Privacy Concerns
In many cases, real data cannot be used to train AI due to privacy issues. Instead, fake data is created based on real data, which can lead to inaccuracies and lower performance in the AI system.
AI privacy refers to the protection of personal or sensitive information that is collected, used, shared, or stored by AI systems. One reason AI poses a greater data privacy risk than other digital technology is the sheer volume of information AI needs to be trained on: terabytes or petabytes of text, images or video which often includes sensitive data such as healthcare information, personal data from social media sites, personal finance data, and biometric data used for facial recognition.
As more sensitive data are being collected, stored, and transmitted, the risks of exposure from AI models rise.“ This [ data ] ends up with a big bullseye that somebody’ s going to try to hit,” Jeff Crume, an IBM Distinguished Engineer, explained in an IBM Technology video.
Data leakage from an AI model can occur through the accidental exposure of sensitive data, such as a technical security vulnerability or procedural security error. Data exfiltration, on the other hand, is the theft of data. An attacker, hacker, cybercriminal, foreign adversary, or other malicious actor can choose to encrypt the data as part of a ransomware attack or use it to hijack corporate executives’ email accounts.
It’ s not data exfiltration until the data are copied or moved to some other storage device under the attacker’ s control. Sometimes, the attack may come from an insider threat— an employee, business partner, or other authorized user who intentionally or unintentionally exposes data due to human error, poor judgment, ignorance of security controls, or out of disgruntlement or greed.
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The Future of AI in Fraud Detection
As AI technology improves, it is expected to become more effective at detecting and preventing complex fraud. For example, let’ s consider phone insurance fraud. Phone insurance fraud, also known as device insurance fraud( because it can refer to fraud involving laptops and tablets as well as smartphones), occurs when someone intentionally makes a false claim on their device’ s insurance company, falsely asserting that their device was lost, stolen, or damaged, or exaggerating the extent of the damage.
One survey showed that 40 percent of all insurance claims are fraudulent. For companies, fraud can result in significant losses and increase the cost of premiums for consumers. Rates of fraud incidents have increased significantly. A survey by Javelin Strategy Research found that fraudulent claims on mobile phones increased by 63 percent between 2018 and 2019.
Phone theft has also become more sophisticated and organized, leading to phishing attacks and social engineering used to access stolen devices and perpetrate fraud and false claims. In some instances, phone owners will buy multiple policies on the same phone and then claim theft, loss, or damage to obtain money for the phone from numerous insurance providers.
There’ s another type of fraud to be aware of. According to Jonathan Nelson, director of product management for Hiya, an insurance and finance provider, insurers need to be mindful of how their customers are being misled or unwittingly targeted.“ The most common thing that you’ ll experience when you’ re becoming a victim of … an automobile, insurance, or warranty scam, is what we call illegal lead generation. Effectively, the goal is to manipulate the recipient into signing up with a different third-party insurance company [ that ] may or may not be aware of the fact that their new customers are coming through this illegal sort of scam-like channel.”
AI to the Rescue
One promising development is the use of deep learning models, which can quickly compare new insurance claims against millions of past claims. These models look for unusual patterns that might suggest fraud, such as strange damage descriptions, multiple claims from the same person, or inconsistencies in location data.
These advanced models don’ t just follow fixed rules; they learn and improve with every new piece of data they analyze. For example, they can examine pictures of damaged phones, compare them with large databases, spot signs of image manipulation, and assess whether fraud may be involved.
The Internet of Things will enhance these fraud prevention efforts by connecting data from various devices, such as smartphones and wearables. This will allow insurers to gather real-time information about how devices are used, where they are located, and any unusual activity. Additionally, new platforms are being developed to help insurance companies share anonymous data on fraud, making it easier to identify repeat offenders and stay ahead of evolving fraud tactics.
As AI continues to develop, striking a balance between innovation and ethical considerations will be key. While AI has the potential to revolutionize fraud detection and many other industries, it is essential to address biases, improve data quality, and ensure transparency. With proper oversight and responsible implementation, AI can be a powerful tool that benefits both businesses and consumers.