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Banking & Finance


new age investment: How machine learning is changing the game of investing - The Economic Times

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Anomaly detection algorithms are one of the most common techniques used in machine learning for risk management. These algorithms can analyze historical financial data and identify patterns that deviate from normal behaviour. For instance, it can detect abnormal fluctuations in stock prices or trading volumes, indicating a potential market crash or stock bubble. Once identified, these anomalies can be used to create early warning systems that alert investors to potential risks, allowing them to take preventive measures.


AI and Automation: Solving Pain Points in AP/AR

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As businesses continue to look for ways to streamline their operations and reduce the risk of errors, many are turning to document AI to automate their accounts payable (AP) and accounts receivable (AR) processes. The use of payment AI and automation, and digitization software can greatly reduce errors and improve the efficiency of financial transactions. According to the Federal Reserve, 75% of bills are manually processed, which is slow, error prone, and leads to frustration from billers and payers. Businesses communicating with other businesses send a lot of PDF documents, however it is difficult to extract and organize important information from PDF documents, especially if they're not structured in a consistent way. This can lead to delays and errors, which can be costly for the business and frustrating for customers.


US firms pumping billions into China's AI sector

FOX News

Chief national security correspondent Jennifer Griffin reports that the U.S. will only shoot down the Chinese spy balloon if officials can assure zero civilian casualties. U.S. investors were involved in at least 37% of all investment transactions in China's artificial intelligence, or AI, sector between 2015 and 2021, according to a new report. Georgetown University's Center for Security and Emerging Technology found that $40.2 billion of the total money raised by all Chinese AI companies over this time period had U.S. backing. However, the center couldn't determine what percentage of that amount came from U.S. investors or investors abroad. The money went to 251 Chinese AI companies, and 91% of the U.S. investment came as venture capital to earlier-stage businesses.


Google invested $300M in AI firm which link to Bankman-Fried

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A startup company for artificial intelligence (AI) called Anthropic received about $500 million from former FTX CEO Sam Bankman-Fried roughly six months before to FTX's tragic collapse, according to reports of a $300 million investment from Google Cloud. We're excited to use Google Cloud to train our AI systems, including Claude! https://t.co/IaqQ5lpJrP "The Series B round was driven by Sam Bankman-Fried, CEO of FTX. The round likewise included investment from Caroline Ellison, Jim McClave, Nishad Singh, Jaan Tallinn, and the Middle for Emerging Risk Research (CERR)." According to Crunchbase, Bankman fundraising initiatives were carried out in April 2022.


Shell #2 - Girl in.

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AI girls, or AI-powered virtual assistants, do not have physical appearance or emotions like human girls. However, here are some things that are considered "beautiful" or advantageous about AI girls in a virtual or technological context: Efficiency: AI girls can perform tasks quickly and accurately, without getting tired or making mistakes. Customization: AI girls can be programmed to respond to specific commands or perform specific functions, making them highly customizable to meet different needs. Availability: AI girls are available 24/7, without breaks or time off, making them highly convenient for users who need assistance at any time. Language abilities: AI girls can understand and respond to multiple languages, making them accessible to a wide range of users. Cost-effectiveness: In many cases, AI girls are more cost-effective than hiring human workers, as they do not require salaries, benefits, or time off.


Council Post: How To Overcome Five Roadblocks When Implementing AI/ML In The Financial Sector

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Do you have a digital wealth management application for your investment portfolio that recommends investing in specific funds? You are likely using artificial intelligence (AI) to manage your money. From automating and optimizing processes to using conversational AI for enhanced customer engagement and fraud detection, AI and machine learning (ML) are leaving an indelible mark on banks and financial institution performance, completely disrupting the financial industry. In fact, the global market for AI in banking is expected to reach $64.03 billion by 2030. Today, 80% of banks are very aware of the potential benefits of implementing AI, and a majority are looking to deploy AI-enabled solutions.


Luddites

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Capitalism aims to convert ambition to success. Its alchemy of incentives fosters a relentless pursuit of economic opportunity from things we crave: food, shelter, bigger iPhones, desirable mates. Our hunger for wealth and love drives us to work and create -- capitalism's genius is finding new avenues for that drive. The philosopher's stone of capitalism is technological innovation. It's no accident capitalism flourished and spread across the globe contemporaneously with the adoption of new technologies in production, transport, and information.


Data Engineer - Data & Insights – German Speaking at Nets - Ballerup, Denmark

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We are a part of Nexi Group - The European PayTech. Handling billions of transactions annually, Nets, is among the solid payment processors in Europe. We keep a tight focus on making it even easier and more intuitive for our customers to handle digital payments and related services. This has made us a trusted partner to more than 700,000 merchant outlets, including 140,000 online merchant outlets, more than 260,000 enterprises and over 250 banks across Europe. Changing the future of payments takes great personalities At Nets, you'll develop in a fast-growing tech company in a high-paced, high-impact market.


How Invoice Automation Processing Works

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During manual invoice processing, invoices received from a supplier are matched, verified, and approved. The entire process is elaborate and takes several days. But that's not all – each invoice must be manually entered into the system to be posted for payment. Finally, the payment is made. The entire process can take weeks.


NVIDIA a powerful partner in Financial Services

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Using a GPU (Graphics Processing Unit) can accelerate trading by allowing for faster processing of large amounts of data. This can be particularly useful for traditional banks, capital market firms and fintech companies that rely on data-intensive trading algorithms and need to process large amounts of data in real-time. Running machine learning algorithms: Machine learning algorithms can be computationally intensive, and a GPU can speed up the training process. This can be especially useful for developing and testing trading strategies that rely on machine learning. Data processing: A GPU can process large amounts of data quickly, which can be useful for tasks such as real-time data analysis and market monitoring.