Scopes of Machine Learning and Artificial Intelligence in Banking & Financial Services ML & AI - The Future of Fintechs


Whether financial institutions are looking for improved customer service, risk management, fraud prevention, investment prediction or cybersecurity, the scopes of machine learning and artificial intelligence are limitless. In the modern era of the digital economy, technological advancements are no longer a luxury for the organizations, but a necessity to outsmart their competitors and business growth. With the technological advancements in the recent times, the impact of Machine Learning (ML) and Artificial Intelligence (AI) are very critical than ever before. Previously, we discussed the scopes of big data and data science in banking and financial services. In this article will explain in detail about ML and AI, and their scopes in banking and financial services.

Can RegTech Really Save Banks Billions Each Year?


The global investment banking industry is worth a few hundred billion dollars annually, as are both the audit and legal professions. And since the last decade or so, increased regulation has forced banks to devote around 10% of their salary costs to employing an army of compliance controllers to ensure that their transactions and processes meet the standards required by the law. And the stakes are high. Rogue traders, breaches of confidentiality, and reckless financial positions can expose financial institutions to fines, cripplingly negative publicity, and even prison sentences, not to mention huge financial losses. These stakes are what make banks the earliest adopters of many technological innovations.

AI Changing FinTech Modus Operandi


Financial payments and banking started in a very inefficient and traditional way, which was slow but still acceptable to the customers due to the stage in the information age. There are lucrative but under-utilised banking opportunities and banks in the region need to step up and grasp these opportunities to succeed otherwise its almost a lost game for them. AI technologies such as machine learning, deep learning, prescriptive learning, predictive analytics, virtual agents and natural language understanding technologies are gaining popularity among progressive banks. AI is not a magic push button and it will never be but as a strategy if one adopt it will take them to new height though, particularly for financial institutions, fintechs and banks where data access and security play a critical role. Competition in Fintech world today at its peak, so adoption of new technologies to stay one step ahead of the competition is no brainier.

Business Models of #WebRTC @CloudExpo @Twilio #IoT #RTC #AI #ML


WebRTC services have already permeated corporate communications in the form of videoconferencing solutions. However, WebRTC has the potential of going beyond and catalyzing a new class of services providing more than calls with capabilities such as mass-scale real-time media broadcasting, enriched and augmented video, person-to-machine and machine-to-machine communications. In his session at @ThingsExpo, Luis Lopez, CEO of Kurento, introduced the technologies required for implementing these ideas and some early experiments performed in the Kurento open source software community in areas such as entertainment, video surveillance, interactive media broadcasting, gaming or advertising. He concluded with a discussion of their potential business applications beyond plain call models. Speaker Bio Dr. Luis Lopez is associate professor at Universidad Rey Juan Carlos in Madrid, where he works in the creation of advanced multimedia communication technologies.

Influencer Interview: Spiros Margaris reflects on the past 12 months Fintech Recap 2017


A year after Spiros Margaris last spoke to bobsguide, we invited him back to see whether his predictions for 2017 had come true. We also talked about whether ageism is an issue in fintech and what exactly is needed to make a successful startup, as well as which ingredients make the best technologies. You've been named fintech's most influential voice in countless tables - does this confine you to a consultancy role? Would you consider revisiting your start-up past? I'm invested in several startups but I wouldn't want to pursue a more hands-on role.

Fintech trends: The rise of AI Fintech 2017 Recap


This article on 2017's AI comes very recently after Google's AutoML project created an AI child that was smarter than AI built by humans. The'child AI' called NASANet was created by two parent AIs and utilises'reinforcement learning' that enables it to report, learn and improve from its parent AIs. Whilst we'd scheduled an AI recap for 2017 it seems that this has been the most significant development in AI technology this year. We've put together the interesting world of AI as told by our articles over the months of 2017. We'll look at the many applications of AI, what AI is and where it's going.

#4 Fintech Trends to Watch Out for in 2018


You're reading Entrepreneur India, an international franchise of Entrepreneur Media. India has been soaring ahead in the technology space with products in financial technology taking the lead. With many government interventions and implementations like demonetisation and GST, 2017 has been a crucial year for the financial technology space. The introductions of policies and programs such as Jan Dhan Yojana and UPI or even the controversial linking of Aadhar with most financial institutions has clearly shown the government's interest in digitizing the sector. Reports even suggest that India's rising stance in the fintech space could see it soon surging ahead of China.

An investor's view of AI in 2018


Artificial Intelligence has become a buzzword for investors of late, many of whom recognize its enormous potential to become the most game-changing technology since the industrial revolution. Indeed, the projected impact of AI is likely to be greater than all prior tech trends combined, and savvy investors would be wise not to miss out. From an investor's point of view, you can divide the AI sector into a few major sub-sectors: infrastructure, algorithms, platforms, and applications. The infrastructure segment includes technologies and companies that provide the underpinnings enabling AI: machine learning, deep learning, natural language processing, and computer vision, including cloud infrastructure, specialized semiconductors, large-volume storage devices, low-latency databases, edge-based computing elements, and more. On the algorithm side, one would primarily count neural nets, classification, and clustering algorithms, good old Bayesian networks, and hidden Markov models.

How Much Can We Trust AI?

International Business Times

Artificial intelligence software gets more sophisticated almost every day. Computers can already beat human experts at chess, stock market predictions, and detecting cancer. Yet most machine learning experts believe we shouldn't hand over complete control to AI-powered robots any time soon. "We don't trust autonomous vehicles yet, despite the fact that they rarely make mistakes, because the cost of error is too high," New York University professor Vasant Dhar, who also founded one of the longest running AI-powered hedge funds, told International Business Times. "It takes a while to learn the machine learning program's style.

Is Crowdsourced Data Reliable?

International Business Times

Countless companies now leverage crowdsourcing as a key component of their business model even though crowdsourced data has several crucial weaknesses. It's hard to guarantee the diversity, reliability and expertise of information collected from strangers. For example, NBC reported more than 3,000 global news outlets inadvertently published tweets by Russian-backed trolls during the 2016 U.S. presidential elections. Crowdsourced data is often so dangerously flawed that this idea is now the premise of a CBS crime drama called "Wisdom of the Crowd." When it comes to the financial industry, crowdsourced mistakes can be costly as well as embarrassing.