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How Machine Learning is Impacting the Finance Industry

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Machine learning is streamlining and optimizing processes ranging from credit decisions to quantitative trading and financial risk management. This exciting technology has the potential to transform financial services business models and markets for trading, credit and blockchain-based finance, reduce friction and enhance product offerings. Machine learning is a subset of artificial intelligence that utilizes advanced statistical techniques to enable computing systems to improve at tasks with experience over time. Chatbots like Amazon's Alexa and Apple's Siri improve every year thanks to constant use by consumers coupled with the machine learning that takes place in the background. Machine learning has grown substantially within the finance industry, enabled by the abundance of available data and the increase in the affordability of computing capacity.


Algorithms in the Financial Services industry - The right choice for the right problem

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Optimization problems: this is still a bit of an unexplored and immature domain, with little (user-friendly) tooling available, like I also mentioned in one of my previous blogs. Interesting names to look at are JuMP (based on Julia language), ADMB, GLPK, OpenMDAO, Motulus, OptaPlanner… However all those tools are still rather complex and therefore still difficult to use for non-specialized developers.


The Critical Role of Artificial Intelligence in Payments Tech - FintechNews

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Long an obsession of science fiction writers, "artificial intelligence" in the modern era of fast-paced technological innovation is a term that is as ubiquitous as it is nebulous. For the payments technology industry, however, the term describes advanced analytical technology that has an outsized potential to improve the payments ecosystem for banks, payments processors, merchants and consumers. In fact, financial services companies will spend US$11 billion on AI in 2020, according to an analysis by IDC -- more than any other industry cited. They'll stand to make a nice return on their investment as well, according to PwC estimates. In North America alone, AI is projected to increase the GDP of the financial and professional services industry as much as 10 percent by 2030, driven by increases in both productivity and consumption.



The Critical Role of Artificial Intelligence in Payments Tech Trends

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Long an obsession of science fiction writers, "artificial intelligence" in the modern era of fast-paced technological innovation is a term that is as ubiquitous as it is nebulous. For the payments technology industry, however, the term describes advanced analytical technology that has an outsized potential to improve the payments ecosystem for banks, payments processors, merchants and consumers. In fact, financial services companies will spend US$11 billion on AI in 2020, according to an analysis by IDC -- more than any other industry cited. They'll stand to make a nice return on their investment as well, according to PwC estimates. In North America alone, AI is projected to increase the GDP of the financial and professional services industry as much as 10 percent by 2030, driven by increases in both productivity and consumption.


Global Big Data Conference

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In business you often want to accurately predict a relevant product need for a given client. Much research has been done on "recommender" systems using, for example, collaborative filtering to suggest a product based on either the purchases of similar clients or on the past purchases of that client. Then, there are the cases where you have a product or service and want to find the ideal client. This becomes more difficult if the ideal client population is a small subset of the general population. This scenario is particularly tricky in the Wealth Management business.


Collaboration key to leveraging AI and machine learning in financial advice

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Greater collaboration across the Australian financial services industry is required in order to help financial advisers leverage the full potential of artificial intelligence and machine-learning technology, according to education provider Kaplan Professional and regtech pioneer Red Marker. Matt Symons, Red Marker CEO, said there needs to be a mindset change in how AI and machine-learning tools can best assist the industry. "It is important both dealer groups and vendors progress with realistic expectations, particularly around the'pre-work' that needs to be done to ensure financial advice can become an ideal candidate for automated solutions," Symons said. "If the financial services industry wants to increase the likelihood that effective statement of advice (SoA) review solutions emerge at a faster rate, then we need to come together and collaborate... working together is going to be key to developing highly reliable, automated review solutions." The two organisations said that before the industry could leverage AI and machine learning in financial advice, existing pre-conditions needed to be in place, including managing expectations, recognising the limitations in training data, and resolving diverging approaches to SoA construction, automatic programming language, and product comparison logic.


5 use cases of Machine Learning in the banking industry

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Machine Learning (ML) is currently the verge that has the biggest impact on the banking industry. Take a look at how 5 largest banks of the US are using ML in their workflows. The Federal Reserve of the US has recently published an official report on the largest banks in the US. It lists quite a ton of banks, yet we are not surprised by the fact 5 largest and most influential banks of the US are investing heavily into imbuing their services with Artificial Intelligence (AI) and ML. Just to illustrate the efficiency of this approach -- these banks have closed more than 400 of local branches in 2016 and still met their margin thresholds, as mobile banking combined with the ML helped them meet and exceed their customer's expectations.


Banking on artificial intelligence

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Artificial intelligence (AI) is the latest in a long line of technologies to play a part in the digital transformation of the financial services industry. The potential of this technology is vast: it can cut costs, provide human and systemic efficiencies, boost customer experience, promote loyalty and boost returns. According to business research firm Gartner, the two key components of AI – machine learning and deep learning – will be adopted as the norm within the next two to five years. There is real impetus and enthusiasm for organisations to adopt these technologies. Andy Pardoe, principal director of AI at Accenture Digital UK&I, says: "AI can be used across the entire value chain, from first contact with a potential new customer all the way to providing additional services to long-term customers. This is happening across the front, middle and back office functions."


Artificial Intelligence in Finance: AI is the New Electricity

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This article was written by Harry Chiang, a Financial Analyst at I Know First. "The big paradox here is that people think technology will lead to banking becoming more and more automated and less and less personalized, but what we've seen coming through here is the view that technology will actually help banking become a lot more personalized." Over the past few years, news articles have casually floated the term'Artificial Intelligence' around at an increasing rate. It's one of those buzzwords that somehow finds its way in to every tech-related conversation. Even the least tech-savvy person has a vague notion of what it is. The problem is, some of the more tech-savvy person don't have a much clearer notion of what it is either. The definition of AI ranges and has vague boundaries.