You are reading: How AI is Being Used in Fraud Detection

April 11, 2024

How AI is Being Used in Fraud Detection

Daniel Berlind

By now, you’ve likely tuned into the conversation on artificial intelligence – commonly referred to as AI. Gone are the days when people picture robots and scary computer programs. We’ve now ushered in an age where AI can be a positive game changer for many industries.

Fraud detection is one such industry where AI can be a time saver and a money saver. While the advancement of technology has led to helpful AI programs, it’s also made room for fraudsters to hone their craft and find new ways to exploit businesses. Luckily, AI plays a significant role in stopping these scammers from getting away with it.

In this article, we’ll explore how businesses use AI-enabled fraud detection and its benefits and risks.

How AI Works in Fraud Detection

Everything is online nowadays. Rental applications, online banking, document signing, you name it – we’ve moved away from paper trails and are fully embracing the Internet and all it has to offer. In turn, this has made us more susceptible to fraud. As new technology makes our day-to-day lives easier, it also provides new opportunities for scammers. But have no fear, AI algorithms can help detect and prevent multiple types of fraudulent activity.

AI-driven fraud detection systems use a variety of data points to recognize patterns and anomalies in user behavior, document formatting, and transactions that could be fraud indicators. These include online habits, location information, past purchase history, and even social media activity.

Types of Fraud AI Can Detect

AI can detect various types of fraud across different industries. Some of the common types of fraud that AI can detect include:

Financial Fraud: AI software can scan data to identify patterns indicative of credit card fraud, identity theft, and money laundering.

Insurance Fraud: These systems are used to help insurance companies identify cases of fraudulent claims revolving around property damage, medical expenses, or other types of coverage.

E-commerce Fraud: AI systems review online shopping behavior to detect suspicious or fraudulent transactions.

Identity Theft: AI can monitor for suspicious activities related to identity theft, such as unauthorized access to accounts, changes in personal information, and unusual login locations or devices.

Application Fraud: In the real estate industry, AI can help verify the authenticity of documents (typically bank statements and pay stubs) submitted during the application process.

Benefits of Using AI in Fraud Detection

The first benefit of incorporating AI fraud detection programs into your operations is the time you and your team will save. Say goodbye to the hours spent manually reviewing documents and forms, and say hello to systems that can run in the background while you attend to other tasks.

Along with improved speed comes a higher accuracy rate. AI may not be perfect, but its algorithms can analyze large amounts of data and identify patterns that are difficult for humans to detect. It can even detect the smallest alterations to a document that the human eye could never catch.

Implementing AI can also keep costs down. Man hours will be reduced as various tasks can be taken care of by AI programs. Not only that, these programs will help with long-term costs like lawsuits over false fraud accusations and reputational damage.

Risks of Using AI in Fraud Detection

Because AI provides so many benefits for businesses, it’s easy to think they’re a one-stop solution with no errors. However, solely relying on these AI systems can provide a false sense of security. While they tend to be more reliable than a human, they are not perfect and can still make mistakes. This is why it’s essential to perform audits and update the systems accordingly.

Another potential pitfall is that fraudsters are becoming increasingly aware of these advanced fraud detection systems and are doing everything in their power to sneak past them. Businesses should stay informed on the latest trends and use that information to stay steps ahead of fraudsters.

Social engineering threats remain a concern, even with AI fraud detection. These types of threats are not automated (for example, a phishing email), so all it takes is one absent-minded employee to click a bad link. The best thing you can do is train your team to recognize and combat these scams.

Lastly, there are some privacy concerns when it comes to using AI in fraud detection. Businesses need to understand the data they access, how it’s used, and what its limitations are. By understanding these regulations, companies can ensure they remain compliant.

Industries That Can Benefit From Fraud Detection

The industries that would most benefit from AI-enabled fraud detection include:

  • E-commerce
  • Real estate and property management
  • Banking and financial services
  • Insurance
  • Healthcare
  • Government and public administration

Examples of AI in Fraud Detection

Let’s look at some AI fraud detection examples in a couple of different sectors.

Mastercard’s Decision Intelligence technology uses patterns from historical shopping data and spending habits of cardholders to set a behavioral baseline against which it compares new transactions. Standard prevention technologies rely on a one-size-fits-all approach to evaluate all transactions. In contrast, AI systems get around the common causes of false declines by looking at every transaction in the context of another. IBM predicts that AI usage in fraud prevention can reduce the number of false declines by a whopping 80%.

Circa LA started using Snappt to attack the issue of fraudulent rental applications. Snappt’s AI-enabled technology analyzes financial documents submitted by the applicant, searching for various telltale signs that the document has been altered. The technology boasts a 99.8% accuracy rate and is a much faster alternative to manual methods of verification.

AI vs. Machine Learning

In your AI research, you might see both Artificial Intelligence and Machine Learning used in conjunction. What’s the difference between the two?

Well, kind of. Think of machine learning as a subset of AI. Machine learning is the process of using mathematical models of data to help a computer learn without direct instruction. This allows the computer to continue learning and improving on its own based on the experience acquired. AI is an all-encompassing term used to describe the capability of a computer to mimic human cognitive functions, such as learning, data collecting, and problem-solving.

To Sum Up…

The role of AI in fraud detection should not be overlooked. From financial institutions to healthcare providers, AI stands as a vigilant guardian against the evolving landscape of fraudulent activities. While the benefits of AI-powered fraud detection are clear—quick identification, higher accuracy, and time saved—it’s essential to navigate the potential risks as well. The reliance on AI algorithms has raised concerns about privacy breaches, fraudster capabilities, and the need for human oversight. Do your due diligence when deciding if an AI system is the best fit for your business.

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