Fraud detection often involves uncovering irregularities in datasets that may hint at manipulation or deceit. One powerful tool for such analyses is Benford’s Law, a statistical principle that reveals predictable patterns in naturally occurring numerical datasets. By leveraging modern AI tools like ChatGPT and Microsoft Co-Pilot, Certified Fraud Examiners (CFEs) can efficiently apply Benford’s Law to detect potential financial fraud.

What is Benford’s Law?

Benford’s Law states that in datasets drawn from natural processes (e.g., financial transactions, population numbers), the leading digits of numbers are not uniformly distributed. Instead, smaller digits like 1 occur more frequently than larger digits. For example:

•1 is the leading digit about 30% of the time.

•9 appears as the leading digit less than 5% of the time.

When a dataset deviates significantly from this expected distribution, it can indicate potential manipulation, such as fabricated or altered data. CFEs use Benford’s Law to identify anomalies in financial records, expense reports, and transaction logs, which may signify fraud.

Using AI Tools to Simplify Benford’s Law Analysis

Applying Benford’s Law manually can be time-consuming, especially with large datasets. This is where AI tools like ChatGPT and Microsoft Co-Pilot come into play, streamlining the process and enabling CFEs to focus on interpreting results and investigating irregularities.

How ChatGPT Supports Benford’s Law Analysis

ChatGPT can assist CFEs in several ways:

1.Explaining Concepts: ChatGPT can provide clear explanations of Benford’s Law and guide users through its application.

2.Code Generation: It generates scripts for programming languages like Python to automate Benford’s Law analysis.

3.Result Interpretation: ChatGPT helps CFEs understand deviations and assess whether they warrant further investigation.

Example Use Case with ChatGPT:

•Prompt: “Generate Python code to apply Benford’s Law to a financial dataset and visualize the results.”

•Output:

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

# Load dataset

data = pd.read_csv("financial_data.csv")

data['LeadingDigit'] = data['Amount'].astype(str).str[0].astype(int)

# Benford's Law expected frequencies

benford_freq = np.array([0.301, 0.176, 0.125, 0.097, 0.079, 0.067, 0.058, 0.051, 0.046])

# Calculate actual frequencies

actual_freq = data['LeadingDigit'].value_counts(normalize=True).sort_index().values

# Plot comparison

plt.bar(range(1, 10), benford_freq, alpha=0.7, label="Benford's Expected")

plt.bar(range(1, 10), actual_freq, alpha=0.7, label="Actual Data")

plt.xlabel('Leading Digit')

plt.ylabel('Frequency')

plt.legend()

plt.show()

This code quickly analyzes a dataset and visualizes deviations between expected and actual distributions.

Microsoft Co-Pilot: Automating and Enhancing Analysis

Microsoft Co-Pilot integrates with tools like Excel and Power BI to simplify Benford’s Law application, especially for professionals less comfortable with coding.

1.Data Preparation: Co-Pilot can clean and organize datasets, such as extracting leading digits from transaction amounts.

2.Automated Calculations: It generates formulas for calculating actual digit frequencies and comparing them to Benford’s expected distribution.

3.Visualization: Co-Pilot helps create charts and dashboards to visualize discrepancies at a glance.

Example Use Case with Co-Pilot:

•Request: “Set up a Benford’s Law analysis template in Excel, complete with a chart comparing actual and expected frequencies.”

•Result: Co-Pilot generates the necessary formulas and visualization, saving time and effort.

Real-World Application of AI and Benford’s Law

A CFE investigating procurement records used ChatGPT to generate a Python script for Benford’s Law analysis and Co-Pilot to visualize the data in Excel. The analysis revealed an unusually high occurrence of transactions with the leading digit 7, prompting further review. This led to the discovery of falsified invoices and kickback schemes.

Key Benefits of AI Tools for CFEs

1.Efficiency: Automates time-intensive tasks.

2.Accessibility: Simplifies complex analyses for non-technical users.

3.Enhanced Insight: Facilitates quick identification of anomalies.

Conclusion

Benford’s Law is a valuable tool in the CFE’s arsenal for uncovering financial fraud. By integrating modern AI tools like ChatGPT and Microsoft Co-Pilot, CFEs can conduct more efficient, accurate, and accessible analyses. As these tools continue to evolve, fraud examiners can expect even greater support in their investigations.

If you’ve used AI tools in fraud detection, share your insights in the comments or join the discussion at our next ACFE PNW Chapter conference in April!