Summary  
Demonstrates how to implement an unsupervised anomaly detection algorithm (IsolationForest) on numerical features to fit a model and flag outlier data points.

General domain of usage  
Compliance monitoring

Machine learning is a valuable tool in compliance because it helps you detect patterns and predict suspicious activity more efficiently than manual review alone. In a compliance context, machine learning can analyze large volumes of transaction data to identify behaviors that deviate from the norm, which may indicate fraud, money laundering, or other regulatory breaches. By learning from historical data, these algorithms can flag unusual transactions for further investigation, reducing the risk of missed violations and improving the speed and accuracy of compliance monitoring.

from sklearn.ensemble import IsolationForest
import pandas as pd

# Hardcoded dataset of transactions
data = {
    "transaction_id": [1, 2, 3, 4, 5, 6, 7, 8],
    "amount": [100, 110, 95, 102, 5000, 98, 105, 120],
    "customer_age": [34, 36, 33, 35, 40, 34, 37, 32]
}

df = pd.DataFrame(data)

# Use only numerical features for anomaly detection
features = df[["amount", "customer_age"]]

# Create and fit IsolationForest model
model = IsolationForest(contamination=0.125, random_state=42)
df["anomaly"] = model.fit_predict(features)

The **IsolationForest** algorithm works by randomly selecting features and splitting data points to isolate observations. In practice, **outliers**—or anomalies—are easier to isolate because they differ significantly from the rest of the data. This makes `IsolationForest` well-suited for compliance data, where you want to flag rare or unusual transactions that could signal suspicious activity. By training on features such as `transaction amount` and `customer_age`, the model can help you focus your review on the most atypical transactions, improving the efficiency and effectiveness of compliance monitoring.

# Print out transactions flagged as outliers
outliers = df[df["anomaly"] == -1]
print("Flagged outlier transactions:")
print(outliers[["transaction_id", "amount", "customer_age"]])

What is the purpose of using machine learning in compliance?

Which scikit-learn model can be used for anomaly detection?

What is an outlier in the context of compliance?

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.

Simple Machine Learning for Compliance

1. What is the purpose of using machine learning in compliance?

2. Which scikit-learn model can be used for anomaly detection?

3. What is an outlier in the context of compliance?