The UN Office on Drugs and Crime estimates that 5% of global GDP (£1.6 trillion) is laundered yearly, with increasing volumes of online data and the digitization of the economy making fraudsters more creative and difficult to catch.
“Enterprises in the finance, banking, and telecommunications sectors are the most susceptible to online fraud, but it can happen to any company,” said Vaidotas Šedys, Head of Risk Management at Oxylabs. “Unauthorised transactions made with the help of lost or stolen credit cards, counterfeit cards, ID document forgery and identity theft, fake identification, email phishing, and imposter scams are among the most common types of payment fraud today."
Artificial intelligence (AI) systems and machine learning (ML) models enable companies to get ahead against fraud perpetrators by opening the possibility to gather and analyze massive datasets in real time. ML algorithms scan thousands of transactions, identifying hidden correlations or patterns, an impossible task for human risk analysts.
V. Šedys continued, “Adaptive fraud detection techniques based on deep learning and behavioral pattern recognition allow cybersecurity experts to monitor and analyze an increased number of transactions per second, flagging anomalies instantly. Cloud technologies also play an important role in the latest anti-fraud developments. Such services as distributed cloud account protection can detect malicious actors very accurately by monitoring transactions at a large scale and in real time.
“ML can monitor login attempts too, with companies using historical logs to train an algorithm on the most common user practices, such as the place, time, or devices used to log in. With cohesive training, the model can then monitor and flag login attempts that do not resemble common patterns as a sign of possible unauthorized access.”
As cyber-attacks continue to increase, the real-time monitoring of internal systems and threat intelligence is vital for a robust security strategy. Cybersecurity experts use web scraping to gather critical information from target websites and obtain unique insights, sometimes even infiltrating the dark web and later analyzing this information with the help of ML.
V. Šedys continued, “Developments in web scraping and AI and ML positively reinforce each other. ML developers must gather good quality and diverse data to make the algorithms more accurate, which would be impossible without web scraping. Subsequently, AI and ML automate different parts of web scraping, making it less complicated to perform.”
“AI-powered web scrapers and proxy solutions can identify inactive URLs, generate dynamic fingerprints using different parameter sets (IP address, browser, location, window resolution, etc.), and bypass flagging or bans. It is also possible to employ natural language processing (NLP) and scan scraped content to determine if it meets primary goals.”
V. Šedys concluded, “AI and ML technologies are vital in the fight against cybercrime, helping organizations identify anomalies. AI-powered scraping solutions allow cyber security researchers to be more proactive in their role, at the same time reducing their time-to-reaction when attacks occur. Web scraping and AI serve as the foundation for many of the current advancements in cybersecurity and will continue being fundamental to further developments in the field.”
