If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
As enterprises look to deploy distributed ledgers, the industry's largest IT providers have launched blockchain-as-a-service (BaaS), offering a way to test the nascent technology without the cost or risk of deploying it in-house. The BaaS offerings could help companies who don't want to build out new infrastructure or try to find in-house developers, which are in hot demand. "The thing to be thinking about is that we're still in the early innings of this blockchain wave," said Bill Fearnley Jr., IDC's research director for Worldwide Blockchain Strategies. "There are very few people with multiple years of deep, hands-on experience." While heavily hyped, blockchain technology – which gained its initial notoriety from bitcoin cryptocurrency – has the potential to offer a new paradigm for the way information is shared; tech vendors and companies are rushing to figure out how they can use the distributed ledger technology to save time and admin costs.
The insurance industry – like many elements within Financial Services (FS) – has come under intense pressure over the past decade or so. The fintech revolution has meant that smaller and more agile startups are able to offer a variety of new services to consumers and businesses. These services are not only more interactive and based on the latest technologies, but they are also services that bigger insurance firms cannot easily offer. This increased competition from newer market entrants is a growing problem for more established insurance providers. A 2016 PwC survey revealed that 65 per cent of insurance chief executives see new market entrants as a threat to growth, while 69 per cent of insurance chiefs were concerned about the speed of technological change in their industry.
There are two big tech topics in banking these days, namely Artificial Intelligence (AI) and Blockchain. These two tech titans are changing the back office of banks into intelligent shared structures that will be mind-blowing a decade from now. In fact, they're not far off that today. I've written a lot about Blockchain, and feel that it's a slow-burn as it needs an awful lot of agreements before it will come to fruition. A bit like Brexit, Blockchain has too many counterparties involved in agreeing how it will happen, which is why it will not be until the next decade that we really start to see this technology transform clearing and settlement.
Our world is advancing at an extremely rapid rate. Technologies such as artificial intelligence, machine learning, drones, internet of things, augmented reality, and blockchain are growing in popularity every single day. Personally, I feel another industrial revolution is approaching quickly and the world we will in is going to drastically change. Blockchain is a difficult technology to understand but it has the potential to impact many organizations across the globe. If you're looking to get a head start on an innovative idea that will change our world then you're in the right place!
Burdened with outdated legacy systems and the rise of advanced fintech banking options, banking executives continue to seek out new ways to boost margins and embrace the technological disruption prevalent in their industry. While the promise of artificial intelligence signals new opportunities for robust growth, it's important for banks to first ensure they have all their data organized and ready to use for the prerequisite machine learning necessary to ramp up an AI system. While popular culture has been predicting the coming of advanced AI for decades, it's taken a bit longer for reality to catch up to the movie magic. But now AI is a major part of society and business, as consumers comfortably talk to their Amazon Echoes or easily discuss their daily schedules with Apple's Siri. "Some analysts dubbed 2017 as the Year of AI in banking."
In today's rapidly growing digital age, long-term survival and success are increasingly being linked with how "smart" a business can be. And for financial services firms that means using technology to become more intelligent, and processes and systems that talk to each other and learn from one another. Banks, insurers and wealth managers are pouring billions of dollars into artificial intelligence (AI), machine learning and other types of technologies that are already not only raising productivity levels, but reducing risk and actually creating new jobs. According to Accenture research, the financial services industry is now the third most impacted by productivity gains achieved with the implementation of AI, behind only the media and telecoms, and manufacturing sectors. The figures show that by 2035 its use should have resulted in an increase of US$1.2 trillion in gross value added, which measures the output value of all goods and services in a sector.
Artificial Intelligence is the future of growth. There is sure to be at least one article in the newspaper/internet/blogs daily on the revolutionary advancements made in the field of Artificial Intelligence or its subfield disrupting standard industries like Fintech, Banking, Law, or any other. In banking domain digital banking teams of all modern banks planning to transform the customer experience with their AI based chat-driven intelligent virtual assistant i.e. bots. AI promises benefits, but also poses urgent challenges (not threats, please make a note) that cut across almost all industries and business be it of any nature, i.e software development, technical support, customer care, medicines, law domain or factory / manufacturing work. The need of the hour is to upgrade our skill sets to exploit AI rather than compete with it.
One of the most intriguing concepts put forward by the blockchain community is the possibility to use Ethereum (an open software platform based on blockchain technology that enables developers to build and deploy decentralized applications) to build so-called Decentralized Autonomous Organizations (DAO). "A DAO is a fully autonomous, decentralized organization with no single leader. DAO's are run by algorithms, and governed by a set of smart contracts written on the Ethereum blockchain. The code is designed to replace the rules and structure of a traditional organisation, eliminating the need for people and centralized control. A DAO is owned by everyone who purchases tokens, but instead of each token equating to equity shares and ownership, tokens act as contributions that give people voting rights…" Imagine, for a moment, a DAO governing the transportation system of a whole city (including public transport and other privately run mobility services).
Whenever you spot a trend plotted against time, you would be looking at a time series. The de facto choice for studying financial market performance and weather forecasts, time series are one of the most pervasive analysis techniques because of its inextricable relation to time--we are always interested to foretell the future. One intuitive way to make forecasts would be to refer to recent time points. Today's stock prices would likely be more similar to yesterday's prices than those from five years ago. Hence, we would give more weight to recent than to older prices in predicting today's price.
In the years leading up to 2016, investors have had a love affair with fintech. In 2016, an astronomical $19 billion1 was invested in fintech companies, the highest to date. Investors had good reasons to love fintech, says Braden More, head of Partnerships and Industry Relations at Wells Fargo. These new companies brought disruptive technologies and much-needed innovation to the financial sector. They enabled mobile payments, robo-investing, and deployed machine learning to disrupt lending practices in the personal and small business loan market.