Cathy Ross, the finance and tech expert behind Fraud.net‘s AI-powered risk management platform.
An invisible war surrounds us: cybercriminals versus the security of data and money. This battle has been a constant since the inception of the computer age, with cyber threats evolving in sophistication and scale. Fraud schemes cost Americans $10.3 billion in 2022, according to the FBI’s Internet Crime Report from that year. Phishing was the most common online attack, and investment schemes were the most costly.
Cybercrime costs are projected to top $10 trillion by 2025. Cybercrime Magazine notes: “More than half of all cyberattacks are committed against small-to-midsized businesses (SMBs), and 60 percent of them go out of business within six months of falling victim to a data breach or hack.”
The Rise Of Artificial Intelligence
But there is a new weapon at our disposal: artificial intelligence. This futuristic technology promises companies a new level of defense against hackers, right?
Unfortunately, the answer is not so simple.
Yes, AI-powered fraud detection systems, particularly machine learning algorithms, can analyze vast amounts of data swiftly and accurately. These algorithms can detect anomalies and patterns that human analysts might miss, increasing the early detection of fraudulent activities. Whether credit card fraud, identity theft or account takeovers, AI systems can recognize suspicious behaviors and transactions in real time.
However, victory is not certain in this ongoing cyber war. AI has limitations, and hackers can also use it to their advantage.
So, can businesses use AI to truly safeguard their data effectively? Let’s take a look.
Protecting Consumer Data
Here’s how businesses can use AI-powered fraud detection to protect consumers’ data from unauthorized access and misuse:
1. Real-time detection: AI systems can monitor transactions in real time, flagging unusual activities that may indicate fraud. This rapid response helps prevent fraudulent transactions before they can harm consumers.
2. Behavioral analysis: AI can analyze individual consumers’ behavior patterns, creating profiles of normal activity. Deviations from these patterns trigger alerts, enabling prompt investigation and intervention.
3. Adaptive security: Unlike static rules-based systems, AI can adapt to new types of fraud as they emerge. This adaptability is crucial in an environment where cybercriminals continually innovate their methods.
4. Reduced false positives: Traditional fraud detection systems often generate false positives, inconveniencing legitimate users. AI’s precision in distinguishing between genuine and fraudulent transactions can help minimize these errors.
5. Enhanced privacy measures: AI systems can anonymize and secure sensitive consumer data, reducing the risk of data breaches and identity theft.
AI Uses To Watch Out For
On the flip side, AI algorithms used to protect consumers can be programmed to automate and enhance malware in multiple ways to steal their data. Here are a few:
1. Advanced phishing attacks: AI can generate convincing phishing emails by analyzing behaviors and crafting messages that evade traditional spam filters.
2. Credential stuffing: AI-powered bots can swiftly test stolen credentials across multiple platforms, exploiting reused passwords to gain unauthorized access.
3. Data poisoning: Hackers can feed an AI algorithm altered or “bad” data, negatively impacting its output.
4. Automated social engineering: AI algorithms can analyze social media and other publicly available data to create targeted social engineering campaigns, tricking users into divulging sensitive information.
5. Predictive attacks: AI can predict patterns in user behavior, facilitating more precise timing for attacks, such as intercepting financial transactions or compromising personal data.
6. Deepfakes: AI can easily alter audio and video content, making it difficult to tell what—and who—is real. The manipulated content can go viral, stoking fear and uncertainty.
Other Limitations Of AI
In addition to being used maliciously, AI comes with additional challenges. Machine learning models are prone to degradation over time.
Then there is the “black box” problem: the inability of software developers to understand exactly how deep learning systems make their decisions. Unwanted outcomes, therefore, are difficult to fix. This could apply to self-driving cars that do not perform as expected, or to a complex judgment call of who should get approved for a loan or medical treatment. There is a very real danger of AI perpetuating human biases that are disadvantageous to individuals or demographic groups.
This technology relies on large datasets to train itself effectively. That data often comes from what’s readily available online, meaning content created by writers, artists, journalists, graphic designers and anyone using social media. Meta, the owner of Instagram and Facebook, was forced to pause efforts to mine European and U.K. users’ public posts to train its AI after backlash from the Irish and U.K. governments. In the U.S., Meta was not required to notify users. Questions and concerns abound over privacy and the ethical use of this data, eroding consumer trust.
Pending Legislation
The primary federal law for prosecuting cybercrime in the U.S. is the Computer Fraud and Abuse Act of 1986.
Two new relevant bills have been proposed: H.R.7156, the Combating Money Laundering in Cyber Crime Act of 2024, is a bipartisan effort that would give the Secret Service expanded authority to investigate financial crimes. And S.3205, the Federal Artificial Intelligence Risk Management Act of 2023, which includes language stating that federal agencies must use a framework developed by The National Institute of Standards and Technology to improve AI systems’ security.
The Human Factor
As technology continues to evolve, so must businesses’ methods of safeguarding data. With its ability to adapt and learn, AI can and should play a pivotal role in the ongoing battle against fraud.
But despite AI’s capabilities, human oversight remains critical. Employees need continuous training to recognize AI-generated deepfakes and sophisticated phishing attacks. Human analysts provide crucial context and decision-making capabilities that complement AI. I’ve found the most effective fraud prevention strategies involve a hybrid approach, where AI augments human expertise rather than replacing it entirely.
AI-powered fraud detection systems represent a significant advancement in protecting businesses’ and consumers’ data from fraud and unauthorized access. By leveraging machine learning and real-time analytics, these systems can detect and mitigate fraudulent activities swiftly and effectively. However, their implementation must be accompanied by robust privacy measures, ethical considerations and human oversight to maximize their efficacy and ensure consumer trust.
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