Using AI for Fraud Detection

Just as your average cyberattack has grown more sophisticated, so have the avenues for fraud, phishing, and other social-engineering events. It makes sense that as the digital tools we use become more prevalent and ingrained in our daily lives, nefarious actors are looking to capitalize on them.

In their latest report, McAfee and the Center for Strategic and International Studies (CSIS) reveal that cybercrime now costs the world $600 billion, or 0.8 percent of the global GDP. Many of those events involve scams, fraud, and other human-driven activities rather than high-tech hacking or trojans.

Ransomware, another popular social-engineering hack, involves locking down the system or encrypting sensitive data and then demanding a ransom, usually paid out in cryptocurrencies – and the attackers rarely, if ever, return access. This is exactly what happened during the recent Colonial Pipeline incident.

The question is, how does one combat this sort of thing? It can be achieved with the help of artificial intelligence (AI) and machine learning (ML).

Two major advantages AI and ML tools can leverage make them ideal for fraud detection.

First, they can analyze massive troves of data and information at unprecedented speeds, certainly faster than any human ever could. What’s more, neural networks can learn, over time, after ingesting data and input from investigators, exactly what to look for. It means they become smarter, more effective, and more accurate at detecting nefarious activities and patterns.

AI solutions never have to rest in the same way a human operator would. They can continue working 24/7, including during the odd hours that international or nefarious attackers would.

Through the lens of advanced analytics, AI and ML tools can identify threats, pinpoint attack vectors, and help security teams shore up system vulnerabilities and network concerns.

When applied to fraud detection, specifically, the technology can label fraudulent content or access attempts, predict potential threats, and provide a better classification for legitimate and illegitimate sources.

Picture it as preparing a fort before a battle. All entry points would be locked down and guarded, all potential threat concerns would be addressed, and there would be contingencies in place for when various weak points are breached. AI is capable of doing all these things, faster and with better accuracy than human operators.

Additionally, auxiliary support can be encoded into the ML algorithms to facilitate future actions. For example, if there is an attack and legal action is required, the tool can extract the necessary information and send it off to the appropriate parties. The result is automated audit processes.

Financial fraud attorneys will need the pertinent information to make a case and stand their ground. If it arrives early in the process, or even before the event has been flagged by authorities, they have more time to prepare. Fraud related to banking and financial services is complicated to navigate, especially when it comes to government funds and proving due diligence. That head start can be invaluable, achieved because the ML tool was ready and capable of sending off the necessary information.

Business

Typically, a business or organization hit with fraud deals with the problem after the fact, which can result in severe financial damages. This is also because fraud is challenging to detect. Until recently, it hasn’t been viable to do so with optimal performance.

However, chipmakers, like Intel, now have the power to detect fraudulent events, like payments, in real-time and with the help of on-chip AI. It means payment firms and businesses can better identify fraud, catch would-be bad actors as they strike, and essentially stop the entire situation mid-play.

Government

While AI is being deployed in many areas, a promising development in the government and financial sector has to do with budgetary oversight. Algorithms can be used to detect anomalies or mistakes, which are then referred to human investigators who look for signs of fraud. This allows the various parties to be more ethical and responsible, but also helps fend off the potential effects and dangers of fraud.

Insurance fraud is a major concern, but AI can be used to adapt to the evolution of fraud techniques and various behaviors. Insurance agencies and investigators can leverage AI to identify anomalous patterns, labeling potential threats for closer inspection but also to put eyes on the right incoming channels.

For example, claims coming from a particular party pre-labeled as potentially fraudulent would net more scrutiny. It makes a big difference when working with data-related activities, such as unemployment insurance fraud, which can easily slip through the cracks without the support of AI-powered analytics.

AI and ML platforms grow more capable every day. That’s because, as more data is fed into these solutions, the algorithms become more effective at detecting anomalous behavior – a solid indicator of fraud. Expect many industries to continue finding novel ways to use tools like these in the pursuit of a safer and fairer business environment.