5 Ways AI is Detecting and Preventing Identity Fraud

The field of cybersecurity is growing rapidly compared to other American industries. If that’s true, why are networks and profiles still being compromised at the highest levels?

Growths in technology undoubtedly beef up companies’ security, but they also give criminals new avenues to attack. Businesses tend to focus on integrating helpful technologies as quickly as possible without much regard for how it opens them up to unknown dangers.

One of the best tools against modern strategies is artificial intelligence. AI’s incredible analytical speed allows businesses to respond to the rapidly evolving attacks of today’s hackers. Below are five ways that AI can help mitigate the risk of identity fraud.

Strategies for Improving AI Fraud Detection and Prevention

Artificial intelligence isn’t a use out-of-box technology. Professionals need to optimize it according to their company’s security goals, or it may do more harm than good. The best way to accomplish this is to guide the AI’s learning by controlling the data it sees.

Start with Supervised Learning

While AI is highly self-sufficient, that doesn’t mean it doesn’t need a few tune-ups once in a while. Combining supervised with unsupervised AI protocols teaches artificial intelligence when it should produce a positive or negative result based on the data.

For example, a business might feed the AI common examples of spam and phishing emails it receives. This teaches the program that the traits it finds in those emails should produce a positive ‘fraud’ result.

Make Changes with Adaptive Analytics

The full scope and speed of an AI’s learning ability are hard to grasp. These programs create biases from massive data sets spanning across decades and instantaneously determine how much weight to give a single, new data point. Any changes caused by these additions are what is known as adaptive analytics.

Adaptive analytics makes AI exceptional at locating the line between fraud and an outlier. As cybercrime evolves, it’s vital that your AI’s seed data is updated to meet them. Properly using this will determine if your AI protocols save or lose money.

Artificial Intelligence and Fraud Detection

For a business, having securities fail is like having a hole in your wallet. Fraud is often used to trick employees, steal information, and cause a data breach. According to IBM, the average cost of a data breach in the US is about $9.5 million. That’s over twice the global average.

How AI is Preventing Identity Fraud with Behavioral Analysis

AI can use behavioral analysis to detect fraud in real-time. This is especially helpful for companies that operate in fast-paced environments or frequently deal with emergencies. Here’s why identity monitoring will help AI to prevent frauds.

Attacks like phishing and other social engineering scams impersonate high-level executives to steal sensitive information from lower-level employees. Scammers leverage the authority of these personas to pressure their targets into responding rashly.

Having an AI program around keeps threats off an employee’s screen. By continuously analyzing incoming emails and texts, the AI learns how the sender speaks and can determine if a message is legitimate.

Artificial intelligence also prevents large-scale data breaches that result in the loss of millions of credit card numbers and customer information. AI programs conduct penetration tests on a company’s security system so they can address weaknesses and prevent breaches.

Significant Role of Machine Learning (ML) in Cyber Scam Detection

Machine learning is the use of algorithms to detect patterns in data. It’s used to identify patterns in data that humans may miss and then train other systems to detect them.

Machine learning algorithms find patterns in data that humans may overlook. For example, if the AI looked at a large number of credit card transactions over time, it might notice an unusual pattern.

For example, if there are purchases from a store, the cardholder has never expressed any interest in. This may indicate a criminal impersonating the cardholder to make unlawful purchases.

In this case, machine learning allows computers to learn what types of behavior typically indicate fraud without needing explicit instructions from human programmers. This bias is inputted into the new algorithm and searched for when analyzing future data sets.

AI Weakens Synthetic Identity Fraud

Synthetic identity fraud is a relatively new strategy in which a scammer assumes the identity of a fabricated individual. Fraudsters use this method to fool current fraud detection systems by combining a potentially credible social security number (SSN) with a list of personal identifiers.

For example, the fraudster uses a random SSN and pairs it with an address, name, and date of birth. However, none of this information will match a single person. The fraudster creates an imaginary identity they can use to apply for credit lines with no intention to pay.

Because there is no actual consumer to notify the organization of fraud, many organizations are ignorant that they’ve been had. Additionally, current fraud detection systems cannot notice that the provided identifiers don’t match entirely due to SSNs not using a codified format.

Organizations experience a lot of trouble filtering out these fraudulent accounts. Manually analyzing each one isn’t an option since endless applications flood in on the daily. However, setting up a hardline for suspicious activity could discourage legitimate clients due to false positives.

Artificial intelligence circumvents these issues by comparing the actions of individual accounts against historical data. By learning about the fraudster’s personal habits and analyzing their spending patterns against legitimate accounts with similar backgrounds, organizations can put together a risk score for new accounts.

Conclusion

AI is an invaluable tool for preventing identity fraud. It helps businesses detect unusual behaviors and create a security system that learns what to watch out for.

Having these systems in place is becoming mandatory as online transactions rise. To keep up, organizations have to integrate more automated processes that scammers can abuse using false identities.

AI can work around the clock to prevent these cybercrimes in real time—a vital advantage over traditional methods of prevention that rely on humans or other systems that can be slow to react.