Cybercrimes are evolving rapidly and becoming more diverse and innovative, and their rates are reaching a never-seen-before high. As old tricks stop working, fraudsters come up with more complex, multistage approaches. Since cybercriminals are very quick to adapt to new social, political, and cultural tendencies, people need to think fast to combat potential threats.
It’s a good idea to use modern technologies to protect data at an advanced level. The analysts at Grapherex have explored whether it is efficient to detect fraud with AI algorithms and machine learning. Let’s see how it works and explore the pros and cons.
What Does Fraud Detection Include?
In 2023, there are many different types of cybercrime. These include scamming using games and streaming services, phishing via social media, attracting people to invest in crypto and metaverses, business email compromise (BEC) attacks, and more.
To deal with these risks, both individuals and companies apply the same methods: they detect a threat and protect themselves. The second step includes either preventing malicious actions or taking measures to minimise their consequences. Below, we will discuss the many cyber threats that businesses face.
It is best to be proactive to prevent unauthorised financial activity or data leakage. And to do this, we need to detect fraud before it causes any damage. Fraud detection is a set of procedures established to prevent fraudulent transactions with bank cards, minimise the risk of theft and cyberattacks, and identify security vulnerabilities.
In simple words, everything that looks fake, too good to be true, or just not normal (like a private company’s data published online) must be double-checked. It could be phishing, scamming or data theft, so beware.
AI vs Machine Learning: What’s the Difference?
Artificial intelligence and machine learning are two terms that are often used interchangeably, but there’s a difference between them. AI is a vast field that includes technologies that allow machines to perform tasks that traditionally require human intelligence. AI includes machine learning, natural language processing, computer vision, and robotics.
Machine learning is a field of AI that focuses on training algorithms and models to study patterns and make predictions based on large data sets. Deep learning is the dominant field of machine learning that uses algorithms to train artificial neural networks with multiple layers to automatically make decisions based on input data without explicitly designing functions.
How Can Machine Learning Improve Fraud Detection?
In the field of online fraud detection and prevention, machine learning serves as a powerful toolset of algorithms that are trained on your historical data. Thanks to this, ML-powered software provides clear risk rules applicable to your business. These rules are then put into action to either allow or block certain user actions and detect fraudulent transactions, theft, or suspicious logins.
To train the ML engine effectively, you’ll need to properly flag previous instances of both fraud and non-fraud in the data. This is necessary to avoid false positives and increase the accuracy of the resulting risk rules. As the system learns and improves over time, the risk rules become more precise.
Benefits of Machine Learning for Fraud Management
The core idea behind using ML in fraud prevention is that it can increase the speed and accuracy of fraud detection or even automate the process completely. Algorithms allow companies to quickly identify suspicious patterns and behaviours, which can take humans months to establish. But there are even more benefits.
Reduced Manual Review Time
Machine learning reduces the amount of time spent manually reviewing data, as this technology can analyse all data points much faster than humans can. At the same time, algorithms eliminate the probability of human error. They are not naive, do not follow links to phishing sites and do not risk falling into a trap. Properly programmed software removes the human factor and, in general, the need for a person to participate in decision-making.
Better Predictions with Large Datasets
The more data you provide to the ML engine, the more trained and efficient it becomes. It processes huge data amounts and detects patterns that may be missed by human analysts. To put it simply, while large datasets sometimes make it difficult for people to find fraudulent activities, for AI systems, the opposite is true.
Cost-Effective Fraud Management
Cost is a major advantage of using Machine learning. Instead of hiring many professional risk management (RiskOps) agents, you’ll need only one ML system to review all the data provided to it, regardless of volume. It’s believed to be an ideal solution for businesses that experience seasonal fluctuations in traffic or check-outs, or check-ins. ML can scale up or down as needed without dramatically increasing the cost of risk management.
Drawbacks of Machine Learning with Fraud Detection
Increased speed and accuracy, reduced time, better fraud predictions, and cost-effective risk management — it sounds like nothing could be better. Still, AI and ML have some limitations, and it’s important to take them into account. Let’s see the key disadvantages.
Lack of Control
With automated fraud detection, a company has less control over risk management. This is especially true when it comes to blackbox machine learning mechanisms. Blackbox engines have non-transparent internal processes that are difficult to interpret, where we know only the input and output. It is difficult, if not impossible, to understand how the algorithm arrived at its decisions. Such ML systems could make mistakes without anyone noticing them.
False Positives
We already touched upon false positives when we talked about how to train an ML engine. False positives occur when a legitimate action is mistakenly seen as fraudulent. If this is not fixed, it will have a negative impact on the overall system. Poorly calibrated machine learning engines can create a loop whereby unflagged false positives become a norm. This will eventually reduce the precision of the results, waste your time, and even harm the business.
No Human Understanding
When you develop an AI model or ML system to detect fraud, the very first thing to do is to explain what “suspicious activity” means. Sometimes nothing works better than human psychology. Machines only detect patterns based on what they have been trained on. A person takes into account the broader context and can recognise suspicious behaviour in a customer’s communication style or tone of voice.
Takeaways
- Cybercrime evolves constantly, and fraudsters come up with increasingly complex approaches to their activities. So, it is reasonable to use modern technologies like AI and ML to protect valuable data.
- Fraud detection involves processes necessary to prevent unauthorised access, fake transactions, lower the risk of theft, and identify security vulnerabilities.
- Machine learning is a powerful algorithm that increases the speed and accuracy of fraud detection and automates this process.
- The benefits of ML in fraud management include reduced review time, high-quality predictions based on large datasets, and cheaper risk management.
- The disadvantages of ML are a lack of control, false positives, and the absence of human logic.
