Chapter 14: Techniques for Detecting Fraud: Fraud Detection using Peer-Group Analysis

Published: 13 November 2023| Version 2 | DOI: 10.17632/yc8mbfy3rt.2


account_summary.csv, consists of transactional data across various accounts. The goal is to conduct a peer-group analysis to detect anomalies that may signify fraudulent transactions. You will focus on calculating statistical measures, such as the average transaction amount and standard deviation, and then use these to compute a 'distance' metric for each transaction. Transactions with a 'distance' exceeding a predetermined threshold will be flagged as potential fraud.


Steps to reproduce

To apply peer-group analysis for anomaly and fraud detection, follow these steps using Python and its data analysis libraries. This example assumes you have a CSV file named account_summary.csv with transactional data. Step 1: Setup the Python Environment: Make sure you have the necessary packages installed. Step 2: Load the Dataset: Read the data from the CSV file into a DataFrame. Step 3: Compute Summary Statistics - Group the data by account_id and calculate the sum, mean, and standard deviation of the transaction amounts. - Handle any potential NaN values which might occur if a standard deviation can't be calculated due to a single transaction. Step 4: Anomaly Identification - Calculate the Z-score for each transaction to determine its distance from the mean. - Establish a threshold, for instance, transactions with a Z-score greater than 2 or less than -2 could be potential anomalies. Step 5: Visualization: Use a scatter plot to visualize the transactions and highlight anomalies. Step 6: Analysis and Interpretation: Filter out the potential fraudulent transactions for further analysis. Step 7: Translate Findings to Fraud Detection: Compare the identified anomalies with known cases of fraud, if available, to evaluate the effectiveness of the peer-group analysis method. Remember to adjust the file path to the CSV file accordingly. This is a simple example, and in a real-world scenario, you would need to perform additional data cleaning, feature engineering, and possibly consider other factors that might influence transaction patterns.


Statistics, Machine Learning, Insurance Fraud