const features = await this.featureEngineer.transform({
      failure: failureContext,
      customer: customerContext,
      payment: paymentContext
    });
    
    // 2. Get predictions from each model
    const xgboostPred = await this.models.xgboost.predict(features);
    const nnPred = await this.models.neuralNetwork.predict(features);
    const rfPred = await this.models.randomForest.predict(features);
    
    // 3. Ensemble predictions
    const ensemblePrediction = this.combinepredictions(
      [xgboostPred, nnPred, rfPred],
      this.models.ensembleWeights
    );
    
    // 4. Post-processing and business logic
    const finalPrediction = this.applyBusinessRules(
      ensemblePrediction,
      failureContext,
      customerContext
    );
    
    return finalPrediction;
  }
  
  private combinepredictions(
    predictions: Prediction[],
    weights: number[]
  ): EnsemblePrediction {
    // Weighted average of probabilities
    const ensembleProbs = predictions[0].probabilities.map((_, idx) => {
      return predictions.reduce((sum, pred, modelIdx) => {
        return sum + pred.probabilities[idx] * weights[modelIdx];
      }, 0);
    });
    
    // Select strategy with highest probability
    const primaryStrategyIdx = this.argmax(ensembleProbs);
    const primaryStrategy = STRATEGIES[primaryStrategyIdx];
    
    // Calculate confidence based on probability distribution
    const confidence = this.calculateConfidence(ensembleProbs);
    
    // Get fallback strategies
    const fallbackStrategies = this.getFallbackStrategies(
      ensembleProbs,
      primaryStrategyIdx
    );
    
    return {
      primaryStrategy,
      probability: ensembleProbs[primaryStrategyIdx],
      confidence,
      fallbackStrategies,
      allProbabilities: ensembleProbs
    };
  }
  
  private applyBusinessRules(
    prediction: EnsemblePrediction,
    failureContext: FailureContext,
    customerContext: CustomerContext
  ): RecoveryPrediction {
    // Override ML prediction if business rules dictate
    
    // Rule 1: Never retry fraud-suspected transactions
    if (failureContext.category === 'fraud_suspected') {
      return {
        strategy: 'not_recoverable',
        reason: 'fraud_risk',
        confidence: 1.0
      };
    }
    
    // Rule 2: High-value customers get priority routing
    if (customerContext.lifetimeValue > 10000) {
      // Use alternative processor first for VIP customers
      if (prediction.primaryStrategy !== 'alternative_processor') {
        return {
          ...prediction,
          primaryStrategy: 'alternative_processor',
          reason: 'vip_customer_priority'
        };
      }
    }
    
    // Rule 3: Low confidence predictions default to safe strategy
    if (prediction.confidence < 0.5) {
      return {
        strategy: 'delayed_retry',
        reason: 'low_confidence_safe_default',
        confidence: prediction.confidence
      };
    }
    
    return prediction;
  }
}

interface InferencePerformance {
  // Latency targets
  p50_latency: '< 50ms';
  p95_latency: '< 100ms';
  p99_latency: '< 200ms';
  
  // Throughput
  requests_per_second: 1000;
  
  // Resource usage
  memory_per_request: '< 10MB';
  cpu_utilization: '< 70%';
  
  // Model size
  xgboost_size: '~50MB';
  neural_network_size: '~30MB';
  random_forest_size: '~100MB';
  total_ensemble_size: '~180MB';
}

class ModelEvaluator:
    def evaluate(self, y_true, y_pred, y_pred_proba):
        """Comprehensive model evaluation"""
        
        metrics = {
            # Classification metrics
            'accuracy': accuracy_score(y_true, y_pred),
            'precision': precision_score(y_true, y_pred, average='weighted'),
            'recall': recall_score(y_true, y_pred, average='weighted'),
            'f1_score': f1_score(y_true, y_pred, average='weighted'),
            
            # Multi-class metrics
            'confusion_matrix': confusion_matrix(y_true, y_pred),
            'classification_report': classification_report(y_true, y_pred),
            
            # Probability calibration
            'log_loss': log_loss(y_true, y_pred_proba),
            'brier_score': brier_score_loss(y_true, y_pred_proba),
            
            # Business metrics
            'recovery_rate': self._calculate_recovery_rate(y_true, y_pred),
            'revenue_impact': self._calculate_revenue_impact(y_true, y_pred),
            'time_to_recovery': self._calculate_avg_time_to_recovery(y_pred),
            
            # Per-strategy metrics
            'strategy_metrics': self._per_strategy_metrics(y_true, y_pred)
        }
        
        return metrics
    
    def _calculate_recovery_rate(self, y_true, y_pred):
        """Calculate actual recovery rate from predictions"""
        # Predictions that led to successful recovery
        successful = (y_pred != 'not_recoverable') & (y_true == 'recovered')
        return successful.sum() / len(y_true)
    
    def _calculate_revenue_impact(self, y_true, y_pred):
        """Calculate revenue recovered vs. potential"""
        # Would need transaction amounts to calculate actual $$$
        pass

interface ModelMonitoring {
  // Real-time metrics
  realtime: {
    prediction_latency: TimeSeries;
    prediction_distribution: Distribution;
    confidence_scores: Distribution;
    error_rate: number;
  };
  
  // Model performance tracking
  performance: {
    daily_accuracy: TimeSeries;
    strategy_success_rates: Map<string, TimeSeries>;
    calibration_error: TimeSeries;
    feature_drift: Map<string, number>;
  };
  
  // Alerts
  alerts: {
    accuracy_drop: {
      threshold: 0.05;  // Alert if drops 5%
      window: '24 hours';
    };
    latency_spike: {
      threshold: 200;  // Alert if p95 > 200ms
      window: '5 minutes';
    };
    feature_drift: {
      threshold: 0.15;  // Alert if feature distribution shifts 15%
      window: '7 days';
    };
    prediction_bias: {
      threshold: 0.10;  // Alert if bias towards one strategy
      window: '24 hours';
    };
  };
}

class FeedbackCollector {
  async recordOutcome(
    recoveryId: string,
    prediction: RecoveryPrediction,
    actualOutcome: RecoveryOutcome
  ): Promise<void> {
    // Store prediction and outcome for model retraining
    await this.database.insert('model_feedback', {
      recovery_id: recoveryId,
      prediction: {
        strategy: prediction.strategy,
        probability: prediction.probability,
        confidence: prediction.confidence,
        model_version: prediction.modelVersion
      },
      outcome: {
        success: actualOutcome.success,
        strategy_used: actualOutcome.strategyUsed,
        time_to_recovery: actualOutcome.timeToRecovery,
        revenue_recovered: actualOutcome.revenueRecovered
      },
      features: prediction.features,
      timestamp: new Date()
    });
    
    // Update real-time metrics
    await this.metricsTracker.updateAccuracy(
      prediction.strategy === actualOutcome.strategyUsed
    );
  }
  
  async collectRetrainingData(): Promise<TrainingDataset> {
    // Collect feedback since last training
    const feedback = await this.database.query(`
      SELECT * FROM model_feedback
      WHERE timestamp > :last_training_date
      AND outcome IS NOT NULL
    `);
    
    return this.prepareRetrainingDataset(feedback);
  }
}

interface OnlineLearningStrategy {
  // Incremental updates
  incremental: {
    enabled: true;
    update_frequency: 'daily';
    min_samples_per_update: 1000;
    learning_rate_decay: 0.95;
  };
  
  // Model versioning
  versioning: {
    maintain_versions: 5;
    rollback_enabled: true;
    champion_challenger: {
      enabled: true;
      challenger_traffic: 0.10;
      evaluation_period: '1 week';
    };
  };
  
  // Adaptive learning
  adaptive: {
    merchant_specific_models: true;
    regional_adaptations: true;
    seasonal_adjustments: true;
  };
}

import shap

class ModelExplainer:
    def __init__(self, models):
        self.models = models
        self.explainers = {
            'xgboost': shap.TreeExplainer(models['xgboost']),
            'neural_network': shap.DeepExplainer(models['neural_network']),
            'random_forest': shap.TreeExplainer(models['random_forest'])
        }
    
    def explain_prediction(self, features, prediction):
        """Generate SHAP explanation for a single prediction"""
        
        # Get SHAP values from each model
        shap_values = {}
        for model_name, explainer in self.explainers.items():
            shap_values[model_name] = explainer.shap_values(features)
        
        # Combine SHAP values using ensemble weights
        ensemble_shap = self._combine_shap_values(shap_values)
        
        # Get top contributing features
        top_features = self._get_top_features(ensemble_shap, n=10)
        
        return {
            'prediction': prediction,
            'feature_contributions': top_features,
            'shap_values': ensemble_shap,
            'explanation_text': self._generate_explanation_text(top_features)
        }
    
    def _generate_explanation_text(self, top_features):
        """Generate human-readable explanation"""
        explanations = []
        
        for feature, contribution in top_features:
            if contribution > 0:
                explanations.append(
                    f"{feature} increased the likelihood of this strategy by {contribution:.1%}"
                )
            else:
                explanations.append(
                    f"{feature} decreased the likelihood of this strategy by {abs(contribution):.1%}"
                )
        
        return "\n".join(explanations)

interface PredictionExplanation {
  // High-level explanation
  summary: string;
  
  // Feature contributions
  topFeatures: Array<{
    name: string;
    value: number;
    contribution: number;
    description: string;
  }>;
  
  // Strategy reasoning
  reasoning: {
    whyRecommended: string;
    alternativeStrategies: Array<{
      strategy: string;
      probability: number;
      reason: string;
    }>;
  };
  
  // Confidence factors
  confidenceFactors: {
    modelAgreement: number;  // How much models agree
    historicalAccuracy: number;  // Model accuracy on similar cases
    dataQuality: number;  // Completeness of input data
  };
}

class FairnessValidator:
    def validate_fairness(self, model, test_data):
        """Ensure model doesn't discriminate based on protected attributes"""
        
        metrics = {}
        
        # Test for demographic parity
        metrics['demographic_parity'] = self._test_demographic_parity(
            model, test_data,
            protected_attributes=['geographic_region', 'customer_segment']
        )
        
        # Test for equal opportunity
        metrics['equal_opportunity'] = self._test_equal_opportunity(
            model, test_data,
            protected_attributes=['geographic_region', 'customer_segment']
        )
        
        # Test for calibration across groups
        metrics['calibration'] = self._test_calibration_across_groups(
            model, test_data,
            protected_attributes=['geographic_region', 'customer_segment']
        )
        
        return metrics
    
    def _test_demographic_parity(self, model, data, protected_attributes):
        """Ensure similar prediction rates across demographic groups"""
        results = {}
        
        for attr in protected_attributes:
            groups = data[attr].unique()
            prediction_rates = {}
            
            for group in groups:
                group_data = data[data[attr] == group]
                predictions = model.predict(group_data)
                prediction_rates[group] = (predictions == 'recovered').mean()
            
            # Calculate disparity
            max_rate = max(prediction_rates.values())
            min_rate = min(prediction_rates.values())
            disparity = (max_rate - min_rate) / max_rate
            
            results[attr] = {
                'rates': prediction_rates,
                'disparity': disparity,
                'passes': disparity < 0.15  # Max 15% disparity allowed
            }
        
        return results

interface BiasMitigation {
  // Pre-processing
  preprocessing: {
    balanced_sampling: boolean;
    protected_attribute_removal: string[];
    fairness_aware_encoding: boolean;
  };
  
  // In-processing
  inprocessing: {
    fairness_constraints: boolean;
    adversarial_debiasing: boolean;
    prejudice_remover: boolean;
  };
  
  // Post-processing
  postprocessing: {
    threshold_optimization: boolean;
    calibration_adjustment: boolean;
    reject_option_classification: boolean;
  };
}

interface ProcessorScore {
  processorId: string;
  score: number;
  factors: {
    historicalSuccessRate: number;
    cardBINCompatibility: number;
    geographicOptimization: number;
    costEfficiency: number;
    responseTime: number;
  };
}

class ProcessorSelector {
  async selectOptimalProcessor(
    failureContext: FailureContext,
    customerContext: CustomerContext,
    availableProcessors: Processor[]
  ): Promise<ProcessorScore[]> {
    const scores: ProcessorScore[] = [];
    
    for (const processor of availableProcessors) {
      const score = await this.calculateProcessorScore(
        processor,
        failureContext,
        customerContext
      );
      scores.push(score);
    }
    
    // Sort by score descending
    return scores.sort((a, b) => b.score - a.score);
  }
  
  private async calculateProcessorScore(
    processor: Processor,
    failureContext: FailureContext,
    customerContext: CustomerContext
  ): Promise<ProcessorScore> {
    // Fetch historical data
    const historicalSuccessRate = await this.getProcessorSuccessRate(
      processor.id,
      failureContext.failureCategory,
      customerContext.customerSegment
    );
    
    // Check BIN routing tables
    const cardBINCompatibility = await this.checkBINCompatibility(
      processor.id,
      failureContext.cardBIN
    );
    
    // Geographic optimization
    const geographicOptimization = this.calculateGeoScore(
      processor.supportedRegions,
      customerContext.country
    );
    
    // Cost analysis
    const costEfficiency = this.calculateCostScore(
      processor.fees,
      failureContext.amount
    );
    
    // Performance metrics
    const responseTime = await this.getAverageResponseTime(processor.id);
    
    // Weighted scoring
    const score = (
      historicalSuccessRate * 0.40 +
      cardBINCompatibility * 0.25 +
      geographicOptimization * 0.15 +
      costEfficiency * 0.10 +
      responseTime * 0.10
    );
    
    return {
      processorId: processor.id,
      score,
      factors: {
        historicalSuccessRate,
        cardBINCompatibility,
        geographicOptimization,
        costEfficiency,
        responseTime
      }
    };
  }
}

class AlternativeProcessorStrategy implements RecoveryStrategy {
  async execute(context: RecoveryContext): Promise<RecoveryResult> {
    // 1. Select optimal alternative processor
    const processors = await this.processorSelector.selectOptimalProcessor(
      context.failure,
      context.customer,
      context.availableProcessors.filter(p => p.id !== context.originalProcessor)
    );
    
    if (processors.length === 0) {
      return { success: false, reason: 'No alternative processors available' };
    }
    
    // 2. Attempt payment with top 3 processors in sequence
    for (const processor of processors.slice(0, 3)) {
      try {
        const result = await this.attemptPayment(
          processor.processorId,
          context.payment,
          context.customer
        );
        
        if (result.success) {
          // Track success for ML model
          await this.trackStrategySuccess(processor.processorId, context);
          
          return {
            success: true,
            transactionId: result.transactionId,
            processor: processor.processorId,
            strategy: 'alternative_processor'
          };
        }
      } catch (error) {
        // Log failure and try next processor
        await this.trackStrategyFailure(processor.processorId, context, error);
        continue;
      }
    }
    
    return { success: false, reason: 'All alternative processors failed' };
  }
}

class DelayedRetryStrategy implements RecoveryStrategy {
  async execute(context: RecoveryContext): Promise<RecoveryResult> {
    // Calculate optimal retry time based on failure type
    const retrySchedule = this.calculateRetrySchedule(context.failure);
    
    // Schedule retry attempts
    for (const retryTime of retrySchedule) {
      await this.scheduleRetry(context.recoveryId, retryTime);
      
      // Send customer notification
      await this.notificationService.sendRetryNotification(
        context.customer,
        retryTime,
        context.payment.amount
      );
    }
    
    return {
      success: true,
      strategy: 'delayed_retry',
      nextAttemptAt: retrySchedule[0]
    };
  }
  
  private calculateRetrySchedule(failure: FailureContext): Date[] {
    const schedule: Date[] = [];
    const now = new Date();
    
    switch (failure.category) {
      case FailureCategory.INSUFFICIENT_FUNDS:
        // Retry after typical pay cycles
        schedule.push(addDays(now, 3));  // Next pay cycle
        schedule.push(addDays(now, 7));  // Bi-weekly
        schedule.push(addDays(now, 15)); // Monthly
        break;
        
      case FailureCategory.PROCESSING_ERROR:
        // Quick retries for transient errors
        schedule.push(addMinutes(now, 15));
        schedule.push(addHours(now, 1));
        schedule.push(addHours(now, 6));
        break;
        
      case FailureCategory.DO_NOT_HONOR:
        // Medium-term retries
        schedule.push(addDays(now, 1));
        schedule.push(addDays(now, 3));
        schedule.push(addDays(now, 7));
        break;
        
      default:
        schedule.push(addHours(now, 24));
        schedule.push(addDays(now, 3));
        break;
    }
    
    return schedule;
  }
}

class AlternativePaymentMethodStrategy implements RecoveryStrategy {
  async execute(context: RecoveryContext): Promise<RecoveryResult> {
    // Generate recovery UI for customer
    const recoverySession = await this.createRecoverySession(context);
    
    // Determine alternative payment methods to offer
    const alternativeMethods = this.selectAlternativeMethods(
      context.payment.paymentMethod.type,
      context.customer.country
    );
    
    // Send notification to customer
    await this.notificationService.sendPaymentMethodUpdateRequest(
      context.customer,
      recoverySession.url,
      alternativeMethods
    );
    
    return {
      success: true,
      strategy: 'alternative_payment_method',
      recoveryUrl: recoverySession.url,
      status: 'customer_action_required'
    };
  }
  
  private selectAlternativeMethods(
    failedMethod: PaymentMethodType,
    country: string
  ): PaymentMethodType[] {
    const alternatives: PaymentMethodType[] = [];
    
    // Always offer these as alternatives
    if (failedMethod !== 'bank_account') {
      alternatives.push('bank_account');
    }
    if (failedMethod !== 'card') {
      alternatives.push('card');
    }
    
    // Region-specific alternatives
    switch (country) {
      case 'US':
        alternatives.push('venmo', 'cashapp', 'paypal');
        break;
      case 'GB':
        alternatives.push('open_banking', 'paypal');
        break;
      case 'DE':
        alternatives.push('sofort', 'giropay', 'paypal');
        break;
      default:
        alternatives.push('paypal');
    }
    
    return alternatives;
  }
}

class InstallmentStrategy implements RecoveryStrategy {
  async execute(context: RecoveryContext): Promise<RecoveryResult> {
    // Only offer installments for insufficient funds failures
    if (context.failure.category !== FailureCategory.INSUFFICIENT_FUNDS) {
      return { success: false, reason: 'Installments not applicable for this failure type' };
    }
    
    // Calculate installment plans
    const plans = this.calculateInstallmentPlans(context.payment.amount.value);
    
    // Create recovery session with installment options
    const recoverySession = await this.createInstallmentSession(
      context,
      plans
    );
    
    // Notify customer
    await this.notificationService.sendInstallmentOffer(
      context.customer,
      recoverySession.url,
      plans
    );
    
    return {
      success: true,
      strategy: 'installments',
      recoveryUrl: recoverySession.url,
      status: 'customer_action_required'
    };
  }
  
  private calculateInstallmentPlans(amount: number): InstallmentPlan[] {
    const plans: InstallmentPlan[] = [];
    
    // Minimum $50 per installment
    const minInstallmentAmount = 5000; // cents
    
    if (amount >= minInstallmentAmount * 2) {
      plans.push({
        numberOfPayments: 2,
        paymentAmount: Math.ceil(amount / 2),
        frequency: 'biweekly'
      });
    }
    
    if (amount >= minInstallmentAmount * 3) {
      plans.push({
        numberOfPayments: 3,
        paymentAmount: Math.ceil(amount / 3),
        frequency: 'monthly'
      });
    }
    
    if (amount >= minInstallmentAmount * 4) {
      plans.push({
        numberOfPayments: 4,
        paymentAmount: Math.ceil(amount / 4),
        frequency: 'monthly'
      });
    }
    
    return plans;
  }
}

<!DOCTYPE html>
<html>
<head>
  <style>
    /* Responsive email template */
  </style>
</head>
<body>
  <div class="container">
    <img src="{{merchant_logo}}" alt="{{merchant_name}}" />
    
    <h1>Payment Issue - Easy Fix Available</h1>
    
    <p>Hi {{customer_first_name}},</p>
    
    <p>We noticed your recent payment of <strong>{{amount}}</strong> for 
       <strong>{{order_description}}</strong> couldn't be processed.</p>
    
    <div class="issue-box">
      <strong>Issue:</strong> {{failure_message}}
    </div>
    
    <p>Good news! We have several easy ways to complete your purchase:</p>
    
    <div class="options">
      {{#if show_retry}}
      <div class="option">
        <h3>🔄 Retry Payment</h3>
        <p>We'll automatically retry your payment on {{retry_date}}</p>
      </div>
      {{/if}}
      
      {{#if show_alternative_method}}
      <div class="option">
        <h3>💳 Use Different Payment Method</h3>
        <p>Add a different card, bank account, or digital wallet</p>
      </div>
      {{/if}}
      
      {{#if show_installments}}
      <div class="option">
        <h3>📅 Pay in Installments</h3>
        <p>Split your payment into {{installment_count}} smaller payments</p>
      </div>
      {{/if}}
    </div>
    
    <a href="{{recovery_url}}" class="cta-button">Complete Your Purchase</a>
    
    <p class="footer">
      This link expires in {{expiry_hours}} hours. 
      <a href="{{contact_url}}">Need help?</a>
    </p>
  </div>
</body>
</html>

interface RecoveryUIConfig {
  merchantBranding: {
    logo: string;
    primaryColor: string;
    fontFamily?: string;
  };
  paymentDetails: {
    amount: Money;
    description: string;
    orderId: string;
  };
  availableOptions: RecoveryOption[];
  customerInfo: {
    name: string;
    email: string;
  };
  expiresAt: Date;
}

interface RecoveryOption {
  type: 'retry' | 'alternative_method' | 'installments';
  title: string;
  description: string;
  icon: string;
  recommended?: boolean;
  metadata?: {
    retryDate?: Date;
    availableMethods?: PaymentMethodType[];
    installmentPlans?: InstallmentPlan[];
  };
}

{{merchant_name}}: Payment issue for order #{{order_id}}. Complete your ${{amount}} purchase here: {{short_url}}

Options available:
- Retry payment
- Different payment method  
- Pay in installments

Link expires in {{expiry_hours}}h.

-- Recovery sessions
CREATE TABLE recovery_sessions (
  id VARCHAR(36) PRIMARY KEY,
  merchant_id VARCHAR(36) NOT NULL,
  merchant_order_id VARCHAR(255) NOT NULL,
  customer_id VARCHAR(36) NOT NULL,
  status VARCHAR(50) NOT NULL,
  created_at TIMESTAMP NOT NULL,
  updated_at TIMESTAMP NOT NULL,
  expires_at TIMESTAMP NOT NULL,
  completed_at TIMESTAMP,
  
  -- Original payment details
  original_amount INTEGER NOT NULL,
  original_currency VARCHAR(3) NOT NULL,
  original_processor VARCHAR(100) NOT NULL,
  original_payment_method_type VARCHAR(50) NOT NULL,
  
  -- Failure details
  failure_category VARCHAR(50) NOT NULL,
  failure_code VARCHAR(100) NOT NULL,
  failure_message TEXT,
  failure_timestamp TIMESTAMP NOT NULL,
  
  -- Recovery details
  current_strategy VARCHAR(50),
  strategy_confidence DECIMAL(3,2),
  total_attempts INTEGER DEFAULT 0,
  recovery_url TEXT,
  
  -- Result
  recovered_amount INTEGER,
  recovered_at TIMESTAMP,
  final_strategy VARCHAR(50),
  final_processor VARCHAR(100),
  final_transaction_id VARCHAR(255),
  
  INDEX idx_merchant_order (merchant_id, merchant_order_id),
  INDEX idx_customer (customer_id),
  INDEX idx_status (status),
  INDEX idx_created_at (created_at)
);

-- Recovery attempts
CREATE TABLE recovery_attempts (
  id VARCHAR(36) PRIMARY KEY,
  recovery_id VARCHAR(36) NOT NULL,
  attempt_number INTEGER NOT NULL,
  strategy VARCHAR(50) NOT NULL,
  processor VARCHAR(100),
  status VARCHAR(50) NOT NULL,
  started_at TIMESTAMP NOT NULL,
  completed_at TIMESTAMP,
  
  -- Result
  success BOOLEAN,
  transaction_id VARCHAR(255),
  failure_reason TEXT,
  failure_code VARCHAR(100),
  
  -- Performance metrics
  processing_time_ms INTEGER,
  
  FOREIGN KEY (recovery_id) REFERENCES recovery_sessions(id),
  INDEX idx_recovery (recovery_id),
  INDEX idx_strategy (strategy),
  INDEX idx_processor (processor)
);

-- Customer interactions
CREATE TABLE recovery_interactions (
  id VARCHAR(36) PRIMARY KEY,
  recovery_id VARCHAR(36) NOT NULL,
  interaction_type VARCHAR(50) NOT NULL,
  timestamp TIMESTAMP NOT NULL,
  
  -- Context
  device_type VARCHAR(50),
  browser VARCHAR(100),
  location VARCHAR(100),
  
  -- Details
  details JSONB,
  
  FOREIGN KEY (recovery_id) REFERENCES recovery_sessions(id),
  INDEX idx_recovery (recovery_id),
  INDEX idx_type (interaction_type)
);

-- Processor performance metrics
CREATE TABLE processor_performance (
  id VARCHAR(36) PRIMARY KEY,
  processor VARCHAR(100) NOT NULL,
  failure_category VARCHAR(50) NOT NULL,
  
  -- Time period
  date DATE NOT NULL,
  hour INTEGER,
  
  -- Metrics
  total_attempts INTEGER DEFAULT 0,
  successful_attempts INTEGER DEFAULT 0,
  failed_attempts INTEGER DEFAULT 0,
  success_rate DECIMAL(5,4),
  avg_processing_time_ms INTEGER,
  
  -- Amount metrics
  total_amount INTEGER DEFAULT 0,
  recovered_amount INTEGER DEFAULT 0,
  
  INDEX idx_processor_date (processor, date),
  INDEX idx_category (failure_category)
);

interface RecoveryMetrics {
  // Overall performance
  totalRecoveries: number;
  successfulRecoveries: number;
  failedRecoveries: number;
  overallSuccessRate: number;
  
  // Amount metrics
  totalAttemptedAmount: Money;
  totalRecoveredAmount: Money;
  recoveryRate: number;
  
  // Time metrics
  averageTimeToRecovery: number; // hours
  medianTimeToRecovery: number;
  
  // Strategy performance
  strategyBreakdown: {
    [strategy: string]: {
      attempts: number;
      successes: number;
      successRate: number;
      avgTimeToRecovery: number;
    };
  };
  
  // Processor performance
  processorBreakdown: {
    [processor: string]: {
      attempts: number;
      successes: number;
      successRate: number;
      avgProcessingTime: number;
    };
  };
  
  // Failure category analysis
  failureCategoryBreakdown: {
    [category: string]: {
      total: number;
      recovered: number;
      recoveryRate: number;
    };
  };
  
  // Customer behavior
  customerInteractionRate: number;
  averageTimeToInteraction: number;
  customerDropoffRate: number;
}

class RecoveryAnalytics {
  async getRecoveryMetrics(
    merchantId: string,
    startDate: Date,
    endDate: Date
  ): Promise<RecoveryMetrics> {
    // Implementation would query database and calculate metrics
  }
  
  async getStrategyEffectiveness(
    strategy: string,
    failureCategory: FailureCategory
  ): Promise<StrategyEffectiveness> {
    // Analyze which strategies work best for which failure types
  }
  
  async getProcessorRecommendations(
    merchantId: string,
    failureCategory: FailureCategory
  ): Promise<ProcessorRecommendation[]> {
    // Return ranked list of processors for specific failure types
  }
  
  async predictRecoveryProbability(
    failureContext: FailureContext,
    customerContext: CustomerContext
  ): Promise<number> {
    // ML model prediction of recovery success
    // See Payment_Recovery_ML_Model_Spec.md for details
  }
}

interface SecurityControls {
  authentication: {
    type: 'api_key' | 'oauth2';
    keyRotation: number; // days
    rateLimiting: {
      requestsPerMinute: number;
      burstLimit: number;
    };
  };
  
  webhookSecurity: {
    signatureValidation: boolean;
    secretRotation: number; // days
    ipWhitelisting?: string[];
  };
  
  dataAccess: {
    encryptionAtRest: boolean;
    encryptionInTransit: boolean;
    dataRetention: number; // days
    autoRedaction: boolean;
  };
}

interface PricingTier {
  name: string;
  monthlyFee: number;
  successFee: {
    percentage: number;
    perTransaction: number;
  };
  limits: {
    monthlyRecoveries: number;
    apiCalls: number;
  };
  features: string[];
}

const pricingTiers: PricingTier[] = [
  {
    name: 'Starter',
    monthlyFee: 0,
    successFee: {
      percentage: 5.0, // 5% of recovered amount
      perTransaction: 50 // $0.50
    },
    limits: {
      monthlyRecoveries: 100,
      apiCalls: 10000
    },
    features: [
      'Basic processor routing',
      'Email notifications',
      'Standard recovery UI',
      'Basic analytics'
    ]
  },
  {
    name: 'Growth',
    monthlyFee: 299,
    successFee: {
      percentage: 3.5,
      perTransaction: 50
    },
    limits: {
      monthlyRecoveries: 1000,
      apiCalls: 100000
    },
    features: [
      'ML-powered routing',
      'SMS + Email notifications',
      'Custom recovery flows',
      'Advanced analytics',
      'Installment plans',
      'Priority support'
    ]
  },
  {
    name: 'Enterprise',
    monthlyFee: 'Custom',
    successFee: {
      percentage: 2.5,
      perTransaction: 50
    },
    limits: {
      monthlyRecoveries: -1, // unlimited
      apiCalls: -1
    },
    features: [
      'Everything in Growth',
      'Custom ML model training',
      'White-label solutions',
      'Dedicated support',
      'Custom SLA',
      'Multi-merchant management'
    ]
  }
];

function calculateROI(
  monthlyFailedPayments: number,
  averagePaymentAmount: number,
  currentRecoveryRate: number = 0
): ROIAnalysis {
  const handsInRecoveryRate = 0.30; // 30% average
  const additionalRecoveries = monthlyFailedPayments * (handsInRecoveryRate - currentRecoveryRate);
  const additionalRevenue = additionalRecoveries * averagePaymentAmount;
  const serviceFee = additionalRevenue * 0.035; // 3.5% for Growth tier
  const netGain = additionalRevenue - serviceFee;
  
  return {
    additionalRecoveries: Math.round(additionalRecoveries),
    additionalRevenue: Math.round(additionalRevenue),
    serviceFee: Math.round(serviceFee),
    netGain: Math.round(netGain),
    roi: Math.round((netGain / serviceFee) * 100)
  };
}

// Example: 
// 1000 failed payments/month
// $150 average payment
// 0% current recovery
// = 300 additional recoveries
// = $45,000 additional revenue
// = $1,575 service fee
// = $43,425 net gain
// = 2,756% ROI

interface ProductKPIs {
  // Core metrics
  recoveryRate: number; // % of submitted failures that recover
  revenueRecovered: Money; // total $ recovered
  averageTimeToRecovery: number; // hours
  
  // Strategy metrics
  strategySuccessRates: Map<string, number>;
  strategyUsageDistribution: Map<string, number>;
  
  // Processor metrics
  processorSuccessRates: Map<string, number>;
  processorUtilization: Map<string, number>;
  
  // Customer experience
  customerInteractionRate: number; // % who engage with recovery flow
  customerSatisfaction: number; // CSAT score
  completionTime: number; // time from view to completion
  
  // Technical performance
  apiLatency: {
    p50: number;
    p95: number;
    p99: number;
  };
  uptime: number; // %
  errorRate: number; // %
  
  // Business metrics
  activemerchants: number;
  revenuePerMerchant: Money;
  merchantRetention: number; // %
  nps: number; // Net Promoter Score
}