Fraud Investigation: A Comprehensive Guide to Techniques and Tools for Fraud Investigators (2023)

Financial crime in the United States has surged  staggeringly , costing trillions of dollars.

Did you know that the average time for detecting fraud is a year? Such delay harms individuals and businesses financially.

Fraud investigation agencies often struggle due to a lack of resources or reliance on outdated tools like Excel, limiting their ability to analyze financial crimes.

In this article, we share practical techniques to track money flow and combat illicit activities. Plus, a list of powerful tools to aid fraud investigations without draining resources.

Given the urgency of addressing financial crimes, it’s crucial to adopt enhanced techniques. Equip yourself with the best strategies available for quicker solutions.

The Art of Fraud Investigation: 5 Proven Techniques Investigators Should Know About

Here are the 5 techniques to uncover corporate fraud and combat financial misconduct:

1. Transaction Overview

Transaction overview is a machine learning model used in financial fraud detection that detects unusual behavior in transactions by checking for information like the credit/debit card number, location, date, time, I.P. address, amount, and transaction frequency. 

All this data is fed as input into the fraud detection algorithm, and the analyst checks for any unusual activity that may indicate a fraud attack. Transaction records document the transfer of assets between parties which when viewed over time, exhibit specific patterns. Fraudulent activity frequently deviates from these trends in some way, offering a starting point for data-driven fraud detection methods. 

2. Flow of Funds 

The flow of funds model helps in understanding the movement of money within a system or organization. 

The model does not measure the performance of any particular asset but rather focuses on how money moves – by graphically demonstrating where money comes from, where it goes, who has been involved, and more.

Flows might differ based on the frequency at which money is transferred, the currency and the nature of the business, the goods or services the business provides, by whom the business is run, and the asset types that the business holds. 

Models such as this one help the fraud investigator scrutinize the movement of funds in various cyber crimes, elder abuse cases, and more.

3. Benford’s Law

Benford’s law is a mathematical tool and an effective method for data analysis to help detect fraud. 

Benford’s law describes the relative frequency distribution of numbers with leading digits in datasets. Leading digits with lesser values occur more frequently than leading digits with larger values. 

The rule stipulates that 30% of numbers begin with 1 and 5% with 9. This law states that leading 1s occur 6.5 times more than leading 9s! Benford’s law is the First Digit Law. Leading digits 1–9 would appear 11.1% of the time if they had equal probability. However, this does not stand true in several datasets.

The theory is that fraudsters tend to submit larger fake invoices, disrupting the natural order of numbers as predicted by Benford’s law. For example, if you run a Benford’s law tes