Artificial intelligence, or AI for short, is not a new phenomenon. However, the last few years have seen this technology take the world by storm. From self-driving cars to virtual assistants like Alexa and Siri, AI is all around us today, and its application continues to expand.
Finance is one sector where AI has made serious inroads lately, specifically in the fraud detection segment. Mastercard, for instance, recently announced that it’s developing a proprietary generative AI model that could help the banks in its network improve their fraud detection rates by up to 300%!
Given that fraud is one of the most persistent issues that financial institutions face, could AI be the ultimate solution they’ve been waiting for all along? Could AI finally help put an end to financial scamming?
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This guide delves into this topic, examining how AI-powered fraud detection works, the types of fraud it can help detect, and the main benefits and risks of using AI to detect fraud.
Understanding AI Fraud Detection: What Is It and How Does It Work?
AI fraud detection refers to the use of artificial intelligence technologies to identify and prevent fraudulent activities within financial transactions.
One of the most commonly employed AI technologies in fraud detection is machine learning. This involves developing and training algorithms that can access a vast amount of data, analyze it, and autonomously learn from it. By identifying patterns within the data, these algorithms empower computers to make informed decisions or useful predictions.
For example, banks and other financial institutions can deploy machine learning algorithms that analyze various data sets, such as customer behavior, network activity, and transaction records, to identify anomalies or patterns that indicate potentially fraudulent activity.
As these algorithms are exposed to new and diverse data over time, they continuously learn from it and consequently become more adept at detecting fraud.
Types of Fraud That AI Can Detect
AI fraud detection systems can flag a variety of fraudulent activities, including the following:
Fake Accounts
AI can help prevent the creation of fake or synthetic accounts. Institutions can train it to spot patterns that indicate fraudulent behavior, such as abnormally fast form completion, use of proxy IPs, or similarity of account details to others previously flagged as fake. By identifying such patterns in real time, AI systems can flag suspicious accounts for further review or automatically block their creation.
Credit Card Fraud
Credit card fraud is when a criminal steals your credit card details and then uses them to conduct financial transactions, like paying for goods and services. It’s one of the most common types of financial fraud. For example, in 2022, people around the world lost a total of $33.45 billion to credit card fraud. Experts expect this figure to reach $43 billion by 2026.
AI systems can reduce credit card fraud. They can analyze customer transactions and flag those that deviate from typical behaviors, such as a high-value purchase in a foreign country, or a large number of transactions in a short time period.
Account Takeover
As the name suggests, an account takeover is where a fraudster gains illegal access to a user’s financial account to siphon funds or make unauthorized transactions. AI can detect this type of fraud by monitoring account anomalies, like logins from an unusual or unfamiliar location or a sudden change in account details.
Upon detection of suspicious activities, the system can trigger immediate protective actions, such as alerting the user, requiring authentication, or temporarily locking the account.
Money Laundering
AI can also help in detecting money laundering, which is another big issue that modern financial institutions usually have to deal with. This technology can analyze large volumes of transaction data and identify trends or patterns that are consistent with money laundering activities. They can then flag these transactions for further investigation.
Benefits of Using AI to Detect Fraud
Integrating AI into fraud detection systems or mechanisms presents numerous benefits. Here are the main ones.
Greater Detection Accuracy
AI’s decision-making process is based purely on data analysis. That means that it’s free from the subjective biases that humans might hold. This enhances the accuracy of fraud detection.
Real-time Time Detection
AI fraud detection systems process and analyze transactions in real time. That ensures that suspicious activity is caught as it happens, and action can be taken immediately.
More Efficient Use of Capital and Resources
AI-driven systems automate the process of detecting and flagging fraudulent transactions, significantly reducing the need for extensive manual oversight. This allows organizations to reallocate financial and human resources to other areas of need.
Decreased Organization Losses From Fraud
The ability of AI to quickly identify and prevent fraudulent transactions before they are completed can save organizations substantial amounts of money that would otherwise be lost to scammers.
Scalability
Unlike human-based fraud detection systems, AI systems are scalable — that is, they can handle an increasing volume of transactions without a corresponding increase in operational costs or a compromise in the quality of fraud identification. This scalability allows businesses to expand seamlessly, maintaining consistent fraud detection effectiveness and cost-efficiency regardless of size.
Risks and Drawbacks of Using AI Fraud Detection
AI offers several transformative benefits in the field of fraud detection, as we’ve seen. Unfortunately, it also comes with certain risks and drawbacks that institutions need to be aware of. Let’s look at a few of these.
Data Security Concerns
AI systems require access to vast amounts of personal and financial data to operate effectively. Unfortunately, this raises data security concerns as there is the risk of data breaches, where malicious actors can exploit vulnerabilities in AI systems to access confidential, private information.
False Positives And Negatives
Although AI can detect fraud with high accuracy, false positives (legitimate transactions flagged as fraudulent) and false negatives (fraudulent transactions passing as legitimate) remain a reality. A high rate of false positives or negatives can have a lot of unintended consequences. For instance, customers can quickly become frustrated if legitimate transactions are being flagged as fraudulent on a regular basis.
Lags In Adapting To Evolving Fraud Tactics
While AI systems are adept at learning from new data, there’s usually a time lag between the emergence of new fraud tactics and the system’s ability to recognize and respond to them. Fraudsters can take advantage of this time lag to carry out fraud.
Complacency Due To Overreliance On AI
There’s a risk that reliance on AI for fraud detection could lead to complacency, with firms potentially underinvesting in other critical areas of fraud prevention, such as personnel training and customer education.
Furthermore, an overdependence on AI could lead to vulnerabilities if the system fails or is compromised. Technical issues, such as hardware failures or software bugs, for example, can temporarily disable an organization’s fraud detection capabilities, putting them at risk.
Bottom Line
AI has the potential to revolutionize fraud detection. Through technologies such as machine learning, AI empowers financial institutions to detect fraud at a higher rate, with more accuracy, and at a lower cost than other methods.
That said, remember that preventing fraud starts with you. At Spokeo, we are committed to empowering you with critical knowledge and tools to enhance your digital safety. Subscribe to our blog today to always stay informed on the latest scams and how to protect yourself.
Sean LaPointe is an expert freelance writer with experience in finance and tech. He has written for several well-known brands and publications, including The Motley Fool, Finder, and CapLinked.
