When FinTech meets the Internet of Things
The new EU Privacy Regulation is in the process of being adopted and among the changes that are going to be introduced there will be a massive increase of the potential fines up to 4% of the global turnover of the breaching entity. Likewise, the development of technologies such as fintech that require the collection and the analysis of large amounts of data will require a so called “privacy impact assessment” to be submitted and validated by privacy authorities. The implementation of a privacy by design approach can be the sole defense in a regulatory framework where the burden of proof of having complied with regulations will be on the investigated entity i.e. the company exploiting the fintech platform. In addition to privacy issues, there are legal issues as to the ownership of data which need to be reviewed under a privacy, an intellectual property and a contractual confidentiality perspective.
Big Data: 6 Key Areas Every Product Manager Should Address
The two main considerations regarding storage are: how to store and where to store. How to store your data depends on your overall use case. The type of data you produce will determine the type of database you will require. If you have structured data, then a relational database such as SQL Server or MySQL are your best bet. On the other hand, if you have unstructured data such as images, videos, or tweets, then you probably need a schema-less database such as Hadoop or MongoDB. Or maybe, like some systems I’ve worked on, you need both. I’m not suggesting that Product Managers dictate the type of DB or the architecture of the data tier. That’s the role of your Architecture and IT team. However, it IS our job as Product Managers to define clear use cases and convey those to our technical team, so they can implement the right infrastructure for your product.
New fintech partnerships to push automation in trade finance
“We looked at a dozen OCR products, but none served the idea that we had, namely, to have an OCR that could put together all the different formats and make it work. That’s the reason we had to develop it ourselves.” As part of the collaboration, Traydstream have also become a member of PFU’s Imaging Alliance Programme, which gives access to tools, resources and code to build innovative scanner solutions. The data-sharing collaboration with Lloyd’s List Intelligence, meanwhile, will augment Traydstream’s compliance engine, the second part of its solution. This engine uses machine learning algorithms to quickly scrutinise the digital transaction data for a range of issues, such as blank fields, the inconsistency of names, industry-specific legislation, sanctions and country restrictions, to help banks and large corporations tackle anti-money laundering (AML) and compliance issues.
How blockchain is changing the way we invest
What blockchain has essentially done is diversify the means people can invest. People can now invest in cryptocurrencies, startups, and tokenized real-world assets all because of blockchain. The next challenge then is to encourage adoption of these investment vehicles. Cryptocurrencies are getting the most attention as the value of these coins continue to move up and more countries accept these as legal tenders. ICOs are also generating much interest as startups come up with interesting ideas on using blockchain to drive business. These new blockchain trading platforms, while still unproven, offer much promise. “My dream is to make NASDAQ on Blockchain with a wider range of tradable assets and a dramatic reduction of listing costs, settlement time, and transaction costs,” said LAToken CEO Valentin Preobrazhenskiy.
How Machine Learning Is Helping Morgan Stanley Better Understand Client Needs
So Morgan Stanley’s wealth management business unit has been working for several years on a “next best action” system that FAs could use to make their advice both more efficient and more effective. The first version of the system, which used rule-based approaches to suggesting investment options, is being replaced by a system that employs machine learning to match investment possibilities to client preferences. There are far too many investing options today for FAs to keep track of them all and present them to clients. And if something momentous happens in the marketplace — for example, the Brexit vote and the resulting decline in UK-based stocks — it’s impossible for FAs to reach out personally to all their clients in a short timeframe.
Rise of the chatbot: security concerns
As chatbots become increasingly intelligent, they are being equipped with additional capabilities, including the ability to process financial transactions. If you are comfortable using Facebook Messenger or WhatsApp to chat with your friends, then using it to make payments or check your account balance by just a matter of writing a message, versus having to open a new internet banking app, makes sense. This type of transaction through chatbots is already being seen in the US where Uber is integrated into Facebook Messenger, which added a payments solution for businesses in late 2016, meaning users can order and pay for their Uber through a simple message.
The core ethics of data analytics
It was not possible to be prescriptive about how data should be used as that risked limiting both the benefits to the consumer or citizen, and the benefits to the corporation or Government using the data, Mr Sherman said. Regulation would also struggle to keep pace with technology developments he said, and the looming impact of big data, artificial intelligence (AI) and machine learning on privacy was noted by a series of speakers at the conference. Simon Entwistle, deputy commissioner of the UK Information Commissioner’s Officer, noted that as machine learning and AI took hold it would be increasingly difficult to know how people’s data was being used, or whether deidentified data was being transformed into personal data. “Taking an ethical approach is more important than ever – to go beyond compliance to the underlying legal obligations,” he said.
McKinsey argues how the current wave of AI is ‘poised to finally break through’
Robotics and speech recognition are two of the most popular investment areas. Investors are most favoring machine learning startups due to quickness code-based start-ups have at scaling up to include new features fast. Software-based machine learning startups are preferred over their more cost-intensive machine-based robotics counterparts that often don’t have their software counterparts do. As a result of these factors and more, Corporate M&A is soaring in this area with the Compound Annual Growth Rate (CAGR) reaching approximately 80% from 20-13 to 2016. The following graphic illustrates the distribution of external investments by category from the study. These industries are known for their willingness to invest in new technologies to gain competitive and internal process efficiencies.
The Art of Crafting Architectural Diagrams
As Philippe Kruchten said, "architecture is a complex beast. Using a single blueprint to represent architecture results in an unintelligible semantic mess." To document modern systems we cannot end up with only one sort of diagram, but when creating architectural diagrams it is not always straightforward what diagrams to choose and how many of them to create. There are multiple factors to take into consideration before making a decision; for example, the nature and the complexity of the architecture, the skills and experience of the software architect, time available, amount of work needed to maintain them, and what makes sense or is useful for meeting stakeholders concerns. For example, a network engineer will probably want to see an explicit network model including hosts, communication ports and protocols;
Blockchain tokens may be the future of finance — if regulators allow it
Token offerings signal a sea change in the way that blockchain startups and software protocols are being financed. Beyond upsetting the balance of the investor ecosystem, though, this new funding model, and the projects taking advantage of it, may herald the dawn of something even more radical: a decentralized internet powered by applications that blur the line between owners and users. Inevitably, some observers are calling it an overheated market. Some are even comparing it to the dot-com bubble of the early 2000s. Most distressing to traditional investors, and even to early adopters of bitcoin, is the fact that some token projects have raised millions of dollars on the basis of little more than a white paper and a website. The Securities and Exchange Commission recently raised the barrier to entry for American entrepreneurs and investors, and risks putting the kibosh on such innovation altogether.
Quote for the day:
"Learning to ignore things is one of the great paths to inner peace." -- Robert J. Sawyer
