Sometimes they act based on gut instinct rather than sound logic. That's why Sentient Technologies (known as Sentient.AI) of San Francisco is working on Sentient Investment Management, an artificial-intelligence program that decides which stocks to buy and sell. "Our AI system can be more consistent and reliable," says IEEE Fellow Risto Miikkulainen, the company's vice president of research. "It buys and sells stocks based on an entire history of data rather than, for example, on a single unreliable piece of information that a stockbroker might fixate on." The company's researchers have been developing the program for the past 10 years to manage its investments.
Following on from talking around the big ten tech themes last Friday, I thought I would follow up with five FinTech themes for right now. This came out of a keynote speech I gave at Celero's 9th annual technology conference in Canada last week. It was fun with the CEO, Bob Reczka, kicking off the whole shebang talking about the five big themes in FinTech that had risen to the top of the heap over the last year. I agreed with these five themes and thought I'd share them here. Since 2016, there has been a significant move from banks fearing start-ups and start-ups wanting to disrupt banks, to start-ups and banks working together in harmony.
Regulatory technology (regtech) is often cited as the answer to the rising cost of compliance, risk and reporting duties at banks. Will it help financial institutions escape IT silos and enhance control over data? Neil Ainger, Daily News at Sibos' reporter, investigates. The principle of applying technology, such as regtech, to significant business challenges is "not new", says Paul Ellis, global head of regulatory product development at HSBC Securities Services. What is new is that regtech promises to marry the new landscape of post-financial crisis regulation to the new landscape of digital technologies.
The endless parade of emerging technology is gaining the attention of large enterprises and startups alike. The Internet of Things, artificial intelligence, machine learning, natural language processing, robotic process automation, and cognitive computing – all of these digital innovations and more are generating a range of disruptive innovation that is bridging gaps of unfulfilled customer demand. Does it make sense to spend limited resources to take advantage of these same technologies? According to the IDC Analyst Connection, "Analytics for SMBs: Sharpen Operations, Capitalize on Business Opportunities," such investments can bring a level of automation, electronic monitoring, and sensor-enabled insight not seen anywhere outside of the SMB segment. While the largest firms are busy refining processes in response to market dynamics, SMBs are close enough to customers and the competitive environment to effectuate change with tremendous speed and agility.
The biggest threat for our future, according to Elon Musk, is the development of artificial intelligence (AI). Musk addresses a couple of key issues. The first is how we should take care when it comes to the implementation of AI. The second, and perhaps the most important challenge, is how can you regulate and oversee this technology? In today's rush to adopt machine learning and AI techniques to gain a competitive advantage, there is a real danger that the technology will be deployed in an unsuitable field.
About 1,000 staff have been let go from Westpac over the past two years as a result of technological advancements such as automation; however CEO Brian Hartzer sees a silver lining, telling a House of Representatives committee his bank is also witnessing new jobs being created as a result of technology. "It's the case that customers are voting with their feet and with their plastic cards, and they're walking into branches less often to make cash deposits and cash withdrawals. So our branch network size and staffing reflects that," Hartzer told the Standing Committee on Economics on Wednesday. Hartzer conceded there was no question that technology is having an effect on jobs, but called the hype around the topic "overdone". "I personally think the impact of technology will be more about aspects of jobs than about whole jobs.
Artificial intelligence and Big Data are merely technical assistants to investment advisors, and will not completely replace advisors, David Leung, managing director of Standard Chartered Plc's [LSE:STAN] wealth department in China, told Yicai Global in an interview. The two technologies do help save time in facilitating our investment decisions and choosing where to put money, and they do make our work more accurate, Leung said. However, AI will never replace investment advisors. For example, in August last year, Standard Chartered released the first mobile trading platform among foreign-backed banks in China. The app offers direct advice to investors along with detailed explanations, but 90 percent of clients still choose to communicate directly with their own customer managers or advisors.
A new academic paper, Machine Learning for Trading, is the first conclusive study that shows success in having a machine learning-based trading strategy. The author, Gordon Ritter, Adjunct Professor in the Mathematics in Finance Program, New York University, constructed an artificial system which he knew would admit a profitable strategy, to see if a machine would find it. In order to train a machine-learning algorithm to behave as a rational risk-averse investor required appropriate reinforcement learning, specifically a mathematical technique called Q-learning (playing some sort of game where you are trying to maximise the reward function that may occur at several periods in the future). The machine learning agent found and exploited arbitrage opportunities in the presence of transaction costs in a simulated market proof of concept. Ritter explained: "I was really trying to answer the question, does machine learning have any application to trading at all, or no application; sort of a binary question.
Wall Street is looking towards Silicon Valley for a more automated environment and a tech-driven approach. A July 2016 report by CB Insights showed that 41 startups may be introducing AI to Fin-tech. Of the many big names, Goldman Sachs remained dominant in backing as many as four companies that use AI in financial technology. As many as 658 AI deals were closed and $5021 billion was spent on funding the AI startups in 2016. With total AI investment gaining momentum across different industries, an increasing number of companies are branching out to offer a variety of services that range from credit scoring to regulatory compliance and fraud detection.
Even if your organization is not ready to deploy solutions such as chatbots, virtual assistants, artificial intelligence (AI) or the Internet of Things (IoT), the importance of data and advanced analytics has never been greater. If you've been following the financial services scene lately, chances are high that you've seen plenty of talk about chatbots, virtual assistants, and other forms of artificial intelligence (AI). These advanced technologies promise to be a big part of banking going forward, and for good reason: They make the entire process simpler and more intuitive. Who doesn't want to skip phone trees and get straight to the answer they're seeking? That's what chatbots and virtual assistants offer.