Leer Detecting Anomalies in Transaction Data | Automation and Fraud Detection in Banking

Fraud detection is a critical process in banking, aiming to identify and prevent unauthorized or suspicious activities that could result in financial loss. One of the most practical techniques for spotting potential fraud is anomaly detection, which involves searching for transactions that deviate significantly from what is considered normal behavior. In banking, anomalies can include unusually large withdrawals, frequent small transactions, or payments to unfamiliar recipients. Detecting such outliers early helps banks respond quickly to threats and protect both the institution and its customers.


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import pandas as pd

# Sample transaction data
data = {
    "transaction_id": [1, 2, 3, 4, 5, 6, 7],
    "amount": [100, 120, 110, 105, 5000, 115, 108]
}

df = pd.DataFrame(data)

# Calculate mean and standard deviation
mean = df["amount"].mean()
std = df["amount"].std()

# Flag transactions that are more than 2 standard deviations from the mean
outlier_threshold_high = mean + 2 * std
outlier_threshold_low = mean - 2 * std

df["is_outlier"] = (df["amount"] > outlier_threshold_high) | (df["amount"] < outlier_threshold_low)
print(df)

Setting thresholds is an important step in anomaly detection. By deciding how many standard deviations away from the mean a transaction must be to be considered suspicious, you control the sensitivity of your detection system. However, this simple statistical approach has limitations. It may flag legitimate transactions as anomalies if a customer makes a rare large purchase, or it may miss sophisticated fraud that does not result in unusually large or small amounts. Fraudsters may also adapt their behavior to stay within normal ranges, making it harder for basic methods to catch them.


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# Add a column to flag transactions as 'normal' or 'anomaly'
df["status"] = df["is_outlier"].apply(lambda x: "anomaly" if x else "normal")
print(df[["transaction_id", "amount", "status"]])

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Sectie 3. Hoofdstuk 2

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              1234567891011121314151617181920
            
import pandas as pd

# Sample transaction data
data = {
    "transaction_id": [1, 2, 3, 4, 5, 6, 7],
    "amount": [100, 120, 110, 105, 5000, 115, 108]
}

df = pd.DataFrame(data)

# Calculate mean and standard deviation
mean = df["amount"].mean()
std = df["amount"].std()

# Flag transactions that are more than 2 standard deviations from the mean
outlier_threshold_high = mean + 2 * std
outlier_threshold_low = mean - 2 * std

df["is_outlier"] = (df["amount"] > outlier_threshold_high) | (df["amount"] < outlier_threshold_low)
print(df)


              123
            
# Add a column to flag transactions as 'normal' or 'anomaly'
df["status"] = df["is_outlier"].apply(lambda x: "anomaly" if x else "normal")
print(df[["transaction_id", "amount", "status"]])

Was alles duidelijk?

Bedankt voor je feedback!

Sectie 3. Hoofdstuk 2

Detecting Anomalies in Transaction Data

1. What is an anomaly in the context of banking transactions?

2. How can standard deviation help identify suspicious transactions?

3. Why might a simple threshold-based approach miss some types of fraud?

Detecting Anomalies in Transaction Data

1. What is an anomaly in the context of banking transactions?

2. How can standard deviation help identify suspicious transactions?

3. Why might a simple threshold-based approach miss some types of fraud?