Apprendre Rule-Based Compliance Systems | Advanced Compliance Monitoring

Python for Compliance Officers

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A rule-based compliance system allows you to define compliance rules as code, making it possible to evaluate transactions or activities against those rules automatically. In practice, this means you write Python functions or expressions that represent specific compliance requirements, such as transaction amount limits, blacklisted entities, or suspicious frequency of activities. By using Python functions, you can create rules that are both precise and easy to modify as regulations change or as new risks are identified.


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# Define compliance rules as Python functions

def rule_amount_limit(transaction):
    return transaction["amount"] <= 10000

def rule_blacklist(transaction):
    blacklist = {"Evil Corp", "Fraudster LLC"}
    return transaction["counterparty"] not in blacklist

def rule_frequency(transaction, user_history):
    # Flag if user has more than 5 transactions today
    today_transactions = [t for t in user_history if t["date"] == transaction["date"]]
    return len(today_transactions) <= 5

# Example transaction and user history
transaction = {
    "amount": 8000,
    "counterparty": "Trusted Inc",
    "date": "2024-06-01"
}

user_history = [
    {"amount": 3000, "counterparty": "Trusted Inc", "date": "2024-06-01"},
    {"amount": 2000, "counterparty": "Other Corp", "date": "2024-06-01"},
    {"amount": 1500, "counterparty": "Trusted Inc", "date": "2024-06-01"},
    {"amount": 1000, "counterparty": "Trusted Inc", "date": "2024-06-01"},
    {"amount": 500, "counterparty": "Trusted Inc", "date": "2024-06-01"},
]

# Apply rules
all_rules_passed = (
    rule_amount_limit(transaction) and
    rule_blacklist(transaction) and
    rule_frequency(transaction, user_history)
)

print("Transaction is compliant:", all_rules_passed)

Structuring your compliance rules as individual Python functions makes it simple to add, remove, or update rules without rewriting the entire system. Each rule can focus on one aspect of compliance, and you can collect all rules in a list or registry for systematic application. This modular approach is essential for keeping your compliance checks flexible as regulations evolve or as your organization’s risk appetite changes.


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# Existing rules
def rule_amount_limit(transaction):
    return transaction["amount"] <= 10000

def rule_blacklist(transaction):
    blacklist = {"Evil Corp", "Fraudster LLC"}
    return transaction["counterparty"] not in blacklist

# New rule: block transactions on weekends
def rule_no_weekend(transaction):
    from datetime import datetime
    date_obj = datetime.strptime(transaction["date"], "%Y-%m-%d")
    return date_obj.weekday() < 5  # 0=Monday, 6=Sunday

# Collect all rules in a list
rules = [rule_amount_limit, rule_blacklist, rule_no_weekend]

# Sample transactions
transactions = [
    {"amount": 5000, "counterparty": "Trusted Inc", "date": "2024-06-03"},  # Monday
    {"amount": 15000, "counterparty": "Trusted Inc", "date": "2024-06-04"}, # Tuesday
    {"amount": 2000, "counterparty": "Evil Corp", "date": "2024-06-08"},    # Saturday
]

# Apply all rules to each transaction
for tx in transactions:
    compliant = all(rule(tx) for rule in rules)
    print(f"Transaction {tx} is compliant: {compliant}")

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Section 3. Chapitre 1

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Section 3. Chapitre 1

Rule-Based Compliance Systems

1. What is a rule-based system in compliance?

2. How can Python functions represent compliance rules?

3. Why is flexibility important in compliance rule systems?