J.P. Morgan is one of the most advanced banks when it comes to data science and machine learning. It hired in Geoffrey Zweig from Microsoft in February 2017 as head of machine learning. It's actually launched a market-making product (LOXM) based on machine learning and it recently promoted Samik Chandarana, a former credit trader, to head its data science and analytics (effectively its machine learning) strategies. Chandarana hasn't actually started his new job yet – he's still a trader, but he'll be starting it soon and in an interview posted on J.P. Morgan's Youtube channel, he expresses various opinions about what it will entail. The bottom line, as Saeed Amen explained in a recent blog, is that machine learning and data science jobs in investment banks aren't necessarily as exciting as they seem.
Good scientists are not only able to uncover patterns in the things they study, but to use this information to predict the future. Meteorologists study atmospheric pressure and wind speed to predict the trajectories of future storms. A biologist may predict the growth of a tumor based on its current size and development. A financial analyst may try to predict the ups and downs of a stock based on things like market capitalization or cash flow. Perhaps even more interesting than the above phenomena is that of predicting the behavior of human beings.
Try Apache Spark 2.1 & Zeppelin in Hortonworks Data Cloud by Vinay Shukla Wanna try Spark 2.1 Now? Well, you are in luck… Hortonworks Data Cloud ("HDCloud") for AWS gives you a quick way to launch a Spark cluster in the cloud. Read More Machine Learning & its Impact on the Future for Insurance by Cindy Maike First and foremost, machine learning WILL change the way insurers do business. The insurance industry is founded on forecasting future events and estimating the value/impact of those events and has used established predictive modeling practices – especially in claims loss prediction and pricing – for some time now. Read More A Reference Architecture for the Open Banking Standard… by Vamsi Chemitiganti Financial services firms specifically deal with manifold data types ranging from Customer Account data, Transaction Data, Wire Data, Trade Data, Customer Relationship Management (CRM), General Ledger and other systems supporting core banking functions.
The customer experience in banking is about to get a lot more personal, as shown by an investment announced Wednesday in technology that will act as the "brain" behind AI financial services applications. Kasisto – a company that's behind a conversational AI platform about around a dozen financial institutions globally use – just completed a $17 million Series B funding round. It's yet another sign that customers' future interactions with their banks are more likely to be with non-humans. "It not only helps the bank become more informed of their end consumer, it serves the individual better, and in so doing reduces their cost structure and potentially generates additional revenue," said Patricia Kemp, co-founder and general partner of Oak HC/FT, the venture capital firm that led the funding round. "It's strategically important for banks to improve their mobile experiences and improve their online experiences."
The paper, which is the third edition released by the digital performance marketing agency, takes a look at how brands can make the most of machines in 2018, from facilitating seamless consumer experiences to delivering greater efficiencies. The agency interviewed 250 of their global clients, including FTSE 100 and Fortune 500 companies, and used the real-time feedback to outline key insights and priorities necessary for businesses to thrive in our fast moving, high expectation digital economy. Feedback showed that the transformative impact of Voice, AI, and Machine Learning is being felt across the entire business landscape with 55% of marketers surveyed agreeing that Machine Learning will allow them to make better decisions in 2018. Based on the feedback, the 2018 future focus whitepaper discussed the new machine rules including enhanced customer experience, how digital assistants are the new gatekeepers, how AI and machine learning transform marketing, how commerce is everywhere and the rise of Amazon as an'everything store'. "As we look towards an increasingly tech-enabled future, we believe success will be delivered by brands that can effectively leverage both advances in Machine Learning and the strengths of their human strategic capital," said Bowan Spanbroek, head of product and strategy in Asia Pacific at iProspect.
You could be forgiven for wondering why AI is so big all of a sudden. Hasn't humankind been dreaming about human-like robots for a long time? The first Star Wars film (with crowd-pleasing'droids' R2D2, C-3PO) was released in 1977; Terminator (starring Arnold Schwarzenegger as a cyborg assassin) was a massive success in the mid -1980s, a few years after Blade Runner (starring synthetic – or not? The idea of an intelligent machine is not exactly a new one, yet our ability to create something with Artificial Intelligence has increased dramatically in the last decade or so. There is now scope to use AI to make legal assessments, create games, predict purchases, navigate through traffic, translate words into different languages and diagnose diseases.
Between emojis, I write about the delicate dance between exponential technologies and our society. DeepMind's new AlphaZero can teach itself to become super-human in a variety of different games in a matter of hours. Analysis on Chess.com is particularly good, pointing to a different quality to AlphaZeros' tactical and positional play. This more efficient method of searching the probability space seems to be important. Many real-world applications involve far too many degrees of freedom to be'brute forced'.
Walmart has submitted a patent application for a drone delivery system that focuses on how packages will be received. Instead of just delivering goods to your doorstep, drones would drop packages into secure boxes (lockers) that communicate with the drone. The application describes a smorgasbord of technology that could be used to ensure secure drop-off, including geofencing and a blockchain for package tracking and identification. Just like most patent applications, Walmart's "Unmanned Aerial Delivery to Secure Location" is jam-packed with redundant language, ambiguous line drawings, buzz words, and ample legal jargon. Still, the basic premise is clear: a delivery system that includes a robotic vehicle that communicates with a secure locker.
Insurance is now ready for an AI-based analytics platform that can help minimize claim costs and improve customers' claims experience. Insurtech and artificial intelligence (AI) have become the new buzz words and mantra in the insurance industry. Creativity and innovation are thriving in Silicon Valley with more than 1,600 technology companies in the insurtech space for underwriting and claims. If you remember, back in the 1990s, experts predicted that if your company was not an internet company, you would not be around for long. That prediction came true, but what about the current prediction that artificial intelligence for claims will change the insurance industry?