Mint. Mint was one of the first apps confirmed for the Apple Watch. The company describes it as a “companion to the Mint iPhone experience.”
CHEAPER AND FASTER TECHNOLOGY
What would it mean to you if your technology costs dropped? Over the past 30 years, the costs of computing have been falling steadily and, sometimes, exponentially. Some 30 years ago, a computer of decent processing power cost as much as US$20 million and was so big that it required its own highly air‐conditioned room. Today, a machine with comparable specifications can be picked up at a local Best Buy for about $200, and it is about the size of a high school yearbook. The decline in the costs of computer technology has been driven by several factors:
1. Broadly‐based demand for fast, superior computing by retail users, such as video gamers, has created a business case for a larger‐scale manufacturing of computers, reducing costs.
2. Investments in research and development by Silicon Valley consumer‐oriented companies, such as Google and Apple, have resulted in faster, leaner, and more affordable solutions.
3. Overseas investments by countries such as Singapore enabled foreign production of top‐quality components at a fraction of the cost, reducing overall ticket prices of machines.
Lower costs have permeated every aspect of computing from data storage to analytic power, allowing innovations such as cloud computing to flourish.
CLOUD COMPUTING
The term cloud refers to a collection of computers, each with its separate processing and storage engines, which are interconnected and operate with a single interface. The interface is a complex computer program with built‐in intelligence to automatically distribute the workload and the storage capacity among the participating machines. The cloud enables companies to reduce their data storage and processing costs by outsourcing at least some of their infrastructure and data storage.
A great example of a successful cloud deployment is Tradier.
According to Forbes, Tradier offers a brokerage‐account management system, a trading engine, and some market data. It then hands them off to application developers who can launch their own trading platforms, mobile apps, algorithmic trading systems, or other customized features for their customers, who are traders and investors who want to play the markets their own way. Account settings and market data are based in the cloud, so customers can log in to, and trade from, any of Tradier's developer partners.
As Dan Raju, the CEO of Charlotte, N.C.–based Tradier, explains it, “Tradier has decoupled the individual brokerage account from the front‐end investing experience.” Raju believes that his firm is offering a democratic platform that gives everyone access to the same cloud‐based engine that powers retail trading. He thinks of the developers as delivering “that most innovative last mile” to the trader, while the nuts and bolts of account management, tax reporting, funding, and so on are handled by Tradier.
BLOCKCHAIN
Blockchain, a technology underlying Bitcoin and gaining an increasingly wider acceptance in financial settlement, is an example of a cutting‐edge technology made possible by the cloud. The key idea underlying blockchain is an algorithm allowing users to simultaneously update the cloud database while maintaining the database's integrity, all in real time. Applied to financial trading, blockchain enables brokers and other institutions that handle their orders and money to reconcile their ledgers in real time. In other words, blockchain shortens the settlement procedures from T + 3 and T + 1 (still a standard in many financial instruments today) to real time. Shorter settlement times, in turn, allow for real‐time margin calculation and lower margin‐related risks. These developments, once adopted, will lead to even more real‐time trading.
This won't happen overnight. The complexities involved in moving all trading toward real time are nontrivial. Topics like margin, securities lending, and over‐the‐counter (OTC) trading introduce time‐consuming administrative procedures or custom trades that are not perfectly suited to the standardized type of blockchain discussed at this time.
Of course, the value of blockchain extends far beyond financial settlement. It is a tool that allows multiple parties to do business together ensuring reliability and at the same time without the threat of corrupting data. The financial businesses that are likely to be affected by blockchain technology require real‐time electronic negotiations, such as over‐the‐counter trading, loan origination, and any kind of workflow that was historically done slowly due to the high degree of error and the complexity of transactions. In short, before blockchain, many tasks had to be executed by one party at a time to prevent corrupting data. With blockchain, many parties can do tasks at the same time without worrying about possible overwrites, miscommunications, and so on.
FAST ANALYTICS
TransferWise and loan‐issuing apps did not emerge as a function of an ability to quickly send requests on the go. Beneath every successful money transfer and loan approval is a complex analysis that determines the risk of each operation.
At the core of all the super‐fast information sharing is data analytics. Take, for instance, any near‐instantaneous loan approval process. All loans are subject to credit risk – the risk that the loan is not repaid on time, if at all. Typically, the higher the probability that the loan is repaid in full and on schedule, the lower are the interest rates the lender needs to charge the borrower to make the transaction worthwhile. The reverse also holds: The higher is the probability that the borrower defaults, the higher are the rates the lender needs to charge to compensate for the risk of a default. The creditworthiness of the borrower can be forecasted using various factors, of which free cash flow and its relationship to the existing short‐term and long‐term debt, as well as other factors from Edward Altman's model, are critical. The ability to gather and process the required data points in real time are making the here‐and‐now loan approvals possible.
In general, risk, to many financial practitioners, has implied a multiday Monte Carlo simulation, something impossible to accomplish in a matter of hours, let alone seconds. Now, with new technologies, über‐fast processing of data is not only feasible, it is already in deployment in many applications.
How does data processing accelerate over time? Several applications running atop cloud architecture help dissect vast amounts of data faster than a blink of an eye. MapReduce was a first generation of fast software that allowed data mining extensive volumes of information and helped propel Google Analytics to its current lead. Still, newer, faster applications are here. Spark, an application that also runs on top of a cloud architecture, outperforms MapReduce and delivers lightning‐fast inferences through advanced management of computer resources, data allocation, and, ultimately, super‐fast computational algorithms rooted in the same technology that allows real‐time image and signal processing.
To understand why customers make decisions, companies harness the data available to them. In the past, customer segmentation studies were fixed in a point in time and used a variety of analytical approaches. Why go through this effort? By identifying types of customers who have similar tendencies to make similar decisions, a company can tailor their marketing, products, and investments. But that is the traditional approach.
With all forms of transactional and social data available and with enormously more computing power, companies can predict future behavior of clients almost at the same pace as clients are making their own decisions. For example, where will the aggressive high‐frequency traders trade in five minutes? New technologies, such as the one of several offered by AbleMarkets, can answer this question on the fly.
Traditional players need to review their technology spend and consider
