Summary  
This chapter covers how to compute basic aggregate statistics—total, average, maximum, and minimum—from a list of numerical data using loops and built-in functions, enabling the identification of statistical outliers and trends.

General domain of usage  
Regulatory compliance monitoring

Compliance officers rely on **data analysis** to detect **trends**, spot **outliers**, and support investigations into potential regulatory breaches. By systematically examining transaction data, you can identify patterns that may indicate money laundering, fraud, or other forms of non-compliance. Analyzing data helps you move beyond intuition and base decisions on concrete evidence, making your compliance processes both efficient and defensible.

# List of transaction amounts in dollars
transactions = [200, 450, 1300, 50, 700, 60, 1200, 90, 320, 80]

# Calculate total transaction amount
total = 0
for amount in transactions:
    total += amount

# Calculate average transaction amount
average = total / len(transactions)

print("Total transaction amount:", total)
print("Average transaction amount:", average)

Calculating the **total** and **average** transaction amounts provides a foundation for compliance analysis. Large totals or unusually high averages can point to suspicious activity, such as structuring or layering in money laundering. By comparing these numbers over time or between accounts, you can reveal **outliers** or **trends** that merit further investigation. Simple calculations like these are often the first step in uncovering compliance breaches and supporting regulatory inquiries.

# Find maximum and minimum transaction amounts
max_transaction = max(transactions)
min_transaction = min(transactions)

print("Maximum transaction amount:", max_transaction)
print("Minimum transaction amount:", min_transaction)

Why is data analysis important for compliance officers?

Which Python function can be used to find the average of a list of numbers?

What can unusually high or low transaction amounts indicate?

Learn how to leverage Python to automate, analyze, and streamline compliance tasks. This course is tailored for compliance officers seeking to enhance their efficiency and accuracy using Python, focusing on real-world scenarios such as monitoring transactions, detecting anomalies, and generating compliance reports.

Explore how Python can automate routine compliance tasks, such as flagging suspicious transactions and validating regulatory requirements.

Learn how to analyze compliance data using Python and visualize findings to support audits and reporting.

Delve into advanced Python techniques for compliance, including rule-based systems, simple machine learning, and reporting.

Introduction to Data Analysis for Compliance

1. Why is data analysis important for compliance officers?

2. Which Python function can be used to find the average of a list of numbers?

3. What can unusually high or low transaction amounts indicate?