What Role Does Artificial Intelligence Have in Fraud Detection?

November 6, 2023 - Guest Writer

Despite advances in fraud detection technology, many individuals and businesses still face the impact of financial crime. In fact, losses from e-commerce online payment fraud reached 41 billion USD globally in 2022. Despite efforts from fraud detection technologies, cybercriminals are finding loopholes to continue stealing money from innocent customers.

Paying attention to artificial intelligence as a way to protect yourself from fraud is of paramount importance. It’s already paving the way for fraud prevention to protect your customers from financial crime.

What is Artificial Intelligence and How Does it Relate to Fraud Detection?

You may have heard of artificial intelligence in reference to many industries, including financial crime. AI is currently understood as machine learning software that can perform tasks usually done by humans. It can understand language and recognize patterns in order to make decisions that would have once needed to be made by a person. Fraud detection software is typically available online and hosted on Platform as a Service (PaaS) models like Platform.sh. An AI system will actively learn from every experience and can perform human-like thinking to solve complex problems.

By using advanced algorithms, AI can analyze anomalies in data to find evidence of fraud automatically. This makes it perfect for finding instances of fraud in e-commerce transactions. They can even learn from past fraud cases to create new methods for tackling fraud, improving detection accuracy. With AI, businesses can better prevent fraudulent transactions and safeguard themselves against financial losses in the future.

How Is AI Being Used in Fraud Detection?

So, what role is AI currently playing in detecting fraud? Developers are using AI to identify card and e-commerce fraud and find fake accounts. Developers will leverage algorithms and models to analyze large data sets and improve the AI’s machine learning capabilities over time.

This growing usage of AI technologies will be used in combination with various fraud prevention methods unrelated to AI that are already being performed by innovative e-commerce organizations. For example, backend infrastructure will still rely on programming languages and PHP type hinting to ensure code is correct and accurate.

This combination of AI and non-AI fraud prevention techniques can enable organizations to bring various types of fraud down to a minimum.

Looking ahead, let’s explore some ways that AI is starting to be used to detect and prevent fraud.

Detecting Card Fraud

Cybercriminals can obtain card information in many ways. They can either install small devices in ATMs, enact data breaches, or install malware on a victim’s device. These attempts can strain payment gateways, making it easier to steal information quickly. AI can easily detect card fraud, as it doesn’t rely on IPs to reduce incoming threats. Instead, AI monitors users’ behavior to distinguish hacking attempts from people. It then blocks cybercriminals from obtaining information.

Let’s look at an example of AI in action.

Sarah has a credit card issued from a reputable bank. The bank uses an AI system to analyze Sarah’s typical spending behavior, including her transaction locations, amount, and time of day she spends money. They will use behavior analysis on big data sets to retrieve this information.

Sarah’s credit card is used for a large transaction in a country where she has never visited. The bank’s AI system detects the transaction as an anomaly after analyzing Sarah’s previous spending habits.

The AI system gives the transaction a risk score based on this anomaly. The AI will alert the bank’s fraud detection team, depending on the score. It will include information regarding the transaction and the reasons behind the anomaly detection.

The fraud detection team reviews the alert and will try to verify the legitimacy of the transaction. They will likely contact Sarah to try to authorize the transaction.

If Sarah confirms the transaction as legitimate, the bank updates the AI system with this information. The system learns from this feedback and adjusts its understanding of what is classed as normal behavior.

Fake Accounts

Fraudsters use bots to create hundreds of fake accounts almost instantly. By having fake accounts, cybercriminals can spread malware, steal identities, and scam individuals.

AI can help to detect fake accounts by analyzing patterns in how people create accounts and checking for signs of malware or computer viruses. It will identify common characteristics of fake accounts, like usernames and posting behavior.

Using user behavior patterns, AI can examine differences between legitimate and fake accounts. Fake accounts often show unusual behavior, like quickly posting large amounts of content or engaging in repetitive actions.

That said, it’s important to note that the current generation of AI-based bot detection isn’t 100% foolproof. Even Twitter (now X) CEO Elon Musk admitted the challenges in curtailing bots on the social platform in a recent interview.

This low success rate is largely down to the limitations in the data sets used to train the AI. After all, these machine learning models can only work with what we teach them. But as companies become more transparent with their data, it’s likely we’ll see AI bot detection improve significantly.

Here’s an example of how AI can detect fake accounts.

On Instagram, a user named “Jane Jones” creates an account. The AI fraud system has already established a pattern of behavior when legitimate users create an account, noting how often users post and the time they spend online.

To establish whether “Jane Jones” is fake, the AI will evaluate how the user created the account. It checks the profile picture and username to see if they match the patterns of fake account creation.

The system notices that “Jane Jones” has posted 50 times in just an hour since account creation. This is unusual behavior, as real accounts will typically post less frequently and over a longer amount of time.

The AI system goes through the content of the posts. It detects that the text in the posts is repetitive and promotes what seems to be malware with a suspicious domain name, encouraging other users to download it. Domain names will be categorized as suspicious if misspelled or they use unusual top-level domains (TLDs). If a website is genuine, it will likely use a legitimate domain name, so you can easily navigate to the site through search engines.

“Jane Jones” also sends friend requests to many users quickly and engages in random conversations with short, nonsensical messages.

Because of this, the AI system has assigned a risk score to the account that highlights that the account is likely fake. This information is passed to the moderation team, who will manually review the account.

Detecting E-Commerce Fraud

Detecting fraud

AI can play a crucial role in detecting e-commerce fraud by analyzing various data points, patterns, and behaviors associated with fraudulent activities.

Here’s an example of how AI can detect e-commerce fraud.

An Etsy user named Amy is making a purchase on the site. Etsy’s AI fraud system has already established Amy’s typical spending patterns, including how often she buys items and the product categories she typically browses. Normally, Amy likes to buy homewares.

Amy placed an expensive order for an electronic device with expedited shipping. This is abnormal for her, as she has never bought electronics before or paid for faster shipping. The AI flags this and compares Amy’s billing and shipping address. It detects that both addresses are significantly different, and the device she used to place the order is inconsistent with her past history.

The AI system examines the electronic device’s product description and customer reviews to detect suspicious patterns, like a large amount of similar, positive reviews. The AI may flag the listing as fake if abnormal patterns are present.

The AI system gives Amy’s transaction a high-risk score and passes this on to Etsy’s fraud detection scheme.

In this scenario, the Etsy team reviewed the alert and Amy’s account and determined it was fraudulent. Etsy then contacted Amy to verify the transaction. If the suspicion level was high, Etsy may choose to automatically block the transaction before contacting Amy.

The way in which e-commerce companies deal with potential fraud depends entirely on their own processes. Some will use remote manager software to generate alerts for authorized personnel teams, allowing them to take action quickly.

What are the Benefits of Using AI Fraud Detection?

AI combines the power of technology with human-led approaches, making it beneficial for detecting instances of fraud. Humans make mistakes, especially when analyzing large volumes of data. In comparison, AI can accurately identify fraudulent activity almost instantly and notify the relevant fraud detection teams.

Additionally, AI can assist in creating training materials, recommending resources, and conducting assessments to support ongoing training and education efforts within these teams. AI can also enhance your automated webinars by providing real-time interaction through chatbots, and offering adaptive learning experiences to participants,

Here are the main benefits of using AI for the purposes of detecting fraud.

Accuracy

AI algorithms can analyze large volumes of data with high precision, reducing (but still not eliminating entirely) false positives and negatives, which are common with traditional rule-based systems.

Real-Time Detection

AI enables real-time monitoring and detection of fraudulent activities, allowing for swift intervention and prevention of ongoing fraud. With the Vonage cloud VoIP phone system, AI algorithms can identify unusual phone call patterns to block or monitor fraudulent numbers.

Pattern Recognition

AI excels at recognizing complex patterns and anomalies that might be difficult for humans or traditional systems to detect.

Scalability

AI can handle large datasets and a high volume of transactions, making it well-suited for organizations dealing with a growing number of transactions.

Quick Detection

AI can identify potential fraud at an early stage, preventing larger losses and minimizing the impact on the business or financial organization.

Further Education

AI fraud detection software can help inform fraud teams to analyze and find instances of fraud. However, it’s essential to provide training for all team members actively working with an AI fraud detection tool. As a business, you may want to consider creating your own informative content on AI fraud prevention and strategies – not only for the benefit of your employees, but as part of a marketing strategy.

Transform your Fraud Detection Capabilities with Powerful AI Technology

AI has revolutionized fraud detection thanks to its capabilities in analyzing large amounts of data accurately and almost instantly. Rather than allowing humans to go through thousands of transactions to find fraud patterns manually, AI is automating the process of reporting fraud to the relevant parties.

The majority of large E-commerce businesses and financial institutions are already adopting AI to pinpoint instances of fraud in financial transactions to protect customers from losing their hard-earned cash. Join the movement and leverage AI technology as part of your fraud detection strategies.