7 Indian industries affected by AI: Dawn of an era of tectonic change

The minds of Indian software are all poised to fuel a new technological change in the world where software will make decisions for CEOs and management teams. Ray Kurzweil, American computer scientist, author and well-known futurist makes an incredible number of predictions about Artificial Intelligence (AI) since 1990, among other things, a computer would defeat a world champion chess in 1998, that people would be able to talk to their computers through orders, and more. More recently, he predicted that AI will reach human levels in a decade, and in 30 years it will have multiplied billions of information. Every company from Google to Facebook at Oracle to Microsoft SAP is working on projects and platforms where software can learn and make decisions without having drones of people running businesses. AI is not really a buzzword. Before knowing what impact that makes, we need to know the difference between machine learning, deep learning and AI. Machine learning is an approach where you machine-learn to interpret data, while deep learning is an approach where software learns from data models and their interpretations. AI is when the software makes decisions for itself. These are systems that will reshape society in a decade. Will this make companies efficient and responsible? says Amar Chokhawala, founder of Reflektion, who uses AI to convert visitors to a website into potential customers. AI will be government by human standards According to Gartner Inc., the hype and growing interest is pushing established software providers (like Infosys and Wipro) to introduce AI in their product strategy, creating considerable confusion in the process. Analysts predict that by 2020, AI technologies will be virtually ubiquitous in almost all new software products and services. Gartner predicts that by 2020, AI will be one of the top five investment priorities for over 30% of CIOs. “As AI accelerates the hype cycle, many suppliers are looking to The Gold Rush in recent years,” said Jim Hare, vice president of research at Gartner He says that most vendors, unfortunately, are focusing on the goal of simply building and marketing an AI-based product rather than identifying needs, potential uses, and commercial value for customers. ” This changes behaviors without being explicitly programmed, based on data collected, usage analysis and other observations. While there is a widespread fear that AI will replace the reality is that AI and current technologies can significantly increase human capabilities. some things better and faster than humans, once formed, the combination of machines and humans can accomplish more together than separately. Let’s look at the industries that will see rapid changes in the vertical and horizontal sectors: Banking and finance Banks will turn to their IT vendors to automate loan appraisal and risk. The AI ??platforms will be able to look at credit default customer models and suggest measures on increasing or decreasing interest rates. Pattern recognition and actionable information provided by the software can reduce gross NPAs. AI uses different data sources and recognizes loan repayment patterns. It can also use a lot of information to assess the credit worthiness of a company or an individual, ?? Ashwini Anand, founder of Monsoon CreditTech, who builds artificial intelligence to assess risk. Companies like NextAngles, based in New York, have at least 30 data scientists to create an AI platform that can detect market fraud. According to a report by IDC, IT spending in financial services will reach nearly $ 480 billion worldwide in 2016, with a compound annual growth rate (CAGR) of 4.2% in five years. This money is used by the financial services industry to map out all possible paths. Yet, in 95% of cases, money launderers outbid the system by opening multiple fictional accounts and companies around the world. Therefore, NextAngles builds an expert system for Resolve this nagging problem. The basic information coming from knowledge models is deep domain models of specific aspects of banking work. This is different from conventional data science based on large data. Mallinath Sengupta, co-founder of Next Angles, said that the fundamental aspect of these expert systems is a branch of science called “ontologyn”. Computer ontology examines the relationship of what is (money), the attributes that interact with it (bank), and the events that change the relationship (pre-operation washing). . According to PWC, banks require IT service providers to do the following. Update your computer’s performance model to prepare for the new AI and Blockchain. Reduce costs by simplifying legacy systems by deploying SaaS beyond the robot / AI cloud quickly. Develop the technological capability to be informed about your customer’s needs. Prepare architecture to connect with everything, everywhere. Pay enough attention to network security. Make sure you have access to talent and skills to run and win with new IT projects. If this is the case in the financial sector, the customer service industry is also developing. Today, customers are used to a robot voice when they contact a BPO. However, the problem here is that the customer is not determined by the number of years of service. Each transaction was isolated and a loyal customer was upset by the treatment he was given while receiving the answer to a question. AI can solve this problem compare transaction history in a few years. It can even retrieve data from social sources and send customers to the most appropriate robot dealer to solve the problem. Large companies are hired out of their support and they can not handle leaving. AI can solve it for them? Vasudev Bhandarkar, Managing Director of ScoreData, builds such a platform and works with 17 financial clients in India and around the world. He says AI can identify an important customer and screen through noise. Any AI platform can identify models from millions of accounts – in real time – and let the organization know why a particular customer is important. It examines their behavior based on the payment cycle and the type of claim that has been raised over the years. h2>

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