Facebook's foray into crypto has made the firm few friends in political circles. The Libra backlash has been so bad that it has painted bitcoin and the rest of the crypto industry in a bad light. This week US senators have ramped up their rhetoric against internet monopoly and its lofty banking ambitions. A high ranking member of the Senate Banking Committee has lashed out at Libra again this week when he compared it to the subprime mortgage crisis that caused the 2008 global recession. According to Yahoo Finance, Senator Sherrod Brown pulled no punches in his opening remarks during the assembly.
The big story in mortgages today is the rise in mortgage loan rates. For the first time in years, we're seeing 30-year fixed mortgage rates consistently above 4%, and a 5% rate is in sight. Higher rates make sense if you look at it one way: the economy is strong, inflation is climbing, and it's safe to expect Federal Reserve hikes in 2018 and 2019. Industry veterans might be sighing with relief.
Without a doubt, 2016 was the year'disruption' became tangible. Events like Brexit, the U.S. election and India's demonetization exercise brought home the reality we are living in a fast-changing global society where a sense of anti-establishment and rebellion is accelerating change. This shows no sign of stopping in 2017, with new technologies allowing banks to offer service levels more synonymous with hospitality than financial services, and with established technologies like artificial intelligence and robotic process automation seeing a resurgence in combination with new voice commerce models, IoT data, and robo advisors to offer more personal, more contextual and ultimately unique banking experiences for each and every one of us. In meeting with decision-making executives from the U.S to Europe, the Middle East, India and Singapore, I have compiled a clear list of trends that are dominating technology investment discussions across the globe's leading banks. In 2016 we already saw several leaders' like DBS, Santander, Wells Fargo and Bank of America roll out their chatbots, but 2017 is the year when the rebirth of this very old technology will come into its own.
The most recent financial upheavals have cast doubt on the adequacy of some of the conventional quantitative risk management strategies, such as VaR (Value at Risk), in many common situations. Consequently, there has been an increasing need for verisimilar financial stress testings, namely simulating and analyzing financial portfolios in extreme, albeit rare scenarios. Unlike conventional risk management which exploits statistical correlations among financial instruments, here we focus our analysis on the notion of probabilistic causation, which is embodied by Suppes-Bayes Causal Networks (SBCNs); SBCNs are probabilistic graphical models that have many attractive features in terms of more accurate causal analysis for generating financial stress scenarios. In this paper, we present a novel approach for conducting stress testing of financial portfolios based on SBCNs in combination with classical machine learning classification tools. The resulting method is shown to be capable of correctly discovering the causal relationships among financial factors that affect the portfolios and thus, simulating stress testing scenarios with a higher accuracy and lower computational complexity than conventional Monte Carlo Simulations.
China's 19-year-old Go player Ke Jie reacts during the second match against Google's artificial intelligence programme AlphaGo in Wuzhen, eastern China's Zhejiang province on May 25, 2017. Artificial intelligence is firmly embedded throughout the economy. Financial services firms use it to provide investment advice to customers, automakers are using it in vehicle autopilot systems, technology companies are using it to create virtual assistants like Alexa and Siri, and retailers are using artificial intelligence (AI) together with customers' prior sales histories, to predict potential purchases in the future, to name but a few examples. The potential of AI to boost economic growth has been discussed in numerous forums, including by Accenture, the Council on Foreign Relations, the McKinsey Global Institute, the World Economic Forum, and President Obama's Council of Economic Advisers, among others. The most dramatic advances in AI are coming from a data-intensive technique known as machine learning.