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How can Artificial Intelligence help FinTech companies? - Maruti Techlabs

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Financial firms were the early adopters of the mainframe computer, relational databases, and have eagerly awaited the next level of computational power. Inorganics Intelligence helps Fintech companies in solving human problems, by increasing efficiency. Artificial Intelligence (AI) improves results by applying methods derived from aspects of Human Intelligence at a beyond human scale. Technologies like Machine Learning, Artificial Intelligence (AI), Neural Networks, Big Data Analytics, evolutionary algorithms, and much more have allowed computers to crunch huge varied, diverse and deep datasets than ever before. In early ages of Banking, bankers used to have personal connections to their customers to help them assist well for their decisions.


How AI Makes Insurance Claims Processing And Fraud Detection Smarter

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AI technologies have well and truly reformed information systems by making them far more adaptive to humans while significantly improving the interaction between humans and computer systems. With this, AI in insurance industry has overhauled the claims management process by making it faster, better, and with fewer errors. From smart chatbots that offer quick customer service round the clock to the array of machine learning technologies that spruce up the functioning of any workplace through its automation power, the expanding potential of artificial intelligence in insurance is already being used in many ways. With increased awareness and resources about the game-changing influence of AI in the Insurance industry, the initial hesitations and shallow discomfort around its implementation are now fading quickly as it begins to trust in the calibre and numerous opportunities brought forward by Artificial Intelligence and Machine Learning. The only question that remains is – how far can we push its capabilities?


The Future of AI in the Insurance Industry

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With this, Artificial Intelligence within the Insurance industry has overhauled the claims management process by making it faster, better, and with fewer errors. From smart chatbots that offer quick customer service round the clock to the array of machine learning technologies that spruce up the functioning of any workplace through its automation power, the expanding potential of Artificial Intelligence in Insurance is already being used in many ways. With increased awareness and resources about the game-changing influence of AI in the Insurance industry, the initial hesitations and shallow discomfort around the its implementation are now fading quickly as it begins to trust in the caliber and numerous opportunities brought forward by Artificial Intelligence and Machine Learning. The only question that remains is – how far can we push its capabilities? In 2017, Artificial Intelligence has shown its substance in various business verticals by rapidly creating controlled, digitally enhanced automated environments for maximum productivity.


How AI is Revolutionizing the Process of Fintech Firms?

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Artificial Intelligence has revolutionized the finance industry. Not only does it improve the precision level in the industry, but it also enhances the customer engagement level and speed up the query resolution period. In this blog, we will be finding out answers about the importance of AI in financial sectors or FinTech firms. By the year 2030, traditional financial institutions can shave 22% in costs, as per the latest 84-page report of the Autonomous in an AI in the financial industry. Fintech companies and financial firms were the early adopters of relational databases, mainframe computers, and have eagerly awaited the next generation of computational and analysis power.


About the AI disruption on the Financial and Banking Sector

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Big global banks are increasingly turning toward Artificial Intelligence (AI) technologies to stay competitive in the digital era. AI has huge benefits, for both banks and their customers. The implications of AI disruption in the financial sector is that the analysis of users' habits, activities, behavioral characteristics, and financial data products can be customized to meet and anticipate each user's unique and evolving needs. This makes it viable for each user to have his/her own digital personal financial assistant.