Many companies and businesses have been offered artificial intelligence fraud detection as the final solution against the bad actors. But how does AI in fraud detection truly work? Why is relying on AI so advantageous?
Ever since it’s been widely available, AI has been used for the good, the bad, and the ugly. While companies can use it to automate their workflow and tasks, fraudsters or organized groups can also use it to launch a fraud attack.
So using artificial intelligence for fraud detection can help secure businesses and combat the same AI that is being used to commit numerous frauds. This article will explain the power of AI fraud detection and its benefits.
Advantages of AI in Fraud Detection
Fraud detection using AI can give some serious benefits to businesses that can’t be seen on older fraud detection systems anymore. Ranging from better accuracy and real-time protection to adaptability mechanics.
Enhanced Accuracy
Utilizing artificial intelligence in fraud detection can enhance the precision of identifying potential fraud attacks. But how could such systems manage to figure out which is fraud and which is not? These are AI’s capabilities:
- Learn and analyze massive datasets at a faster rate. AI can be trained with a vast amount of models and references to create an accurate fraud prevention that works efficiently.
- Cross-reference data from multiple databases. An example of this can be seen from electronic KYC (Know Your Customer), which is able to check multiple sources to validate identities accurately.
- Accurate pattern recognition compared to humans. AI performs better with recognizing patterns because of their capabilities. It shows that they can replace traditional systems that might overlook a far more complex pattern.
Real-Time Detection
Traditional fraud detection simply cannot overcome the brute force of AI-powered fraud. These frauds can optimize their attacks in real time, creating synthetic identities and mimicking real sites to bait victims.
Unlike traditional methods, AI in fraud detection works in real-time by monitoring each transaction or activity by every user. Each interaction is monitored and compared with their own normal user behavior.
Should a fraud occur, AI can spot and flag it the moment it happens before it can cause any real damage. Companies can now protect their users and minimize losses.
Reduced False Positives
AI implements machine-learning models to learn complex patterns from datasets. Previously, older fraud detection used rule-based systems to flag any transactions based on the rules implemented by the companies.
However, this system was prone to false positives since transactions were more complex. With the use of AI, false positives could be minimized since AI is capable of recognizing patterns rather than just relying on rules.
Scalable and Customizable
The growing landscape of AI-powered fraud, as well as the digital services themselves, has forced businesses to keep up. But with the help of AI, this becomes a cakewalk, considering how scalable and customizable it is.
For instance, AI can be scaled to keep up with growing transaction numbers, which results in more diverse datasets to learn. Artificial intelligence can also offer customization like customer identity journeys for accurate analysis.
Continuous Learning and Adaptation
AI-powered fraud is constantly evolving. Fraudsters find more ways to find vulnerabilities and loopholes, exploiting them to perpetuate fraud. Thankfully, that’s the exact same reason why AI can also prevent them.
Just like AI-powered fraud, artificial intelligence in fraud detection also adapts to these new threats. The scalability factor of AI ensures it can evolve and update its threat databases to combat newer frauds.
Also, AI fraud detection continuously learns from every new transaction’s data and user behaviors. This cements even further that AI is able to detect emerging new threats and trends, ensuring users stay protected.
How AI Detects Fraudulent Patterns in Mobile Device Data
With mobile becoming the daily driver for transactions, companies and businesses should look for mobile fraud detection solutions. This is AI fraud detection in mobile services with deeper analytics.
Rather than just analyzing user behavior, this system can also identify the mobile device that is being used in transactions and activities. Here’s how an artificial intelligence could make that possible:
Behavioral Analytics
As previously explained above, AI in fraud detection is capable of analyzing behavior from every transaction or activity. These technologies found in mobile fraud prevention adapt the same concept to protect users:
- Run-time Application Self-Protection (RASP). This AI-driven technology analyzes the device to determine if there is unusual behavior in the device itself. It shuts down the mobile app immediately if there’s any detection before user data can be compromised for fraud.
- Risk-based authentication (RBA). This AI-driven technology allows analyzing customers and determining their risk levels based on their activities. Users may need to validate further using several factors to confirm their actions if their risk level is high.
- Behavioral profiling. This AI can gather data and analyze customers, creating information for behavioral profiles. This profile can be used to determine whether an user’s current action is anomalous or genuine.
Anomaly Detection
One of the capabilities of artificial intelligence for fraud detection is that they can detect anomalies by using machine learning and behavioral analytics, as explained above. They spot and flag outliers in real time.
This real-time fraud detection works the same way for mobile devices, where even a new device or location can mean a potential fraud attempt. This strict system is superior to the traditional rule-based system that often needs to be manually reviewed.
Pattern Recognition
Any mobile services that comply with AML (anti-money laundering) laws must be able to recognize a pattern of money mule networks. This is an organized fraud where several people transfer laundered money from one bank to another to avoid being traced.
Being better at pattern recognition, AI can detect a ring of groups interlinked by common details, such as the same IP address, locations, and similar phone numbers. Such technology could help investigators find a link to any money laundering chains.
Automated Response
To prevent zero-day vulnerabilities, responses against emerging threats in mobile apps must be done quickly. This is where mobile fraud prevention using AI can save the day.
For example, AI can perform automated patching to close security gaps before the app can be exploited. It can also separate the affected devices, ensuring that the damage won’t spread even further.
In conclusion, AI in fraud detection provides a significant impact on security, which is why implementing it is a must. Interested? Our AI-powered fraud prevention Keypaz is ready to help protect businesses from now on and forever. Try it today!