Artificial intelligence (AI) has taken the world by storm

The global AI market was valued at $279 billion in 2024 and this is expected to grow at a CAGR of more than 35% in the next 5 years!

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 behaviour 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.

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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 analyse 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.

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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 analyse 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.

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What are the Benefits of Machine Learning Fraud Detection?

• Enhanced Accuracy: Machine learning algorithms analyse 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 minimises 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.

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ML in Practice

PayPal

A recent standout example is PayPal, who have leveraged advanced machine learning algorithms to fortify their defences against online payment fraud. The system analyses user behaviour, transaction patterns, and device data in real-time, swiftly identifying and neutralising 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.

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Monzo

The Challenger Bank, Monzo, developed in-house tools to monitor customer transactions using semi-supervised learning. By using ML to identify "friendly fraud" (e.g., chargebacks by legitimate users) and "money mule" networks, they achieved significant fraud detection rates while minimising customer disruption. This also has the effect of helping regulators track emerging financial crime patterns in the UK.

Block Inc

Formerly known as Square, Block Inc is a a financial technology company that provides financial services and digital payments solutions for both consumers and merchants. It uses behavioural analytics and unsupervised ML models to detect unusual transaction patterns from merchants and consumers. Fraud models learn from user behaviour over time and flag suspicious deviations (e.g., sudden sales spikes, refund loops). This has resulted in significantly lower fraud losses across its massive network of small businesses. Block Inc has also maintained low false positives for a better user experience.

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Using Machine Learning for Fraud Detection on Online Platforms & Businesses

Machine learning can play a pivotal role in protecting online platforms and businesses against fraudulent activities, by detecting issues such as fake accounts, fraudulent applications, fake reviews, counterfeit selling, and scams. Themis's AI-driven fraud monitoring technology conducts comprehensive monitoring to identify and address patterns of malicious behaviour across online platforms and businesses. Utilizing graph technology, network analysis, and machine learning algorithms to find patterns and connections between individuals, Themis highlights fraudulent activity, revolutionizing our customers' approach to fraud detection.

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

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With digital threats 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.