Artificial intelligence (AI) has rapidly emerged as a transformative force in the business world, revolutionizing operations, enhancing decision-making, and driving innovation across various industries. This article, which might (or might not) be crafted by an AI, explores the growing importance of AI in business, highlighting key developments and tools shaping the landscape.
Elon Musk’s Bid to Acquire OpenAI
In a bold move underscoring AI’s significance, Elon Musk recently led a consortium offering $97.4 billion to acquire OpenAI, the organization behind the renowned AI model ChatGPT. Musk’s bid aims to influence OpenAI’s strategic direction as it transitions to a for-profit entity. However, a federal judge denied Musk’s request to block this restructuring, though an expedited trial is set for later this year.
The Rise of China’s DeepSeek
Chinese AI startup DeepSeek has made remarkable strides with its R1 model, offering capabilities comparable to leading AI systems at a fraction of the cost. This advancement has positioned DeepSeek as a formidable competitor in the global AI arena, reflecting China’s growing innovation. Companies like Saudi Aramco have integrated DeepSeek’s technology to enhance operational efficiency, signaling a broader adoption of cost-effective AI solutions.
AI in Financial Services and Banking
While tech giants like Microsoft, Google, and Amazon have invested heavily in their proprietary AI initiatives for decades, other industry leaders and market players don’t want to be left behind.
- JPMorgan Chase has integrated AI to improve compliance and risk management, particularly in fraud detection. AI has significantly reduced false positives in identifying fraudulent transactions, improving operational efficiency and customer experience.
Additionally, AI assists in processing vast amounts of regulatory paperwork, benefiting both the bank and regulators. As the bank’s chief managing director and head of AI policy, Terah Lyons said on the HumanX AI conference in April 2025, “This initiative bolsters customer satisfaction, as there are fewer times customers have to call support to make corrections. This ensures greater fraud security, cybersecurity, and operational security for JPMorgan Chase.”
- Visa: Visa has invested $10 billion over five years in technology development, including $3 billion in AI and data infrastructure. This investment aims to handle fraud in real time, leading to a better customer experience and reduced operating expenses.
- NatWest. NatWest is enhancing its digital assistants and customer support. This initiative aims to improve customer experience, reduce costs, and combat financial fraud by leveraging OpenAI’s technology to upgrade the bank’s chatbot, Cora, and staff assistant, AskArchie
Emergence of AI-Based Business Tools
Beyond tech giants, multiple AI-driven tools are poised to enhance business operations globally:
- Monarda: An AI platform from Appen, aiming to improve voice recognition technology by recording questions and commands in English, spoken by people worldwide.
- Outlier: This company develops a Generative AI based on input from subject matter experts in various fields.
These and other tools, while still in development, showcase the market’s push to increase operational efficiency and resilience through AI adoption.
Covery.ai’s Pioneering Fraud Detection
Since 2017, Covery.ai has leveraged AI algorithms to detect fraud attempts dynamically. By analyzing Know Your Customer (KYC) outcomes, Trustchain records, customer behavior history, device fingerprinting, and over 25 other factors, Covery.ai provides robust fraud prevention solutions.
Covery harnesses a rule-based fraud response constructor, where you can craft sequences of actions to deal with the most widespread fraud cases like CNP fraud, synthetic identity fraud, friendly fraud, etc. More importantly, Covery utilizes a proprietary AI algorithm to highlight malevolent actors on the go or detect fraudulent user behavior through logs and records ASAP.
From the beginning, Covery’s architecture was built around Machine Learning usage. Below are the main ways we use AI: data analytics, unstructured text processing, and image processing.
- Data analysis. We use classification algorithms and supervised learning methods. These algorithms help us find behavioral patterns of fraudulent actions that risk analytics might have missed. Such trained models become an important part of a risk scenario and contribute to the final decision, while not making it themselves. This approach helps improve the fraud detection quality without relying solely on ML algorithms and helps keep decision-making logic as a whole transparent for business.
While our system allows analyzing a wide range of unique data from every particular customer, we avoid creating universal ML models. Instead, we favor an individual approach, which has proven to be much more efficient time and again. - Unstructured text processing. In this case, we emphasize detecting names and company titles, depictions of dual-purpose goods, and contact data, like phone numbers, messenger nicknames, social media handles, and email addresses. Payment details are also a high-impact point of interest. Our text-processing models are universal and can work with over 100 languages. But in specific cases, we are open to training a custom-tailored model for a particular customer.
Deploying our models will be useful for banking and financial organizations, or any online services engaged in P2P communication, to reduce the risk of scams and social engineering. - Image processing. This method allows us to compare images, detect and read text on the image, discover any manipulations with the original, and highlight specific objects on the images.
This is very useful for financial institutions and travel services to extract certain data from the IDs. This capability is crucial for other online services to determine unique customers by avatars and helps fight scams and social engineering, most importantly in dating services and online marketplaces.
Risk management has a vast window of opportunities to implement ML in various domains, and Covery actively invests in these areas to provide more AI-powered risk management capabilities for our customers.
Due to this AI-based flexibility, some of our users witnessed a 95% reduction in fraud rates, which helped them move from high-risk to low-risk ratios with their payment service providers, enjoying reduced operational expenses.
Conclusion
AI is indispensable for maintaining a competitive edge in today’s rapidly evolving business landscape. From high-profile acquisitions and groundbreaking startups to innovative tools and fraud detection systems, AI’s integration into business strategies is no longer optional but essential. To stay ahead of competitors and fraudsters, merchants must adopt innovative tools from established service providers, ensuring resilience and success in an AI-driven future.
With Donald Trump imposing trade tariffs on worldwide trade, a risk of recession is looming. Companies must take action to ensure their operational resilience and minimize fraud-related losses, while maintaining high customer satisfaction, and implementing AI-based business tools is a good course of action.
Covery offers a wide variety of AI-based risk management features, ensuring custom-tailored and reliable fraud prevention for online merchants and organizations. Contact us to learn how Covery can help safeguard your business!