finance-expert

SKILL.md

Finance Expert

Expert guidance for financial systems, FinTech applications, banking platforms, payment processing, and financial technology development.

Core Concepts

Financial Systems

Core banking systems

Payment processing

Trading platforms

Risk management

Regulatory compliance (PCI-DSS, SOX, Basel III)

Financial reporting

FinTech Stack

Payment gateways (Stripe, PayPal, Square)

Banking APIs (Plaid, Yodlee)

Blockchain/crypto

Open Banking APIs

Mobile banking

Digital wallets

Key Challenges

Security and fraud prevention

Real-time processing

High availability (99.999%)

Regulatory compliance

Data privacy

Transaction accuracy

Payment Processing

# Payment gateway integration (Stripe)
import stripe
from decimal import Decimal

stripe.api_key = "sk_test_..."

class PaymentService:
    def create_payment_intent(self, amount: Decimal, currency: str = "usd"):
        """Create payment intent with idempotency"""
        return stripe.PaymentIntent.create(
            amount=int(amount * 100),  # Convert to cents
            currency=currency,
            payment_method_types=["card"],
            metadata={"order_id": "12345"}
        )

    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"}

Banking Integration

# Open Banking API integration (Plaid)
from plaid import Client
from plaid.errors import PlaidError

class BankingService:
    def __init__(self):
        self.client = Client(
            client_id="...",
            secret="...",
            environment="sandbox"
        )

    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"]

Financial Calculations

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'))

Fraud Detection

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"

Regulatory Compliance

# PCI-DSS Compliance
class PCICompliantPaymentHandler:
    def process_payment(self, card_data: dict):
        # Never store full card number, CVV, or PIN
        # Tokenize card data immediately
        token = self.tokenize_card(card_data)

        # 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"]

# KYC/AML Compliance
class ComplianceService:
    def verify_customer(self, customer_data: dict) -> dict:
        """Perform KYC verification"""
        # Identity verification
        identity_verified = self.verify_identity(customer_data)

        # 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"
        }

Best Practices

Security

Never log sensitive financial data (PAN, CVV)

Use tokenization for card storage

Implement strong encryption (AES-256)

Use TLS 1.2+ for all communications

Implement rate limiting and fraud detection

Regular security audits

Data Handling

Use Decimal type for money (never float)

Store amounts in smallest currency unit (cents)

Implement idempotency for all transactions

Maintain complete audit trails

Handle timezone conversions properly

Transaction Processing

Implement two-phase commits

Use database transactions (ACID)

Handle network failures gracefully

Implement retry logic with exponential backoff

Support transaction reversals and refunds

Anti-Patterns

❌ Using float for money calculations
❌ Storing credit card data unencrypted
❌ No transaction logging/audit trail
❌ Synchronous payment processing
❌ No idempotency in payment APIs
❌ Ignoring PCI-DSS compliance
❌ No fraud detection

Resources

PCI-DSS: https://www.pcisecuritystandards.org/

Stripe API: https://stripe.com/docs/api

Plaid: https://plaid.com/docs/

Open Banking: https://www.openbanking.org.uk/