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The "Hello World" of Tensorflow - KDnuggets


Tensorflow is an open-source end-to-end machine learning framework that makes it easy to train and deploy the model. It consists of two words - tensor and flow. A tensor is a vector or a multidimensional array that is a standard way of representing the data in deep learning models. Flow implies how the data moves through a graph by undergoing the operations called nodes. It is used for numerical computation and large-scale machine learning by bundling various algorithms together.

Promising Benefits of AI in the Financial Technology Market


Artificial intelligence (AI) is all the rage now. It's impacting numerous industries globally and changing the way we do things. One of the critical industries AI is making strides in is the financial technology "fintech" industry. AI now plays a significant role in facilitating financial services, replacing what required manual work a few years ago. For example, banks now apply AI to assess credit risks with high accuracy.

Artificial Intelligence in the Finance and Banking Sector?


AI is fantabulous and in demand in the banking and finance sector. The technological furtherance in AI – machine learning, computer vision and natural language processing has downright remodelled the business world. The expert opinion states that the growth of the AI market would reach $190 billion by the year 2025! The application of conversational assistants or chatbots is one of the substantial benefits of AI in the banking and finance sector. As opposed to an employee, a chatbot is at one's disposal 24 hours a day, and clients are more complacent using this software programme to answer inquiries and complete many typical banking procedures that traditionally called for face-to-face interaction.

Machine Learning use cases in Banking, Finance & Insurance medium


Nearly 3,000 years ago, the philosopher-mystic Pythagoras claimed that everything can be expressed in numbers. At that time, no one understood him. Today, we are witnessing a digital breakthrough in which machines analyze large amounts of data on decisions made by people in different situations, translate learning algorithms into their own language, and act by analogy with humans. Today, developments in the field of AI and Machine Learning confidently follow the path of creating a computer, the cognitive functions of which are comparable to the human brain. The areas of finance, banking and insurance are the most promising areas to apply these technologies.

Artificial Intelligence in Banking: Top Priorities for 2022 (And Beyond)


Artificial Intelligence in financial services is still largely filled with untapped potential. While many bankers may think of things like chatbots and fraud monitoring when it comes to AI, in reality the technology can be used in just about any conceivable part of a bank or credit union. For the most part the industry has not even scratched the surface of how AI can transform banking. Here are some of the top ways financial institutions can deploy artificial intelligence in 2022 and beyond. Let's start with the most obvious application first.



What is Burn Rate and Why Businesses Need to Track it? What is ARR (Annual Recurring Revenue)? Can AI be the future of Financial Crime? What Is Ad Hoc Analysis? What Is a Bare Metal Hypervisor?



AI technology is rapidly gaining popularity. Businesses are investing in AI to boost their revenue. It will perform a wide range of tasks, which will have huge consequences if you understand the basics of AI. You can use it to detect patterns in data. This will give you insight and allow you to extrapolate learned patterns.

Machine Learning in Financial Crime Control


Yesterday I was approached multiple times about this FD article and Sygno's experience with regulators and Machine Learning in Transaction Monitoring. And, due to the lawsuit, with special interest in how the Dutch central bank (DNB) operates in these matters. Though I don't know the details of this case other than those presented in the media, it seems we have a vastly different experience with (Dutch) regulators. We see a regulator that actively promotes Machine Learning and usage of Data. That doesn't mean that all Machine Learning initiatives pass their scrutiny.

The first IBM mainframe for AI arrives


Mainframes are as relevant in 2022 as they were in the 1960s. IBM's new IBM z16, with its integrated on-chip Telum AI accelerator, is ready to analyze real-time transactions, at scale. This makes it perfect for mainframe mission-critical workloads such as healthcare and financial transactions. This 21st century Big Iron AI accelerator is built onto its core Telum processor. With this new dual-processor 5.2 GHz chip and its 16 cores, it can perform 300 billion deep-learning inferences per day with one-millisecond latency.