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def process_refund(self, payment_intent_id: str, amount: Decimal = None):
    """Process full or partial refund"""
    return stripe.Refund.create(
        payment_intent=payment_intent_id,
        amount=int(amount * 100) if amount else None
    )

def handle_webhook(self, payload: str, signature: str):
    """Handle Stripe webhook events"""
    try:
        event = stripe.Webhook.construct_event(
            payload, signature, webhook_secret
        )

        if event.type == "payment_intent.succeeded":
            payment_intent = event.data.object
            self.handle_successful_payment(payment_intent)
        elif event.type == "payment_intent.payment_failed":
            payment_intent = event.data.object
            self.handle_failed_payment(payment_intent)

        return {"status": "success"}
    except ValueError:
        return {"status": "invalid_payload"}

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def create_link_token(self, user_id: str):
    """Create link token for Plaid Link"""
    response = self.client.LinkToken.create({
        "user": {"client_user_id": user_id},
        "client_name": "My App",
        "products": ["auth", "transactions"],
        "country_codes": ["US"],
        "language": "en"
    })
    return response["link_token"]

def exchange_public_token(self, public_token: str):
    """Exchange public token for access token"""
    response = self.client.Item.public_token.exchange(public_token)
    return {
        "access_token": response["access_token"],
        "item_id": response["item_id"]
    }

def get_accounts(self, access_token: str):
    """Get user's bank accounts"""
    response = self.client.Accounts.get(access_token)
    return response["accounts"]

def get_transactions(self, access_token: str, start_date: str, end_date: str):
    """Get transactions for date range"""
    response = self.client.Transactions.get(
        access_token,
        start_date,
        end_date
    )
    return response["transactions"]

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from decimal import Decimal, ROUND_HALF_UP
from datetime import datetime, timedelta

class FinancialCalculator:
    @staticmethod
    def calculate_interest(principal: Decimal, rate: Decimal, periods: int) -> Decimal:
        """Calculate compound interest"""
        return principal * ((1 + rate) ** periods - 1)

    @staticmethod
    def calculate_loan_payment(principal: Decimal, annual_rate: Decimal, months: int) -> Decimal:
        """Calculate monthly loan payment (amortization)"""
        monthly_rate = annual_rate / 12
        payment = principal * (monthly_rate * (1 + monthly_rate) ** months) / \
                  ((1 + monthly_rate) ** months - 1)
        return payment.quantize(Decimal('0.01'), rounding=ROUND_HALF_UP)

    @staticmethod
    def calculate_npv(cash_flows: list[Decimal], discount_rate: Decimal) -> Decimal:
        """Calculate Net Present Value"""
        npv = Decimal('0')
        for i, cf in enumerate(cash_flows):
            npv += cf / ((1 + discount_rate) ** i)
        return npv.quantize(Decimal('0.01'), rounding=ROUND_HALF_UP)

    @staticmethod
    def calculate_roi(gain: Decimal, cost: Decimal) -> Decimal:
        """Calculate Return on Investment"""
        return ((gain - cost) / cost * 100).quantize(Decimal('0.01'))

from sklearn.ensemble import RandomForestClassifier
import pandas as pd

class FraudDetectionService:
    def __init__(self):
        self.model = RandomForestClassifier()

    def extract_features(self, transaction: dict) -> dict:
        """Extract features for fraud detection"""
        return {
            "amount": transaction["amount"],
            "hour_of_day": transaction["timestamp"].hour,
            "day_of_week": transaction["timestamp"].weekday(),
            "merchant_category": transaction["merchant_category"],
            "is_international": transaction["is_international"],
            "card_present": transaction["card_present"],
            "transaction_velocity_1h": self.get_velocity(transaction, hours=1),
            "transaction_velocity_24h": self.get_velocity(transaction, hours=24)
        }

    def predict_fraud(self, transaction: dict) -> dict:
        """Predict if transaction is fraudulent"""
        features = self.extract_features(transaction)
        fraud_probability = self.model.predict_proba([features])[0][1]

        return {
            "is_fraud": fraud_probability > 0.8,
            "fraud_score": fraud_probability,
            "risk_level": self.get_risk_level(fraud_probability)
        }

    def get_risk_level(self, score: float) -> str:
        if score > 0.9:
            return "CRITICAL"
        elif score > 0.7:
            return "HIGH"
        elif score > 0.5:
            return "MEDIUM"
        else:
            return "LOW"

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    # Store only last 4 digits and token
    payment_record = {
        "token": token,
        "last_4": card_data["number"][-4:],
        "exp_month": card_data["exp_month"],
        "exp_year": card_data["exp_year"]
    }

    return self.process_with_token(token)

def tokenize_card(self, card_data: dict) -> str:
    # Use payment gateway tokenization
    return stripe.Token.create(card=card_data)["id"]

    # Sanctions screening
    sanctions_clear = self.screen_sanctions(customer_data)

    # Risk assessment
    risk_level = self.assess_risk(customer_data)

    return {
        "verified": identity_verified and sanctions_clear,
        "risk_level": risk_level,
        "requires_manual_review": risk_level == "HIGH"
    }

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