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How Data Analytics Backed By AI & ML Is Transforming The BFSI Sector

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Stephen Hawking, the renowned theoretical physicist once said, "It's tempting to dismiss the notion of highly intelligent machines as mere science fiction." The reality is that most AI applications do not have a physical form, but rather "live" in lines of code. The term "AI" includes all technology used to mimic human intelligence, typically falling into one of three subcategories: machine learning, natural language processing and cognitive computing. The business world is getting transformed and changing rapidly with the digital disruption backed with insight and automation opportunities gained out of Artificial Intelligence and Machine Learning (AI/ ML) enabling Data Analytics. "When Amazon recommends a book you would like, Google predicts that you should leave now to get to your meeting on time, your bank stopping a fraudulent transaction on your credit card, and UBER magically get car of your choice at your doorsteps, these are examples of machine learning over a Big Data stream."


Five ways AI is disrupting financial services

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Whether you realise it or not, artificial intelligence (AI) is taking over the world. No need to brace for a Hollywood scripted battle like in movies such as "I, Robot" and "The Terminator". The reality is that most AI applications do not have a physical form, but rather "live" in lines of code. The term "AI" includes all technology used to mimic human intelligence, typically falling into one of three subcategories: machine learning, natural language process and cognitive computing. Currently, there are more than 2,000 AI start-ups in 70 countries that have raised more than $27 billion, according to Venture Scanner, a tech-centric analytics firm.


10 Applications of Machine Learning in Finance

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Machine learning in finance has become more prominent recently due to the availability of vast amounts of data and more affordable computing power. Machine learning in finance is reshaping the financial services industry like never before. Leading banks and financial services companies are deploying AI technology, including machine learning (ML), to streamline their processes, optimise portfolios, decrease risk and underwrite loans amongst other things. Here in this article, we will explore some important ways machine learning is transforming the financial services sector and examples of real applications of machine learning in finance. To answer this question and understand the role of machine learning in finance, we must first understand why machine learning is suitable for finance. Machine learning is about digesting large amounts of data and learning from that data in how to carry out a specific task, such as distinguishing fraudulent legal documents from authentic documents. Machine learning in finance is the utilization a variety of techniques to intelligently handle large and complex volumes of information. ML excels at handling large and complex volumes of data, something the finance industry has in excess of. Due to the high volume of historical financial data generated in the industry, ML has found many useful applications in finance. The technology has come to play an integral role in many phases of the financial ecosystem, from approving loans and carrying out credit scores, to managing assets and assessing risk.


A Primer on AI in Financial Services – Jeff Fraser – Medium

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At a high level, Artificial Intelligence (AI) is a branch of computer science that makes machines imitate intelligent human behavior, simulating (and often exceeding) human performance. AI has finally emerged as the future, after unfulfilled hype that goes back to the 1950s, due to developments such as the availability of an immense amount of data, the open-sourcing of ML algorithm development, and advances in high-density parallel processing infrastructure. In fact, IBM now believes the technology solutions market for AI amounts to a staggering $2 trillion over the next decade. Data is the new oil, and 90% of data in the world right now has been created in the last 2 years alone. The power of data has actually lagged the technical capability to monetize it efficiently and effectively, in a world where the use of data is moving from a competitive advantage to a requirement to compete. As such, enterprises are now shifting development focus from software engineering to data engineering, and a wave of AI M&A has hardly begun as companies in every sector will ultimately need an AI solution.


Artificial intelligence in Banking advantages, disadvantages & Mobile banking services Science online

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AI can be used in banks to decrease financial risk, It can improve loan underwriting through machine learning, improve financial crime risk with advanced fraud detection, It can improve compliance and controls, and reduce operational risk through improved accuracy in transcription & production of documents, banks can use machine learning and big data to prevent criminal activities and monitor potential threats to customers in commerce. Artificial intelligence (AI) includes machine learning and natural language, it can be used in the banking industry, Machine learning is a method of data analysis which automates analytical model building, Machine learning occurs when computers change their parameters/algorithms on exposure to new data without humans having to reprogram them. Natural language processing (NLP) refers to the ability of technology to use human communication, naturally spoken or written, as an input that prompts computer activity, natural language generation (NLG) refers to the ability for technology to produce human quality prose, It sorts through large amounts of available data to produce a human-sounding response, NLG can take the form of speech, or of a multipage report summarizing financial results. AI can help the bank understand the expenditure pattern of the customer, The bank can come up with a customized investment plan & assist the customers for budgeting, banks can send the notification about the advice for keeping a check on the expenses and investments based on the data, The transactional & other data sources can be tracked to help understand the customer's behavior and preferences to improve their experience. Artificial intelligent can sift through massive amounts of data and identify patterns that might elude human observers, One area where this capacity is particularly relevant is in fraud prevention, Artificial intelligence and machine learning solutions are deployed by many financial service providers to detect fraud in real time.