The Benefits of Using Machine Learning for Fraud Detection

As one of the top trending topics of 2023, artificial intelligence (AI) has taken the world by storm.  The global AI market is currently valued at over $136 billion and this is expected to grow by a whopping 120% year-on-year!

From the rise of ChatGPT, to the development of autonomous vehicles, it is widely considered one of the most exciting and, at times, controversial technological trends unfolding in the 21st century. While AI holds the potential for incredible positive contributions, it has also presented challenges, particularly in relation to online scams and fraud. Bad actors are increasingly leveraging AI to scale their malicious efforts, adopting linguistic fluency and crafting deceptively realistic deep fakes.

Despite this, AI has also emerged as an essential tool for the detection and prevention of such bad behavior online. This article delves into the key benefits of using machine learning as part of your fraud detection strategy, highlighting its crucial role in an ever-increasing AI-centric world.

What is Machine Learning?

While AI encompasses machines that simulate human intelligence, machine learning is a specific approach within AI that focuses on enabling computers to learn from data and make decisions without instructions. Unlike traditional programming, where tasks are dictated by explicit commands, machine learning allows systems to autonomously learn and improve from experience.

The history of AI traces back to the mid-20th century when the term "artificial intelligence" was coined in 1955 by computer scientist John McCarthy. Its progress faced skepticism in the 1980s and 1990s (known as the “AI winter”) as progress did not meet initial expectations. However, with enhanced computer power and access to extensive datasets, machine learning has soared to the forefront in the 21st century. Recent years have witnessed remarkable breakthroughs, with AI and machine learning technologies finding applications across a myriad of industries.

Large Language Models (LLMs), such as those used in ChatGPT, are a particular type of deep ML model that uses vast data sets to analyze and generate human language responses. It is worth noting, however, that they are only as reliable as the data they ingest. If they are unable to produce an accurate answer, they can sometimes ‘hallucinate’ - providing false information. Additionally, they use the inputs they receive to further train their models and are not designed to be secure vaults, potentially exposing confidential data in response to queries from other users. Consequently, LLMs do not provide a ready-made, effective solution to detecting fraud.

How Can Machine Learning be Used for Fraud Detection?

Today, machine learning has become an instrumental tool for fraud detection as it allows us to analyze patterns and anomalies in vast datasets. Various types of fraud are commonly detected using machine learning, including credit card fraud, identity theft, and online scams. Machine learning algorithms can adapt and learn from historical data, identifying irregularities in user behavior, transaction patterns, or application activities that might indicate fraud.

What are the Benefits of Machine Learning Fraud Detection?

• Enhanced Accuracy: Machine learning algorithms analyze vast datasets, significantly improving accuracy in identifying fraudulent patterns and anomalies.
• Adaptability: Machine learning models can be retrained to adapt to new fraud tactics, ensuring robust protection against emerging threats.
• Reduced False Positives: By recognising subtle patterns, machine learning minimizes false positives, allowing for more precise identification of genuine threats.
• Swift Detection: Analysis enables prompt responses to potential fraud, mitigating risks swiftly and effectively.

Failure to integrate machine learning complemented by other techniques, such as network and reputational analysis, to detect fraud not only puts your platform at risk of financial loss, but can also jeopardize user trust. In an era where scams and fraudulent activities are rampant, neglecting advanced technological solutions could also result in severe legal implications.

Example 1: PayPal

A recent standout example is PayPal, who have leveraged advanced machine learning algorithms to fortify their defenses against online payment fraud. The system analyzes user behavior, transaction patterns, and device data in real-time, swiftly identifying and neutralizing potential threats. This forward-looking approach has not only bolstered PayPal's security measures but also significantly reduced instances of fraudulent activities on the platform. By staying at the forefront of technological advancements, PayPal demonstrates the effectiveness of modern machine learning solutions in ensuring a secure digital financial ecosystem.

Using Machine Learning for Fraud Detection on Online Platforms

Machine learning can play a pivotal role in protecting online platforms against fraudulent activities, by detecting issues such as fake accounts, fake reviews, counterfeit selling, scam, and spam. For example, Pasabi's AI technology conducts comprehensive platform monitoring to identify and address patterns of malicious behavior across online platforms.

By delving into the nuances of user behavior and intent, Pasabi provides insights into individuals and fraud rings orchestrating suspicious activities. This proactive approach enables the application of the appropriate level of defense at various stages of the user journey, effectively mitigating risks and ensuring a safer online experience.

Example 2: Trustpilot

Leading review platform Trustpilot receives over 3 million posts per month. The platform  faces the continuing challenge of businesses attempting to manipulate their system via fake reviews and review selling activity. Pasabi's Trust & Safety Platform has revolutionized their approach to fraud detection, utilizing graph technology, network analysis, and machine learning algorithms to find patterns and connections between individuals and highlight fraudulent activity. The partnership has empowered Trustpilot to take thousands of legal enforcement actions, and provide a  trusted and authentic user experience. This collaboration exemplifies how machine learning can be instrumental in fortifying online platforms against deceptive practices, allowing them to maintain integrity and trust among consumers.

At a time when digital threats are on the exponential rise, the integration of machine learning as part of your fraud detection strategy is a powerful ally in the ongoing battle against fraud. Get in touch with Pasabi to discuss how we can assist you in protecting your platform today.