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.
Even though Artificial Intelligence (AI) has been around as an academic and scientific discipline since the 1950s, the proponents of AI have never been as hopeful as they are in the present times. It's needless to mention that the current surge in AI research, investment, and real business applications is unprecedented. Market Intelligence firm IDC in their New IDC Spending Guide, September 19, 2018, predicted that the worldwide spending on cognitive and Artificial Intelligence systems would reach $77.6B by 2022. Similarly, Gartner projects the business value created by AI at $3.9T by 2022. While the philosophical debate on the ethical concerns around AI continues in several circles, we have seen myriad business applications of AI.
General purpose technology is a term economists reserve for technologies that spur protracted economic growth and societal advancements, revolutionizing the operations of households and corporations alike. A sample general purpose technology is electricity. Electricity spawned a multitude of products and sectors, including refrigerators, washing machines, trains and, of course, computers. The advent of electricity radically transformed the world. A recent Harvard Business Review article designates artificial intelligence (AI) as the most important general purpose technology of our era. A car that can parallel park itself. Devices that respond with tomorrow's weather when we ask.
Can machines think?" asked Alan Turing, known as the father of artificial intelligence (AI), in a seminal paper on the topic of computing machinery and intelligence in 1950. Turing did not coin the term'Artificial Intelligence' but his work laid the foundations for a new research area to be termed'Artificial Intelligence' by John McCarthy, one of the organizers of the 1956 conference held at Dartmouth College, UK to delve into the fundamental task of developing an electronic brain. However, by 1973, disappointed by the progress of work, funding dried up in the UK and USA and AI plunged into a long'winter'. In the 20th century, AI was an idea for the future. It needed much more computing power and a greater variety of digital data sources than was available at that time. Today, the picture has changed.
APIs, or application processing interfaces, are packages of code critical to AI functionality in products and software. They can add more value to AI capabilities with descriptions, and call outs. The future of AI is marked with a race against time, as man strives to make machines more intelligent than humans! What was a fascinating aspect of science fiction has now become the most powerful technology disruptive everyday processes in industries and businesses, and human touchpoints? With continuous breakthroughs in AI research, across domains and use cases, AI is being implemented by one company after another, at a breakneck speed. Thus, AI is based on several disciplines that contribute to intelligent systems – mathematics, biology, logic/philosophy, psychology, linguistic, computer science, and engineering. You need to have a certain level of expertise in math, probability, statistics, algebra, calculus, logic, and algorithms.