Financial fraud has existed for centuries and continues to pose a constant risk to businesses and individuals. Criminals have refined increasingly sophisticated techniques over time to exploit human and system vulnerabilities, a consequence of the ongoing digitalization of companies.
We now live in an era where traditional Ponzi schemes go hand-in-hand with modern techniques powered by Generative AI, which can be used to create highly realistic and deceptive content or simulate identities with unprecedented detail. Implementing advanced fraud detection systems is the first effective response to this phenomenon.
In the digital age, financial fraud manifests in various ways and affects a multitude of companies and industries.
For businesses, the effects of fraud are easy to imagine, ranging from significant financial and reputational losses to penalties, not to mention increased operational costs and reduced customer satisfaction.
To provide some authoritative data, Revelin's Global Fraud Trends 2024 reported a 69% increase in payment fraud in 2023 and an annual cost of over $15 million for 27% of the companies surveyed. In Italy, the same research found that 34.4% of the sample had been publicly mentioned by the press or social media as victims of fraud within the last year.
Equally interesting is the phenomenon of AI-powered fraud, which affected 63.6% of the sample. This data is significant because the impact of synthetic identities and AI-driven automation is often underestimated, and considered by many as marginal issues. The data suggests the opposite.
As mentioned, in 2024 fraud can take many forms, as most processes and transactions are now digital, and fraud attempts are continuously increasing. Broadly, financial (digital) fraud can be categorized into five main types:
The good news is that there are tools, processes, and activities that can significantly reduce the incidence and impact of financial fraud.
In the era of artificial intelligence, the focus is on advanced fraud detection solutions, which can automate the identification of suspicious behaviors, requests, and potentially fraudulent transactions efficiently and timely. However, every company should approach this issue systematically, assessing not only tools and technologies but also the security of its internal processes and the risks posed by human behavior. Below is a brief checklist for mitigating digital fraud.
Fraud detection solutions are a set of tools and technologies that work synergistically to identify, prevent, and mitigate fraudulent activities within financial transactions and commercial operations. The goal is to detect anomalies in user behavior and transactions to respond promptly and automatically, minimizing financial losses.
Fraud detection solutions analyze transactions and access data in real-time to detect noteworthy anomalies. This can be done using a set of rules, however complex, or through self-learning mechanisms (machine learning), either supervised or unsupervised.
In the case of supervised learning, the machine learning model is trained on a dataset where each transaction is manually labeled as legitimate or fraudulent. This training allows the model to learn the distinctive characteristics of fraud, improving its ability to identify suspicious behavior in future transactions, with increasingly accurate performance.
Unsupervised machine learning, on the other hand, does not require a pre-labeled dataset. In this approach, algorithms search for patterns and anomalies in data without external guidance. This method is particularly useful in contexts where fraud may manifest in unexpected or unknown ways, making it difficult to define fixed rules. The algorithms autonomously identify clusters of similar behaviors and detect anomalies that deviate from these patterns, flagging potential fraud through a scoring mechanism.
So, how is an AI-powered fraud detection solution built, and how does it work? Below are the main steps of the process.
In essence, this solution performs a behavioral analysis of user activity, establishing a baseline and evaluating any deviation from the norm. Through machine learning, often in combination with predetermined rules, the system can monitor transactions in real time, detect anomalies, and, depending on the case, instantly block requests or require additional authorizations through a secure channel. As mentioned, this is a scoring mechanism, a real-time risk assessment for each transaction, while machine learning helps progressively improve performance, reducing both false positives and negatives.
These solutions cannot eliminate the problem of digital fraud, but they represent a significant step in the right direction. When integrated with appropriate processes and best practices, they can significantly reduce risks.