How is AI Affecting Fraud?

AI is driving a new wave of fraud. The volume of phishing emails jumped 1,265% since late 2022 and credential phishing has grown by 967%, in large part due to the proliferation of generative AI. Financial institutions alone could lose up to $40 billion by 2027 due to generative AI-enabled fraud. Deepfakes and other AI-driven scams are making it harder than ever to spot fraud. What does it take to stay ahead?

Keep reading to learn how AI is shaping online fraud and how businesses can fight back.

How is AI Affecting Fraud?

AI is making fraud faster, smarter, and harder to catch. Fraudsters are using generative AI to create convincing phishing emails, fake videos, and automated scams that look and feel real. Because of this, traditional fraud detection tools can’t keep up, and allow AI-powered fraud to slip through without detection.

This leads to more sophisticated attacks, faster execution, and a bigger challenge for businesses relying on outdated defenses. Here are some more ways AI is affecting fraud:

• Continuous Adaptation: AI-powered bots can analyze and adjust their behavior in response to fraud detection systems, making them harder to detect and stay active longer.
• Behavioral Mimicry: Fraudsters train AI models to mimic human behavior, such as natural navigation patterns or typing rhythms, making fraudulent activity harder to distinguish from legitimate users.
• Fraud Networks: AI strengthens fraud networks by connecting stolen data from various breaches, linking accounts, and uncovering shared vulnerabilities like payment details and passwords. 

How Fraudsters Use AI

AI is reshaping digital fraud, using generative AI to create fake identities, documents, and even manipulated audio and video to appear legitimate. At the same time, GenAI helps fraudsters analyze vast amounts of data, identifying vulnerable targets for their scams. Some common uses of AI in fraud include: 

• Automating Large-Scale Attacks: AI allows fraudsters to execute highly automated schemes, such as account takeovers and payment fraud carried out by bots. It also automates spear-phishing campaigns by generating massive volumes of personalized emails.
• Creating Synthetic Identities: AI tools can generate realistic identities by combining real and fabricated data, enabling fraudsters to bypass KYC checks. These synthetic identities are used to open accounts for money laundering and/or fraudulent credit applications.
• Analyzing Security Systems: Fraudsters can use AI to test the vulnerabilities of fraud prevention systems, finding ways to bypass security measures. Adversarial AI techniques tweak fraud patterns just enough to evade detection.
• Social Engineering Attacks: Fraudsters can use generative AI to make realistic fake videos and voice recordings, commonly known as deepfakes, to deceive victims. In fact, according to McAfee, 70% of people can’t be sure if they’re talking to a real voice or an AI-generated fake.
• Running Fraudulent Activity Anonymously: AI anonymizes attacks, allowing fraudsters to operate globally without exposing their methods or identities. AI-powered proxies and VPNs hide location data to distribute attacks to anonymize attacks.

Top Challenges in Stopping AI-Powered Fraud

AI’s decision-making processes are not completely transparent, so we can’t always explain why AI arrives at specific conclusions, making it difficult to anticipate its future actions and control its behavior. Several similar challenges limit our ability to stop AI-driven fraud, including: 

• Limitations of Data Quality in Fraud Models: Fraud models learn from data, but can sometimes lack information about novel fraud schemes. Traditional fraud prevention tools struggle to detect these because they haven’t seen them before. 
• Evolving Fraud Tactic: Fraudsters continuously study fraud protection systems to find new ways and tactics to avoid detection. For example, bot networks are known to change behavior faster than fraud prevention models can be updated.
• Balancing Detection Precision with False Positives: Systems sometimes mislabel legitimate transactions as fraud, disrupting businesses and customers.

Solutions to Address AI Fraud

The impact of AI on fraud and the rapid emergence of AI fraud trends require completely new approaches to stop evolving scams. Here are some key solutions to tackle AI fraud effectively:

• Deploying Counter-Adversarial AI: Implement advanced AI models to detect and intercept fraud as, or before, it happens. These systems should target the techniques fraudsters use to exploit detection gaps. If AI can be used to mimic human behavior, it can be trained to detect such behavior.
• Improving Data Quality for AI Training: Establish systems that supply high-quality, validated data from diverse sources to train AI. Preprocess and verify the data thoroughly to minimize biases and ensure reliable outputs.   
• Building Adaptive AI Systems: Create and deploy AI fraud detection systems that are continuously trained using new data. This helps AI fraud detection systems to adapt to emerging online fraud tactics. 
• Refining Risk Scoring with Behavioral Analytics: AI’s advanced risk intelligence analyzes anomalies in session behavior, such as unusual device changes or failed logins, making it easier to flag fraudulent activity without disrupting legitimate users. 
• Aligning AI Use with Privacy Laws: Use AI fraud detection systems that adhere to privacy laws such as GDPR and CCPA without losing accuracy and effectiveness. Techniques such as anonymized behavioral analysis allow fraud detection systems to identify threats while respecting user privacy and legal constraints.

Why Sift is the Best Solution for Combatting AI Fraud

Criminals now use AI to create hard-to-detect fraud, such as fake identities and attacks that change tactics to bypass defenses. Companies need new transparent systems with better data and adaptive AI to deal with this high-tech crime wave.

Sift offers AI-powered solutions that evolve to match new fraud tactics. The platform uses transparent data analysis to catch fraudsters while protecting trusted customers. Aside from protecting against payment fraud, Sift offers various features and benefits, including: 

• Global Data Network: Sift processes over a trillion events every year. The platform identifies new fraud patterns across different industries in milliseconds. This network enables all customers to defend against fraud and protect each other collectively.  
• Machine Learning Models: The platform’s machine learning models dynamically adjust to apply more or less friction based on risk levels. The aim is to balance stopping fraud while allowing trusted customers a smooth experience.
• Instant Protection: Sift detects and instantly blocks fraudulent activity as it happens. It also supports easy and fast sign-ups and transactions for legitimate customers. 
• Proactive Fraud Prevention: With proactive prevention, Sift detects possible fraud and stops it before it happens. The platform uses end-to-end solutions to handle advanced fraud tactics. 

Request a demo to learn how Sift’s AI-powered solutions can help protect your business from evolving fraud.