NEW SERIES-ARTIFICIAL INTELLIGENCE



The rapid development of artificial intelligence (AI) technology means internal control work in enterprises is facing profound changes. The advantages of AI in data analysis, process automation, and risk identification provide powerful support for internal control in enterprises, but at the same time, it also puts higher demands on the skills and career development of internal control personnel. Internal control specialists, need to consider how to better integrate AI and improve the efficiency and value of internal control, while ensuring their own professional competitiveness. In the era of AI, internal control personnel not only need to possess traditional internal control knowledge, but also need to master new technologies to adapt to the new mode of AI human collaboration. Each month we will follow this section with information, terminology and practice aids regarding the implementation and use of Artificial Intelligence (AI).

Historical Development of AI

1950s: The Birth of AI

• Alan Turing: British mathematician Alan Turing laid the groundwork for AI with his concept of a machine that could simulate any human intelligence task. He also proposed the famous Turing Test to determine if a machine could exhibit intelligent behavior indistinguishable from a human.
• Dartmouth Conference (1956): John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon organized a summer workshop at Dartmouth College, which is considered the birth of AI as a field. They coined the term "artificial intelligence" and explored the possibility of creating machines that could think.

1960s-1970s: Early AI Research

• Expert Systems: AI research focused on developing expert systems that could mimic human decision-making in specific domains. Examples include DENDRAL for chemical analysis and MYCIN for medical diagnosis.
• Natural Language Processing (NLP): Early NLP systems like ELIZA, developed by Joseph Weizenbaum, simulated conversation with a human by using pattern matching and substitution methodologies.

1980s: The Rise of Machine Learning

• Neural Networks: The resurgence of interest in neural networks, inspired by the human brain, led to the development of backpropagation algorithms, which allowed for more effective training of multi-layer neural networks.
• AI Winter: Despite initial successes, AI faced periods of reduced funding and interest, known as "AI winters," due to unmet expectations and limitations in computing power.

1990s-2000s: AI Becomes Practical

• Deep Blue: IBM's Deep Blue defeated world chess champion Garry Kasparov in 1997, showcasing the potential of AI in complex problem-solving.
• Autonomous Vehicles: DARPA's Grand Challenge in the early 2000s spurred advancements in autonomous vehicle technology, leading to the development of self-driving cars.

2010s: The Deep Learning Revolution

• Deep Learning: Advances in deep learning, powered by large datasets and increased computational power, led to breakthroughs in image and speech recognition. Notable examples include Google's AlphaGo defeating Go champion Lee Sedol and the rise of virtual assistants like Siri and Alexa.
• Generative Models: Generative models like GPT-3 and DALL-E demonstrated the ability to generate human-like text and images, pushing the boundaries of AI creativity.

Next Month: Future Trends of AI

A study on the complicated, connected nature of fraud

Fraud is an ever-evolving threat posing significant challenges to businesses worldwide. Moody’s data on fraud risk shows the United States has the highest number of risk events (over 59,000 in 2024—an increase of over 12,500 fraud-related risk events compared to five years earlier).

With the rise of sophisticated fraud schemes and new regulatory requirements, companies are responding by adopting comprehensive strategies to mitigate risks and protect their interests and their clients. This article investigates the current state of fraud risk.

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Fraud Grows More Complex And Widespread As Chargebacks And AI Threats Loom Large

While online fraud for companies has long been synonymous with payment fraud and account takeovers, a new wave of threats is gaining ground led by first-party chargeback fraud, refund abuse, and, increasingly, AI-powered attacks.  Nearly half of merchants (47%) now report a rise in fraud perpetrated by known customers. These are the findings of Ravelin’s annual Global Fraud Survey 2025, which paints a picture of escalating fraud.

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Fraud Prevention in Focus: Examining DOD’s Risk Management Strategies

The Department of Defense (“DOD”) is responsible for almost half of the federal government’s discretionary spending and spends more on contracting than all other federal agencies combined, obligating about $445 billion in fiscal year 2024. Given the renewed focus on fraud detection and prevention across the U.S. government – as highlighted both by the efforts of the Department of Government Efficiency (“DOGE”) and by recent congressional committee hearings focused on reducing waste, fraud, and abuse – the issue of fraud in DOD has become increasingly relevant.

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The Five Gateways of Inventory Control A Case Study

Operational controls and their impact on the financial statements represent a significant challenge for management accountants. The authors present a case study of an industrial manufacturer and its external auditors. They describe the company’s five “gateways” of inventory control and their critical importance to materials management and the annual inventory check.

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Fraud and Its Consequences—Who Steals from Federal Programs and What’s the Cost?

Hundreds of billions of dollars are lost to fraud each year. Fraud can happen at any time. But during emergencies, such as the COVID-19 pandemic, there’s a heightened risk of fraud where spending is increased, and funds go out faster than usual. Bringing fraudsters to justice is a vital step in restoring public trust in federal spending. It’s also essential to understanding how fraud was committed in the first place so that it can be prevented in the future. Today’s WatchBlog post looks at our latest work on how individuals and organized groups defrauded federal COVID-19 relief and other programs. 

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COSO withdraws draft corporate governance framework due to “shifting” regulations

The Committee of Sponsoring Organizations of the Treadway Commission (COSO) has withdrawn its draft corporate governance framework that it released in May, after “extensive feedback” and provisions in the recently passed “One Big Beautiful Bill” caused its authors to reconsider it.

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