With scams and fraudulent activity growing at an alarming rate, it is important to have an effective way to combat and stay ahead of cybercriminals. Is it even possible to fight back? How can we identify and prevent this type of fraud before it happens?
While it might not be possible to eliminate this type of cybercrime, AI detectors can be helpful in decreasing the amount of online fraud. AI detection has become an accurate and effective tool in mitigating online scams.
How Do AI Detectors Help Identify and Prevent Fraud?
AI detectors analyze enormous volumes of data in real time. They use machine learning models, which allow computers to learn without a human telling them exactly what to do and how to do it.
Historically, computers operated by a set of fixed rules. Now, machine learning enables them to detect patterns and adapt to advancing fraud tactics by continuously learning from new data.
Fraud-detection software is used in many industries. Here are a few examples of AI-driven fraud detection tools.
AI Detectors Preventing Fraudulent Activity
An AI detector is handy for identifying AI-generated text and images. This tool has a multitude of uses, including detecting fraud and phishing scams. A user can input the information from the phishing scam, such as the phone number or copy-and-pasted text from an online message, and determine if it is from a legitimate source.
These tools can also help to detect anomalies in datasets to determine if there is some sort of discrepancy that could indicate fraud. This is a tool commonly used in financial institutions.
Advantages AI detectors offer include automated protection initiatives, enhanced security, and fewer false alerts. Machine learning can also predict new scams and patterns that may emerge to help build up protections against scams and fraud in the future.
Let’s take a look at some of the commonly used tools available for protecting consumers and financial institutions.
Mastercard Consumer Fraud Risk
Banks commonly use Mastercard’s Consumer Fraud Risk tool to prevent Authorized Push Payment (APP) fraud. This tool can analyze real-time data to determine in seconds if there is suspicious activity. It evaluates patterns and alerts financial institutions if there’s a potential problem so they can take action to shut down fraudulent activity before it gets out of hand.
The Consumer Fraud Risk tool is just one of many AI-friendly tools put into place to protect both banks and customers from unnecessary loss.
Cifas
Cifas is another AI tool that protects businesses and their employees from identity theft, account takeovers, false claims, and more. It’s basically an expansive database of fraudulent activity where members can run reports and determine if suspicious activity is indeed fraud.
The AI tools used to detect and prevent fraud continue to emerge as scams get more complex. These tools and others like them are helping to identify fraudulent activity before it happens.
Online Scams and Fraud Continue to Grow
Fraudulent activity on the internet continues to grow. As technology evolves, so do scamming methods. Just as AI has streamlined workflow and improved efficiency in businesses worldwide, it has also automated tasks and reduced errors for many items on a scammer’s to-do list.
Common types of online fraud
Identity theft: stealing personal information to use it for criminal purposes, like taking out a loan or traveling internationally
Investment scams: promise high returns with little to no risk (well-known examples include Ponzi schemes and pyramid schemes)
Credit card fraud: unauthorized use of a person’s credit card information
Phishing: emails, text messages, or websites that trick people into giving away sensitive information like social security numbers and bank information
Online shopping scams: payments are taken, but products are never delivered
Tech support scams: often appear as pop-ups that say your computer has a virus and that their tech support can fix it for a fee
Fake charity scams: popular after natural disasters because people will more readily donate; however, the money will never reach those in need
Lottery/sweepstakes scams: victims are told they’ve won money and that a fee is required to claim their prize
Social media impersonation scams: fake profiles are created to trick others into giving away money or personal information
Deepfake scams: realistic AI-generated audio, images, or video used to commit fraud
Romance scams: scammers pretend to be romantic partners to gain trust and then request money
This is just a small sampling of the many scams arising these days; as a result, traditional methods of fraud detection struggle to keep up. The most common traditional fraud-detection tools include manual review and rule-based systems. It’s important to understand how these two systems work.
A rule-based system emits an alert or predetermined action when a threshold of criteria is met within a computerized system. For example, a bank or credit card company might flag a transaction that exceeds a certain amount. A manual review involves a human cybersecurity professional who analyzes data sets, looking for fraudulent activity.
However, due to the constant change and adaptation of fraudulent activity, it is becoming increasingly difficult to combat it without the help of AI.
Scammers Are Tapping Into the Power of AI
Artificial intelligence can be a powerful and efficient copilot for scammers. For example, AI has optimized the workflow of phishing fraudsters, allowing them to create multiple versions of a phishing email in minutes. AI has vastly improved the grammar, spelling, and context relevance of these emails, making them harder to identify.
In a similar way, if AI can create a customer service chatbot, you can expect that fraudsters can use that same technology to create a similar bot. AI can send quick responses to social media direct messages, text messages, and emails. Unfortunately, scammers are making use of this technology.
One harrowing example of scammers using AI to enable fraud involves deepfake scams. Deepfakes have become a popular tactic used to impersonate another person (or fabricate a new one). They are images, videos, or audio that depict real or nonexistent people, usually to obtain personal information or to trick people into authorizing financial transactions.
They’re called deepfake scams because deep-learning algorithms create the fraudulent images, videos, or other content. If the scammer is going to impersonate a particular person, they’ll need as much data on the person as they can get, and the deep-learning algorithms provide that data.
AI Detectors Can Help Us Fight Back
How can AI help us beat cybercriminals at their own game? With the use of AI, of course! AI detectors can highlight fraudulent activity and stop scams where they start. Let’s look at a few examples of how we can use AI to accomplish this task.
Anomaly Detection
This method of detection looks for unusual patterns that might indicate fraudulent activity. Examples of anomaly detection include the following:
identifying differences in spending patterns
typing speed
login locations
mouse movement
types of links
physical locations
browsing behavior
IP addresses
devices
use at odd hours
Anomaly detection features can flag content for human review, automatically remove certain types of content, and even block malicious emails before they reach someone’s inbox.
Natural Language Processing
This technology enables computers to understand, interpret, and generate human language. You can find it in the application of translation software, chatbots, search engines, and sentiment analysis.
In the context of scam prevention, it’s useful for picking up certain keywords, language patterns, and sentence-structure discrepancies. For combating AI that creates phishing content, natural language processing technology is a perfect fit.
Biometric Verification
Facial, voice, and fingerprint recognition verify whether an individual is who they say they are. This form of AI detection is particularly useful for identity theft prevention. Many financial institutions throughout the world use this technology to protect their customers' assets.
For example, if you call your bank, you may need to provide a voice-activated password. The integrated AI will identify the specific notes in your voice to ensure it’s really you.
More and more mobile apps also require facial identification before you can log in to your account. Rather than asking for a username or password, they ask to see your face before granting you access.
Because biometrics are unique to the individual, most institutions view these measures as more secure. It’s much more difficult to imitate or steal biometrics than passwords and usernames.
Computer Vision
Computer vision essentially means that computers can “see” in a similar way to humans. It uses machine learning to train algorithms to recognize anomalies and features in audio, images, and videos. Computer vision looks for unnatural facial features, body movements, and speech patterns.
Fraud analysts often employ computer vision to identify deepfakes that impersonate individuals and trick others into authorizing payments and making bank transfers. Computer vision can also help spot deepfakes that spread fake news, engineer election results, and manipulate public opinion.
Risk Scoring
Machine-learning models assign a numerical value to risks. A higher risk score might be assigned to certain transactions or user accounts. Factors for risk scoring might be based on account history, transaction amount, location, or transaction frequency.
Effective risk scoring requires volumes of data, making the use of artificial intelligence data gathering tools essential. If used correctly, this information can help companies and individuals assess security threats and make changes where needed.
How AI Detectors Are Still Limited in Fraud Detection
AI detectors are created and trained by humans. Of course, humans aren’t perfect, so neither are AI detectors. They do occasionally have blind spots, reflect bias, and produce false positives and negatives.
Additionally, AI detectors can be costly, especially when needed on a large scale. Continuous model training is expensive and time-consuming, and requires frequent human oversight. False positives can be frustrating for clients and drive legitimate customers away, in addition to scammers.
Fraudsters are constantly evolving to avoid detection. As technology evolves, they try to stay a few steps ahead of the trend. Likewise, AI requires frequent updates and training to try to keep up with these swindlers. It’s not uncommon for fraud to regularly remain one step ahead of the systems put in place to stop it.
Ethical Considerations in AI Fraud Detection
Privacy protection is a major concern with all AI-powered systems. Due to the large volume of sensitive user data being analyzed, security concerns are warranted. Data collection, data retention, and surveillance are some of the more pressing concerns.
Other worries include potential bias and the unfair targeting of certain groups or countries. For example, AI could unfairly flag transactions from a specific country due to biased historical data.
Transparency is of paramount importance. Humans are not capable of combing through the amount of information AI can, and thus will not always be able to determine why something was flagged. This makes those decisions hard to appeal.
What is being done to calm these fears? Here are some examples of possible solutions to privacy concerns.
Data-protection laws: Governments can enforce AI regulations to ensure the ethical use of data. Prominent examples include the European Union’s comprehensive General Data Protection Regulation (GDPR) and California’s Consumer Privacy Act (CCPA).
Privacy-preserving AI techniques: These include using data for training without sending data to the central server, adding noise to the data so that it’s useful for training but harder to see identifying information, and removing personal identifying information before using the data to train the model.
Privacy policies: Users can access a comprehensive policy that illustrates what information is kept private and what is shared.
End-to-end encryption: Protects data in transit and storage.
Collecting only necessary data: Websites are now required to inform users of how they’re using cookies to collect data. Users can opt in or out of excessive data collection so that only necessary data is used to run the website.
Data retention limits: Regular deletion of old data means there’s less information out there for AI fraudsters to access.
Requests that users consent or opt out: Whether it involves cookies or email chains, the ability to opt in or out of data collection and personal information use is important for preventing fraud.
Regular security audits: This tool looks for bias, weaknesses, and unfairness within systems and can help prevent security breaches.
Human Moderators: Are They Still Needed?
Until recently, businesses have been forced to employ a large task force to keep their cyberspace secure. Emerging AI innovations have optimized workflow for cybersecurity professionals.
Although we will likely always need a combination of human and AI moderation within cybersecurity teams, AI has automated a large portion of the tasks of these teams. AI detectors can be trained on internal organization data, but they can also receive training on external data, improving their accuracy and ability to identify and prevent fraud.
AI detectors are indispensable. However, they’re still imperfect. With AI detectors occasionally flagging content or transactions wrongfully, human moderators still need to review the content flagged by AI. Together, humans and AI increase the likelihood of fraud detection.
The expectations of the public are continuing to rise when it comes to data protection. For example, social media platforms must identify and remove terrorist recruitment messaging almost instantly. It’s likely that in the future, large businesses will be required to implement AI-powered crime detection software due to the ability to analyze astronomical amounts of data.
How Will AI Fraud Detection Tech Evolve in the Next Decade?
AI fraud detection technology will evolve over the next decade, getting faster and more accurate. This will continue to reduce the time it takes to identify and respond to fraud. Companies might even be able to detect a vast number of scammers within seconds, before customers are ever affected.
How Might AI Fraud Detection Change?
Biometric verification will add keystroke dynamics to its quiver of arrows. Keystroke dynamics analyze your typing speed and patterns to protect your identity.
Cross-industry fraud detection collaboration will become important. Businesses from multiple industries will band together in an effort to use even more data to stop scammers before they can escalate their actions.
AI fraud detectors will be able to detect both complex and subtle fraud techniques. They might even learn to detect patterns in fraudsters' behavior, thus predicting fraud before it happens.
Personalized fraud protection will improve. For example, if you travel somewhere new, multi-factor authentication will automatically be triggered.
False positives will be significantly reduced. This will also reduce customer friction, thus improving customer satisfaction.
AI will get better at doing its job with less human oversight.
How AI Detector Can Help Combat Online Fraud and Scams
AI Detector is constantly updating its detection models to ensure accuracy. It offers detection capabilities for all the major AI models, including ChatGPT. AI Detector specializes in text and images. You can plug in text and images from anywhere, including websites, emails, and text messages, and identify AI-generated content.
This is especially useful in the case of phishing, but can be used to detect AI-generated content for many types of fraud. Tech support scams, fake charity scams, romance scams, and more may all have text or images generated by AI that might indicate fraudulent activity.
