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Companies lose over 5% of their revenue annually to fraud

Companies lose more than 5 per cent of their revenue each year to fraud, according to data from an international study by the Association of Certified Fraud Examiners (ACFE), presented by Chrystalla Kazara, vice president and director of training for the organization’s Cyprus office.

KEY FACTS

  • The study examined 1,921 real-life cases of job fraud from 138 countries.
  • Kazara pointed out that the losses of companies from fraud annually amount to more than 3.1 billion dollars.
  • She added that fraud cases are divided into three categories – embezzlement, corruption and misuse of financial statements. Embezzlement was the most common category, accounting for 89 % of cases in the Association of Certified Fraud Examiners survey, Kazara explained.
  • According to her, during the coronavirus pandemic, there was a serious increase in losses from fraud by 24 %, with the biggest jump being marked by cases of corruption – 33 %, and in second place was fraud with financial statements. According to her, this is due to the compromise of the companies’ internal control systems.

TANGENT

Kazara pointed out that specifically for Eastern Europe, the most serious problems are due to corruption, which refers to 71 % of the cases in the study by the Association of Certified Fraud Examiners.

The study also indicates that more than half of the frauds are due to company employees. Kazara emphasized that if employees were trained in how to act on fraud, it would prevent a large number of cases.

The AI Agent Revolution: Can the Industry Handle the Compute Surge?

As AI agents evolve from simple chatbots into complex, autonomous assistants, the tech industry faces a new challenge: Is there enough computing power to support them? With AI agents poised to become integral in various industries, computational demands are rising rapidly.

A recent Barclays report forecasts that the AI industry can support between 1.5 billion and 22 billion AI agents, potentially revolutionizing white-collar work. However, the increase in AI’s capabilities comes at a cost. AI agents, unlike chatbots, generate significantly more tokens—up to 25 times more per query—requiring far greater computing power.

Tokens, the fundamental units of generative AI, represent fragmented parts of language to simplify processing. This increase in token generation is linked to reasoning models, like OpenAI’s o1 and DeepSeek’s R1, which break tasks into smaller, manageable chunks. As AI agents process more complex tasks, the tokens multiply, driving up the demand for AI chips and computational capacity.

Barclays analysts caution that while the current infrastructure can handle a significant volume of agents, the rise of these “super agents” might outpace available resources, requiring additional chips and servers to meet demand. OpenAI’s ChatGPT Pro, for example, generates around 9.4 million tokens annually per subscriber, highlighting just how computationally expensive these reasoning models can be.

In essence, the tech industry is at a critical juncture. While AI agents show immense potential, their expansion could strain the limits of current computing infrastructure. The question is, can the industry keep up with the demand?

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