As AI technologies advance at breakneck speed, cybercriminals are weaponizing these same innovations for online fraud schemes. Today’s fraudsters deploy machine learning algorithms to create convincing deepfakes and engage in phishing campaigns.
The financial impact of online fraud by AI is staggering. Businesses and consumers are now losing more money than ever due to digital scams. To learn more about artificial intelligence and its threats, continue reading.
How Does Artificial Intelligence Perpetuate Online Fraud?
AI has its drawbacks just like every other technological innovation. It can perpetuate online fraud by:
1. Automated Vulnerability Discovery
AI systems continuously scan digital infrastructure to identify exploitable weaknesses before human security teams can patch them. Machine learning models analyze the following things for vulnerabilities:
- Code repositories
- API responses
- Network traffic
These systems autonomously develop exploitation techniques and deploy them against thousands of targets simultaneously.
The automation enables rapid capitalization on newly discovered security flaws, bypassing traditional security measures during the critical window between discovery and patching.
2. Deepfakes For Bypassing Identity Verification
Artificial intelligence is being widely used to create deepfakes, such as highly authentic images of a person on the internet.
These deepfakes can be used to verify identification documents and bypass security measures on various platforms. Because of such actions, it has become easier for people to hack someone else’s bank accounts.
Cybercriminals can also use this deepfake technology to generate AI voice models to conduct phishing scams.
3. Getting A Fake ID For Scams Is Easier With Artificial Intelligence
Advanced AI tools are also being used by teenagers to create fake IDs that closely replicate government-issued IDs.
These uses of AI are helping people perpetuate online fraud by allowing them to create fake profiles or bank accounts to scam e-commerce sellers.
Differentiating fake IDs from real IDs has also become harder with AI innovations. This is why one must be more alert than ever during online transactions to avoid losses.
How Does Artificial Intelligence Help You Combat Online Fraud Threats?
Every technological advancement including AI, has a good side too. Generative tools may perpetuate fraud, but they can also be used to combat fraud if used responsibly.
1. Advanced Machine Learning Techniques
One way businesses and apps can use to combat online fraud using AI is through machine learning. Training algorithms using vast data is useful in recognizing malicious patterns during cyberattacks and suspicious transactions to detect fraudulent activity.
Machine learning in the financial industry can also allow companies to use AI tools to determine whether a transaction occurring on their platform is fake or authentic. This can help them protect themselves against scammers using fake bank accounts to purchase their services.
2. Data Mining And Analysis
Data mining is the process of discovering patterns and knowledge from large datasets using statistical analysis, machine learning, and database systems to inform business decisions and predict trends.
It can help combat online fraud threats due to the unethical use of AI. Extracting reliable information by analyzing various data sets by AI algorithms can help detect patterns that a human expert may miss.
AI can also identify data patterns and correlations faster than humans. This is why developing AI algorithms for data mining can help various agencies with anti-fraud reporting.
3. Artificial Intelligence Scoring Systems
AI-based scoring systems such as Omniscore are also useful for tagging financial transactions as authentic, suspicious, or fake. Such systems reduce the chances of false positives, adding to reliability.
These methods also improve customer experience by ensuring authentic transactions aren’t unnecessarily flagged and stopped. AI scoring systems detect fraud by:
- Analyzing transaction patterns and behavioural biometrics
- Establishing baseline normal behaviours and flagging anomalies
- Using machine learning to adapt to evolving fraud techniques
- Assigning risk scores to transactions in real-time
- Reducing false positives with contextual data integration
- Implementing multi-layered verification for suspicious activities
These systems significantly reduce financial losses and maintain customer trust in digital commerce.
To Sum It Up
Combating online fraud will only be beneficial if your company is using a reliable AI algorithm. People believe that AI doesn’t make mistakes, but that is a misconception.
AI must learn and develop with time. Sometimes, AI algorithms may give false positives on authentic transactions, tagging them as suspicious, or they may not detect some suspicious behaviour at all.
Combining AI algorithms with device intelligence methods and human oversight is the best way to overcome the earlier challenges.