AI-Powered Risk Assessment for Financial Services

# Confer's Advanced Default Risk Model Implementation

import numpy as np
import pandas as pd
import tensorflow as tf
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, BatchNormalization
from tensorflow.keras.callbacks import EarlyStopping
from typing import Dict, List, Any, Tuple

class MortgageDefaultRiskModel:
    """Advanced neural network model for mortgage default prediction."""
    
    def __init__(
        self,
        model_path: str = None,
        feature_importance_threshold: float = 0.01,
        confidence_threshold: float = 0.75,
        use_alternative_data: bool = True
    ):
        self.model_path = model_path
        self.feature_importance_threshold = feature_importance_threshold
        self.confidence_threshold = confidence_threshold
        self.use_alternative_data = use_alternative_data
        self.scaler = StandardScaler()
        self.model = None
        self.feature_names = []
        self.feature_importances = {}
        
        if model_path:
            self.load_model(model_path)
        else:
            self._build_model()
        
    def _build_model(self):
        """Build the neural network architecture."""
        model = Sequential([
            Dense(256, activation='relu', input_shape=(self.input_dim,)),
            BatchNormalization(),
            Dropout(0.3),
            Dense(128, activation='relu'),
            BatchNormalization(),
            Dropout(0.2),
            Dense(64, activation='relu'),
            BatchNormalization(),
            Dropout(0.1),
            Dense(32, activation='relu'),
            Dense(1, activation='sigmoid')
        ])
        
        model.compile(
            optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
            loss='binary_crossentropy',
            metrics=['accuracy', tf.keras.metrics.AUC(), tf.keras.metrics.Precision(), tf.keras.metrics.Recall()]
        )
        
        self.model = model
        
    def train(self, X_train: pd.DataFrame, y_train: pd.Series, validation_split: float = 0.2):
        """Train the model on mortgage loan data."""
        self.feature_names = list(X_train.columns)
        self.input_dim = X_train.shape[1]
        
        if not self.model:
            self._build_model()
            
        # Scale features
        X_scaled = self.scaler.fit_transform(X_train)
        
        # Train model with early stopping
        early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)
        
        history = self.model.fit(
            X_scaled, y_train,
            epochs=100,
            batch_size=64,
            validation_split=validation_split,
            callbacks=[early_stopping],
            verbose=1
        )
        
        # Calculate feature importances using permutation importance
        self._calculate_feature_importance(X_scaled, y_train)
        
        return history
    
    def predict(self, X: pd.DataFrame) -> Dict[str, Any]:
        """Predict default probability and provide risk factors."""
        X_scaled = self.scaler.transform(X)
        
        # Get raw predictions
        default_probs = self.model.predict(X_scaled)
        
        # Calculate confidence scores
        confidence_scores = self._calculate_confidence(X_scaled)
        
        # Identify risk factors
        risk_factors = self._identify_risk_factors(X)
        
        # Prepare results
        results = {
            'default_probability': default_probs.flatten(),
            'confidence_score': confidence_scores,
            'risk_factors': risk_factors,
            'high_risk': default_probs.flatten() > self.confidence_threshold
        }
        
        return results
    
    def _calculate_confidence(self, X_scaled: np.ndarray) -> np.ndarray:
        """Calculate confidence scores for predictions."""
        # Implementation of Monte Carlo Dropout for uncertainty estimation
        # Run multiple forward passes with dropout enabled
        mc_samples = 50
        predictions = np.zeros((mc_samples, X_scaled.shape[0]))
        
        for i in range(mc_samples):
            predictions[i, :] = self.model(X_scaled, training=True).numpy().flatten()
        
        # Calculate mean and standard deviation
        mean_pred = np.mean(predictions, axis=0)
        std_pred = np.std(predictions, axis=0)
        
        # Convert to confidence score (inverse of normalized std)
        confidence = 1 - (std_pred / (mean_pred + 1e-7))
        
        return confidence
    
    def _identify_risk_factors(self, X: pd.DataFrame) -> List[Dict[str, Any]]:
        """Identify key risk factors contributing to default risk."""
        risk_factors = []
        
        # Calculate SHAP values or other feature attribution method
        # ...
        
        return risk_factors
    
    def _calculate_feature_importance(self, X: np.ndarray, y: np.ndarray):
        """Calculate feature importance using permutation importance."""
        # Implementation of permutation importance
        # ...
        
    def save_model(self, path: str):
        """Save model and preprocessing components."""
        self.model.save(path)
        # Save scaler and other components
        # ...
        
    def load_model(self, path: str):
        """Load model and preprocessing components."""
        self.model = tf.keras.models.load_model(path)
        # Load scaler and other components
        # ...