What is fraud detection?

With the rise of technology, online fraud risks are becoming more prevalent. Examples of online fraud include money laundering, fraudulent banking claims, identity theft, forged bank checks, and cyberattacks. In addition, AI scams, like voice claiming, deep fake videos, and enhanced phishing, are on the rise.

To combat the financial risks associated with fraudulent activities, businesses are implementing comprehensive fraud detection solutions. From using fraud detection software, like Eftsure, to generating internal fraud risk scores and monitoring transactions in real time, minimizing the likelihood of fraudulent transactions can preserve your financial assets from unwanted hacks and attacks.

Types of Fraud Detection Techniques

Most fraud detection techniques deploy a combination of statistical data analysis and artificial intelligence. Let’s break down each of these two categories into more detail, highlighting the common techniques found under each method.

Statistical Data Analysis Techniques

Statistical data analysis uses past transaction data to pinpoint unusual and suspicious transactions. Here are the four types of statistical data analysis fraud detection techniques:

1. Statistical Parameter Calculation – This type of fraud detection solution calculates your transaction averages, quantiles, performance metrics, and probability distributions. Any data point outside of these parameters will be flagged.
2. Regression Analysis – Regression analysis evaluates the relationship between two transactions, helping you develop a model to predict future fraudulent activities based on usage patterns.
3. Probability Distributions and Models – This statistical data analysis technique maps different fraudulent activities, giving you a baseline to detect fraud.
4. Data Matching – Data matching compares two sets of data, looking for duplicate records and identifying links between transactions.

Artificial Intelligence Techniques

Over the past few years, the use of artificial intelligence techniques has been widely adopted in businesses as an effective fraud detection technique. Not only can AI improve efficiency, but it can also help your organization streamline fraud prevention processes. Here are the four main types of artificial intelligence fraud detection solutions:

1. Data Mining – Data mining is the process of classifying data to detect patterns and anomalies that might be fraud.
2. Neural Networks – This AI-based technique goes through the process of classifying, clustering, generalizing, and forecasting fraud-related data based on information from an internal audit or issued financial statements.
3. Machine Learning – Machine learning is the process of teaching your system to detect future fraud transactions based on past data. For example, if you classify a software subscription as non-fraudulent, your system will understand that the recurring subscription is not fraud going forward.
4. Pattern Recognition – This type of AI technique leverages past data to pinpoint differences in patterns, such as a software subscription jumping 10x in cost.

Applications of Fraud Detection Systems

Every organization can benefit from fraud prevention techniques, regardless of industry or size. However, there are a few sectors that must have a dedicated fraud team or fraud detection model in place to minimize fraudulent activity. Let’s go through a few of these industries in more detail.

Banking and Financial Services

The banking and financial services sectors frequently process and receive payments, which increases their risk of fraud. For one, there is the risk of API fraud from the required opening of services under Payment Services Directive 2. Other fraud risks include stolen and fake credit card information, website cloning to mine sensitive data, and ATM fraud using another person’s card to withdraw money.

Ecommerce and Retail

Ecommerce and retail businesses process high levels of payments through online sources, creating a higher risk of payment fraud. One example of financial fraud in the ecommerce and retail sectors includes promo abuse. Promo abuse occurs when an individual abuses the coupon policy to gain access to more goods or services without paying the proper price.

In addition, delivery fraud occurs when a hacker obtains a person’s sensitive information and uses it to purchase goods that are delivered to a different address. The right fraud detection solution can combat these risks.

IT and Telecom

IT and telecom are often used to carry out advanced fraud, such as phone fraud, call forwarding fraud, multiple transfers fraud, and one-ring-and-cut fraud. Without the proper controls in place, IT and telecom fraud could cost your business thousands of dollars.

Fraud Detection Best Practice

The good news is that there are numerous different approaches you can implement to combat fraud in your organization. Let’s explore some fraud detection best practices.

Create a Potential Fraud Risk Profile

Taking a top-down approach in your risk assessment allows you to list the types of fraud your business is most likely to encounter. Understanding how hackers and attackers might try to obtain your information and assets allows you to minimize your exposure.

Implement Continuous Monitoring

Suspicious activities need to be caught timely, even if they are false positives. Having continuous monitoring in place ensures your data is constantly being tested and validated. This doesn’t mean that you need to have a dedicated fraud prevention specialist on your staff verifying all transactions. Instead, you can invest in comprehensive software, like Eftsure.

Provide Employee Training

Training is a crucial component of crafting an effective fraud detection strategy. After all, many scammers target your employees through phishing and phone scams. By training your employees, you can reduce the risk of someone falling victim to fraudulent activities and better protect your organization.

Use Artificial Intelligence

Artificial intelligence is a powerful tool for detecting and preventing numerous types of fraud, from insurance fraud and new account fraud to identity theft and synthetic identity fraud. Most fraud detection software programs will use some form of artificial intelligence to analyze your data. However, constantly update your accounting system and validate transactions, such as creating transaction parameters.

Streamline Fraud Reporting

If your employees or customers come across an unusual request, there should be clear channels to report the issues. Establish a set of controls for reporting fraud. For example, if your employee accidentally clicks on a scam email, what do they do? Do they keep going about their day, or do they have a set of protocols that they are required to follow? Informing everyone about reporting suspicious activities is crucial to catch fraud timely and minimize damages.

Invest in Technology

If you’re like most business owners, you’re constantly on the go. From finding new customers and managing employees to smoothing business disruptions and handling day-to-day transactions, you might not have the time to monitor your fraud risks properly.

Summary

• Fraud detection is the process of preventing and detecting unusual and suspicious transactions.
• Implementing statistical data analysis and artificial intelligence are the two main ways to protect against fraudulent activities.
• Fraud detection is vital for businesses that process payments, especially those in the banking, ecommerce, retail, IT, and telecom industries.
• There are different ways you can protect your business from fraud, including streamlining fraud reporting, using artificial intelligence, implementing continuous monitoring, providing employee training, and investing in the right software programs.
• Leveraging an all-in-one expense platform, like Eftsure, provides you with the necessary fraud protection while advancing your expense management function.