The software’s functional rules are based on assumptions that are limited to a linear number of observations. Reality often proves to be far more complex than expected, meaning automation is eventually suboptimal or the software ends up requiring expensive corrections. Machine learning on the other hand absorbs and develops itself using all available data, regardless of the volume. This means the risk of patterns or a use case being left out of the picture is therefore limited. Limitations show their head when machines avoid human intelligence and are restricted to imperfect selections. A good example is that of the automated processing of loan requests received by banks. An algorithm parses the archives of previous requests where each borrower’s key information is recorded along with reimbursement information. It therefore highlights the likely relationship between a borrower profile and a default risk.
If you’re hanging out in virtual reality, you’re going to need a body, and what this body must look like, or whether it even has to be human, depends on the context. Often, it seems cartoonish human figures are best for staying clear of the uncanny valley, since it’s still difficult to make avatars look just like us. ... Regardless of how well designed your avatar is in VR, one way these worlds resemble real life is that your perceived gender shapes the interactions you have. In Rec Room and other socially geared apps, like AltspaceVR and Facebook Spaces, I prefer to make my avatar female—and preferably similar in appearance to me, with brown hair and, when it’s an option, glasses. Being true to your actual identity can make you feel that your virtual self is authentic, but as a female character you’re likely to face behavior that is obnoxious or worse.
We hear the term “machine learning” a lot these days (usually in the context of predictive analysis and artificial intelligence), but machine learning has actually been a field of its own for several decades. Only recently have we been able to really take advantage of machine learning on a broad scale thanks to modern advancements in computing power. But how does machine learning actually work? The answer is simple: algorithms. Machine learning is a type of artificial intelligence (AI) where computers can essentially learn concepts on their own without being programmed. These are computer programmes that alter their “thinking” (or output) once exposed to new data. In order for machine learning to take place, algorithms are needed. Algorithms are put into the computer and give it rules to follow when dissecting data.
AI professionals are in high demand. To assemble -- and maintain -- an AI team, retention and recruitment are key. But that doesn’t necessarily mean having to look outside the organization. ... “In EY’s tax group, we provide extensive training on technical tax matters. However, we are also starting to add training on automation and AI. While recruiting a graduate with degrees in tax and AI is excellent, there is a significant talent shortage. That is one of the reasons we put resources in upskilling our people,” says Fiore. In the past year, EY has hired over 20 professionals focused on automation and AI. Recruiting AI talent in a hot hiring market often requires going directly to academic institutions. “Being active in the community – especially presenting at conferences and publishing papers – has supported our recruiting efforts. We have also presented at Columbia, MIT, and other leading organizations,” explains Thomson Reuters’ Al-Kofahi.
If finance can be the driving force behind digital transformation, how can it do so when the primary goal is to ensure budgets are stuck to like glue. It is this exact attitude that leads to CFOs adopting a conservative mentality in digital, effectively preserving the status quo. CFOs are by nature risk averse, so subverting this mentality becomes a challenge. ... Finance as a function can drive the implementation of internal processes to upscale efficiency. Using their organisational view of resource and budget allocations, they may then pull this process change back to customer facing systems to create customer intimacy. From here, they may move I.T. strategy to focus on a new product and bring the budget in line for the next year.
APIs enable developers to integrate the features of one application into the code of another. This means that developers can use the existing work of other programmers as they build out their products, drastically increasing speed to market. ... With account authentication now quickly out of the way, the developers can focus their attention on the product itself. Fintech APIs can be viewed as the building blocks out of which new fintech products can be built. As more and more fintech APIs are developed and leveraged for new products, the speed of fintech innovation is likely to increase, which has significant implications for the wider finance industry. In fact, an increase in innovative fintech products is a massive opportunity for traditional financial institutions such as banks, and these institutions can play an active role in fintech innovation with the deployment of their own internal APIs.
The key to success in the digital advice market lies with customer service, according to panelists at both New York meetings this week. “It starts with the client,” said Mike Sha, co-founder and CEO of SigFig. “There’s been a lot of focus on driving alpha, beating the markets [but] fee efficiency is better at driving long-term returns and improving client outcomes. We control not the investment returns through alpha, but how we serve clients.” And clients will expect to set their own preferences for digital advice like they do for the music they listen to on digital platforms like Spotify, said Steve Scruton, president of Broadridge Advisor Solutions. “People are conditioned to have what they want.” Data collection and predictive analysis will help digital advisors learn what clients needs and desire, said Scruton.
While consumers continue to use traditional payment methods such as direct mail, pay-by-phone and in-person payments, online and mobile payments (either through the financial organization or through the biller) now make up 59% of payments, according to the Fiserv research. Not only have the majority of consumers switched to digital channels, they are happy with their decision. For online bill pay users, 79% rated the service 8 of 10 or higher, with 70% of mobile bill pay users having the same sentiment. The reason for the satisfaction is clear. Both banking bill pay services and biller direct services provide speed and convenience. Major points of differentiation between the services are evident though, with biller direct services getting higher marks for speed and financial institution options being preferred due to the ability to pay multiple organizations in one sitting.
Speaking to The Adviser, Brett Spencer, the former CEO of the Stargate Group and executive director of TICH Consulting Group, said that he thinks anyone who believes the broking industry is being replaced by technology is talking “absolute rubbish”. Mr Spencer said that the fact an abundance of “fintech” solutions are coming to the market is exactly the main driver behind brokers remaining relevant and increasingly relied upon by consumers. He explained: “The reason brokers are here and will continue to be here, and market share will grow… is that the sheer proliferation of the number of mortgage products in the market today is in the thousands. “You talk to any one lender and they might say they have three products, but there are probably 30 variations on those products. Joe Consumer just doesn’t understand it.
When it comes to technology falling below its potential, what better industry to learn from than the world of conference calling and remote meetings? Despite being a mature industry that has witnessed new technologies emerge and evolve, the clear majority of conference calls are still audio-only, with employees choosing to ‘dial in’ using numbers and codes just as they did decades ago. While more capable software products have been available for many years now, they continue to be shunned by most users. ... Winning SaaS products are those which recognise and deliver upon distinct needs. For example, Salesforce is a great CRM tool for sales teams, but there’s likely a better one for investment professionals. Jira is a great workflow tool for product and engineering teams, but there’s likely a better one for marketing teams.
Quote for the day:
"Leadership is a potent combination of strategy and character. But if you must be without one, be without the strategy." -- Norman Schwarzkopf