Chapter 10: RegTech & Compliance Solutions

# Example: Comprehensive Capital Analysis and Review (CCAR) Platform
import pandas as pd
import numpy as np
from scipy import stats
import asyncio
from datetime import datetime, timedelta
import logging

class CCARStressTesting:
    def __init__(self):
        self.scenarios = {
            'baseline': {'gdp_growth': 0.025, 'unemployment': 0.045, 'interest_rates': 0.02},
            'adverse': {'gdp_growth': -0.01, 'unemployment': 0.08, 'interest_rates': 0.015},
            'severely_adverse': {'gdp_growth': -0.03, 'unemployment': 0.12, 'interest_rates': 0.01}
        }
        self.portfolios = {}
        self.capital_ratios = {}

    def load_portfolio_data(self, data_sources):
        """Load and consolidate portfolio data from multiple sources"""
        consolidated_data = {}

        for source_name, source_config in data_sources.items():
            try:
                if source_config['type'] == 'database':
                    data = self._extract_from_database(source_config)
                elif source_config['type'] == 'api':
                    data = self._extract_from_api(source_config)
                elif source_config['type'] == 'file':
                    data = self._extract_from_file(source_config)

                consolidated_data[source_name] = data
                logging.info(f"Loaded {len(data)} records from {source_name}")

            except Exception as e:
                logging.error(f"Failed to load data from {source_name}: {str(e)}")

        return self._reconcile_portfolio_data(consolidated_data)

    def run_stress_scenarios(self, portfolio_data, time_horizon=9):
        """Execute stress testing scenarios over specified time horizon"""
        results = {}

        for scenario_name, scenario_params in self.scenarios.items():
            logging.info(f"Running {scenario_name} scenario")

            scenario_results = {
                'credit_losses': [],
                'pre_provision_net_revenue': [],
                'trading_losses': [],
                'capital_ratios': []
            }

            # Project portfolio performance under stress
            for quarter in range(time_horizon):
                # Credit loss projections
                credit_losses = self._project_credit_losses(
                    portfolio_data, scenario_params, quarter
                )
                scenario_results['credit_losses'].append(credit_losses)

                # Revenue projections
                ppnr = self._project_pre_provision_revenue(
                    portfolio_data, scenario_params, quarter
                )
                scenario_results['pre_provision_net_revenue'].append(ppnr)

                # Trading and market risk losses
                trading_losses = self._project_trading_losses(
                    portfolio_data, scenario_params, quarter
                )
                scenario_results['trading_losses'].append(trading_losses)

                # Calculate capital ratios
                capital_ratio = self._calculate_capital_ratio(
                    scenario_results, quarter
                )
                scenario_results['capital_ratios'].append(capital_ratio)

            results[scenario_name] = scenario_results

        return results

    def _project_credit_losses(self, portfolio_data, scenario_params, quarter):
        """Project credit losses based on macroeconomic scenario"""
        total_losses = 0

        for segment, loans in portfolio_data.items():
            segment_losses = 0

            # Apply scenario-specific loss rates
            base_loss_rate = loans['historical_loss_rate']
            stress_multiplier = self._calculate_stress_multiplier(
                scenario_params, segment, quarter
            )

            stressed_loss_rate = base_loss_rate * stress_multiplier
            segment_losses = loans['outstanding_balance'] * stressed_loss_rate

            total_losses += segment_losses

        return total_losses

    def _calculate_stress_multiplier(self, scenario_params, segment, quarter):
        """Calculate stress multiplier based on macroeconomic variables"""
        # Relationship between macro variables and credit losses
        gdp_sensitivity = {
            'commercial': -2.5,
            'residential_mortgage': -1.8,
            'consumer': -2.2,
            'credit_card': -3.1
        }

        unemployment_sensitivity = {
            'commercial': 1.2,
            'residential_mortgage': 2.1,
            'consumer': 2.8,
            'credit_card': 3.5
        }

        # Calculate multiplier
        gdp_impact = (scenario_params['gdp_growth'] - 0.025) * gdp_sensitivity.get(segment, -2.0)
        unemployment_impact = (scenario_params['unemployment'] - 0.045) * unemployment_sensitivity.get(segment, 2.0)

        # Apply time decay (impacts are highest in early quarters)
        time_decay = 1.0 - (quarter * 0.1)

        multiplier = 1.0 + (gdp_impact + unemployment_impact) * time_decay
        return max(multiplier, 0.1)  # Floor at 10% of base rate

    def generate_ccar_report(self, stress_results):
        """Generate CCAR submission report in required format"""
        report_data = {
            'institution_info': {
                'name': 'Example Bank',
                'rssd_id': '1234567',
                'submission_date': datetime.now().isoformat(),
                'reporting_period': 'Q4 2024'
            },
            'capital_planning': {},
            'stress_test_results': {},
            'governance_framework': {}
        }

        # Summarize stress test results
        for scenario, results in stress_results.items():
            min_capital_ratio = min(results['capital_ratios'])
            max_losses = max(results['credit_losses'])

            report_data['stress_test_results'][scenario] = {
                'minimum_tier1_capital_ratio': min_capital_ratio,
                'maximum_quarterly_losses': max_losses,
                'total_projected_losses': sum(results['credit_losses']),
                'quarter_of_minimum_ratio': results['capital_ratios'].index(min_capital_ratio) + 1
            }

        # Validate against regulatory requirements
        validation_results = self._validate_ccar_results(report_data)

        return {
            'report': report_data,
            'validation': validation_results,
            'submission_ready': validation_results['passed']
        }

    def _validate_ccar_results(self, report_data):
        """Validate CCAR results against regulatory requirements"""
        validation_results = {
            'passed': True,
            'errors': [],
            'warnings': []
        }

        # Check minimum capital ratio requirements
        for scenario, results in report_data['stress_test_results'].items():
            min_ratio = results['minimum_tier1_capital_ratio']

            if scenario == 'severely_adverse' and min_ratio < 0.045:  # 4.5% minimum
                validation_results['errors'].append(
                    f"Severely adverse scenario capital ratio {min_ratio:.3f} below regulatory minimum"
                )
                validation_results['passed'] = False

            if min_ratio < 0.025:  # 2.5% absolute minimum
                validation_results['errors'].append(
                    f"{scenario} scenario capital ratio {min_ratio:.3f} below absolute minimum"
                )
                validation_results['passed'] = False

        return validation_results