Learn About AI Fraud & How AI Can Be Used For Fraud Detection

The artificial intelligence market is growing fast. Next Move Strategy Consulting projects that the market will grow from a value of $95,602.77 million in 2021 to $1,847,495.6 million by 2030. As artificial intelligence becomes more and more of a mainstay of modern life, it is worth taking a moment to consider the pros and cons of what it offers us.

While well-intentioned individuals leverage it to help reach their honest goals, many others abuse it for illicit gain. This is especially true in terms of how AI is being used to both fight fraud and facilitate it.

In terms of fighting fraud, Future Market Insights reports that AI in fraud management solutions revenue totaled around $6.5 billion in 2020. That figure is set to climb to $39.5 billion by 2031, with an impressive compound annual growth rate (CAGR) of 18%.

But what is AI fraud detection, and how can businesses best utilize it to prevent suspicious behavior online? Let’s look into the term and consider its many applications.

What Is AI Fraud? How Is It Used?

AI fraud is the use of artificial intelligence systems for fraud. Fraudsters use AI to deceive and harm individuals and organizations, accessing sensitive data and stealing funds. Examples of AI fraud include manipulating AI algorithms and models to create fake identities, generate false information, create phishing emails, conduct fraudulent transactions, and more.

With Precedence Research estimating that the global AI market size will grow from $119.78 billion in 2022 to $1,591.03 billion in 2030, AI fraud is likely to become an increasing headache for banks and businesses over the coming years. This means they need to keep pace, using a fraud detection system that also make use of the potential of AI. AI-powered machine learning algorithms, for example, can analyze data to spot patterns and anomalies in transaction logs or customer onboarding processes.

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Alongside its transaction monitoring applications, fraud detection AI systems can also be based heavily on qualitative data analysis, which is why machine reasoning can likewise be very relevant to AI fraud detection. For example, emerging AI technologies are allowing security firms to parse suspicious language in phishing attempts, or process natural language on social media for instances of adverse media coverage.

Ultimately, AI fraud detection exists to expedite the operations of the human fraud analysts who oversee it, as well as to develop fraud-fighting insights from data pools that are too granular for humans to notice. By using AI in fraud detection, some fraud prevention technologies can be largely automated, particularly if the rates of false positives and false negatives are acceptable for the organization’s risk appetite.

The data that AI fraud detection focuses on is largely relevant to transaction monitoring and user profile risk detection, but there are many emerging applications for AI-powered fraud solutions, such as adverse media scanning, anti-phishing checks, and crime surveillance.

How Is AI Used in Fraud Detection?

In fraud detection, AI is integrated into data analysis tools to flag risk indicators deep inside data pools, at digital speed. The resulting determinations are usually overseen by human fraud analysts. 

Transaction monitoring is one of the core applications of AI in fraud data analysis – especially fraud detection with machine learning. AI is used in transaction monitoring by analyzing granular transactional data and comparing it to historical data for anomalies that suggest fraud.

The AI machine learning algorithms are trained on historical data, learning the subtle differences between a good and bad actor. This way, suspicious behavior that is invisible to the naked human eye can be flagged.

This includes sudden changes in customer behavior, such as a customer suddenly going from consistently small purchases to buying lavish gifts, or a user with a screen size associated with multi-accounting abuse.

An example of how AI can be used to process and present quantitative data to humans for fraud detection

The above points all form a vital umbrella in AI-based fraud detection: behavioral analysis, wherein subtle things like computer mouse movements can be told apart from user to user, which is a crucial factor in detecting account takeover fraud.

Ultimately, AI is equipped to scrutinize very specific things that would not be practical for humans to do.

Is ChatGPT Fraud Possible?

Fraud can be committed and enhanced through the use of ChatGPT, but this only occurs if humans abuse its applications. ChatGPT is able to support fraud attacks because the software has content-writing capabilities that can be abused by fraudsters wishing to deploy phishing emails.

So, while ChatGPT can only be considered a fraud attack tool inasmuch as it can be misused by fraudsters, its natural language processing (NLP) capabilities do indeed threaten readers who are vulnerable to phishing attacks.

This is especially true when fraudsters ask ChatGPT to use threatening or emotive language – the textbook feature of a social engineering attack. In fact, it can even reduce the language barriers from attackers who aren’t native English users by using clear English grammar and spelling. This means phishing scammers are now less likely to give themselves away through poorly written English.

Common Types of Fraud that AI Detects

Thankfully, AI excels at detecting certain types of fraud. It is even able to do so in near real-time, meaning it can reach conclusions faster than human fraud analysts.

While there are many other examples, we’ll focus below on five key types of fraud that AI is able to detect.

Account Takeover (ATO)

An account takeover (ATO) attack is when a fraudster breaks into the target’s account, such as their online banking account. This is often achieved by compromising the victim’s login details, but it can also be done by hacking.

So how can AI fraud detection systems thwart ATO attacks? Through AI and device fingerprinting combined: AI can detect unusual device profiles, such as unrecognized browser apps on smartphones only sold overseas.

Card Fraud

Card fraud is the unauthorized use of a victim’s payment card, often by compromising either the target’s bank details or simply outright stealing their card and using its contactless payment feature in as many shops as possible before the cardholder gets wind of the attack.

AI systems can help credit card fraud detection processes by using pattern recognition to flag anomalies. If a user displays uniform spending habits for the entirety of their bank account lifespan, and one day that all changes, AI can recognize this abrupt discrepancy as suspicious and have a transaction monitoring system raise alerts.

Account Creation

Account creation, in the context of fraud, is the creation of a fake account – using either fabricated or stolen identities – that can be used to trick targets into disclosing their personal information.

Account creation can be used to increase the likelihood of successful phishing attacks, especially through fake social media accounts posing as reliable services.

AI can tackle this through machine reasoning features that can spot things like inconsistent location data and suspiciously similar passwords. For example, if an individual accesses an account from a different country to the registered home address, and is also logging into other accounts with similar passwords, AI can flag that person as suspicious.

Credential Stuffing

Credential stuffing is the use of multiple leaked or stolen login credentials that are entered by a malicious actor, bot, or misused API in one or more login forms online. The process is tried many times, on many sites, and with many credentials in the hopes that a login attempt will eventually be successful and to the detriment of vulnerable account holders’ security and privacy.

Credential stuffing can – and should – trigger multi-factor authentication (MFA) alerts on users’ devices, such as one-time passwords (OTPs). However, even OTPs can be bypassed by attackers trying multiple variations through yet further credential stuffing. AI is pivotal when this occurs as its device fingerprinting capabilities mean it can identify suspicious device activity, such as ultra-frequent login attempts that indicate the use of bots.

Betting Bots

Betting bots are software programs that can input gambling wagers on iGaming sites, such as online casinos, on behalf of one or more users. They can be misused by fraudsters who wish to carry out fraud attacks such as bonus abuse and match-fixing.

Fraud prevention AI can recognize misused betting bots through machine learning algorithms that track user behavior on iGaming sites. If an account is found to have a sudden spike in the frequency of online casino bets, AI may determine that no human alone could have found the time to do this manually.

The Benefits of AI Fraud Detection

The benefits of AI fraud detection are chiefly down to the speed, accuracy, and extra layer of security that AI offers to the overall operations of the fraud prevention industry.

Put another way, AI fraud detection is the core means by which fighting fraud is becoming a largely automated process that enjoys all the upsides that come with being such – efficiency, precision, and safety measures are all granted by the use of AI in fraud detection.

Let’s now take a look at three AI-friendly applications that help bring these benefits to the field of fraud detection.

Pattern Recognition

Pattern recognition is the process of determining the relationship that data points, such as transactions, share with each other. Such relationships can be either close or anomalous. AI systems can help determine whether closely related data points are signs of legitimate activity and whether anomalous data points are signs of suspicious activity.

Consider an iGaming account user who only ever logs in from their home address. If that same account is associated with an uptick in online betting from multiple IP addresses, this anomalous relationship in data points may be flagged as a suspicious activity to an AI system – and possibly even a sign of account takeover in iGaming.

Real-Time Threat Detection

Real-time threat detection (RTTD) is the hands-on use of AI fraud detection. Rather than involving software periodically looking at historical data sets and flagging suspicious activity for review, RTTD processes data on a live basis and can therefore detect threats in real-time.

An example of RTTD involves the use of multi-factor authentication. When a user signs into an account from a new device, AI is able to determine that the device has not been used to access that account before and inform the account holder with a “New sign-in attempt – was this you?” email.

If the user says that the account activity is unrecognized, the AI system can initiate its RTTD response by urging the user to reset their password, carry out biometric authentication, and so on.

The Risks of Using AI for Fraud Detection

Despite the benefits of AI in fraud detection, the caveat of relying on it is that you may end up having a false sense of security. AI-based fraud detection systems are, after all, liable to yield both false positives and false negatives if they’re neither tuned to the specific needs of the organization nor supervised properly by human fraud analysts.

AI shouldn’t be relied on without human supervision – there will always be situations that require the human touch. In fraud prevention, instances of customer behavior will arise that require a fraud team member to make the final judgment on a potentially fraudulent interaction.

When false positives arise because businesses have over-relied on automated fraud prevention systems, it may damage an organization’s reputation.

On top of this, many real fraudsters can learn how AI fraud detection systems work and use that knowledge to circumvent them. This leads to a rise in false negatives, which is all the more reason why organizations should keep their AI systems well-supervised and up-to-date.

How Can SEON’s AI Help Businesses Prevent Fraud?

SEON applies the use of AIs methodically and creatively to outsmart fraud. One of the core ways it achieves this is inside the machine learning algorithms, training the models to pick out suspicious data points, and ultimately stopping fraudsters.

The blackbox (non-human-readable) and whitebox (human-readable) machine learning algorithms make their decisions with a model that best suits a variety of transaction and profile data sets. SEON comes with prepackaged rules tuned with our own extensive fraud training data, that suit the needs of most businesses.

While the software can begin using these blackbox machine learning rules to detect fraud right out of the box, SEON also needs to be trained on historical data to begin its process of recommending human-readable rules through its whitebox ML algorithms.

After implementation, SEON’s AI continues to make rule recommendations to enhance your equipment further still. You can implement these rules with just a few clicks.

To enable businesses to hit the ground running, SEON’s AI capabilities show the best of both worlds through blackbox and whitebox machine learning. It carries out the non-human readable calculations in the background while also making sure the human-readable decision-making reaches you in the foreground.

Ultimately, while it may use artificial intelligence, there’s nothing artificial about SEON’s intelligence!

Frequently Asked Questions

How does AI affect fraud detection?

Compared to human fraud analysts, AI is much better at pulling out inconsistencies and anomalies deep inside numerical data that could indicate malicious behavior. 

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Author avatar
Bence Jendruszak

Bence Jendruszák is the Chief Operating Officer and co-founder of SEON. Thanks to his leadership, the company received the biggest Series A in Hungarian history in 2021. Bence is passionate about cybersecurity and its overlap with business success. You can find him leading webinars with industry leaders on topics such as iGaming fraud, identity proofing or machine learning (when he’s not brewing questionable coffee for his colleagues).

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